Master of Science in Artificial Intelligence | Accredited EQF7 Degree at Woolf

Overview

What you'll learn

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About

About this degree

The Master of Science in Artificial Intelligence is designed to provide students with advanced knowledge and practical skills in the rapidly evolving field of AI. This program offers a comprehensive curriculum that covers core AI concepts, including machine learning, neural networks, natural language processing, and robotics. Students will gain a deep understanding of both theoretical and applied aspects of AI, preparing them to solve complex problems and innovate in various industries. The program emphasises hands-on experience through projects, case studies, and real-world applications, enabling students to apply AI techniques to create intelligent systems and drive decision-making processes. The program is tailored for professionals and graduates who aspire to lead in the AI domain, whether in research, development, or management roles. With a focus on flexibility and accessibility, this degree allows students to balance their studies with professional and personal commitments. Graduates will be equipped to take on advanced roles in AI, such as data scientists, AI engineers, and AI project managers, and will be well-prepared to contribute to the development and deployment of AI technologies across a wide range of sectors, including healthcare, finance, and technology.

Learning Outcomes

What you'll learn

• Design and develop AI models using state-of-the-art tools and techniques, applying machine learning principles to solve complex problems.
• Apply AI techniques to industry-specific applications, utilising data science and computational intelligence for real-world decision-making.
• Optimise AI models and algorithms through iterative testing and refinement, improving efficiency and effectiveness in various applications.
• Execute predictive modelling using advanced data analytics and machine learning approaches, with a focus on accurate predictions and insights.
• Lead AI-focused projects, managing resources, timelines, and stakeholders to deliver AI-driven solutions that align with business goals.

Course Structure

Tiers

1 - Core Modules

875 hours | 35 ECTS

Introduction to Computer Programming: Part 1

125 hours | 5 ECTS

About

This course helps students translate advanced mathematical/statistical/scientific concepts into code. This is a module for writing code to solve real-world problems. It introduces programming concepts (such as control structures, recursion, classes and objects) assuming no prior programming knowledge, to make this course accessible to advanced professionals from scientific fields like Biology, Physics, Medicine, Chemistry, Civil & Mechanical Engineering etc.  After building a strong foundation for converting scientific knowledge into programming concepts, the course advances to dive deeply into Object-Oriented Programming and its methodologies. It also covers when and how to use inbuilt-data structures like 1-Dimensional and 2-Dimensional Arrays before introducing the concepts of computational complexity to help students write optimised code using appropriate data structures and algorithmic design methods.

Teachers

SURVESH SATISH CHAUHAN

Ken Adachi

Elie Varesse Wanko Pohdie

Thiago Meireles Grabe

Oscar Mauricio Forero Carrillo

Abdul-Razak Bawa Yussif

Shubham Saxena

Robinson Jose Vasquez Ferrer

NEO CE ZHENG (LIANG CEZHENG)

Prasun Srivastava

Deepak Sharma

Shinto Theruvil Manuel

Amit Lathiya

Jihwan Min

Patel Aksharkumar Babulal

Ehab Hamdy Mohamed Mohamed Hussein

Satyajeet Sindhiyani

Rafael Castillo Alcibar

Intended learning outcomes

Knowledge

• Develop a critical understanding of a modern programming language such as Java or Python.
• Acquire knowledge of various methods for structuring data.
• Critically assess the relevance of theories for business applications in the domain of technology..
• Develop a specialised knowledge of key strategies related to Object-Oriented Programming.
• Critically evaluate diverse scholarly views on computational complexity.

Skills

• Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing.
• Apply an in-depth domain-specific knowledge and understanding to computer programming.
• Autonomously gather material and organise it into a coherent presentation or essay.
• Creatively apply various programming methods to develop critical and original solutions to computational problems.

Competencies

• Solve problems and be prepared to take leadership decisions related to the methods and principles of computer programming.
• Act autonomously in identifying research problems and solutions related to Object-Oriented programming.
• Create synthetic contextualised discussions of key issues related to converting scientific knowledge into programming concepts, and how to instantiate these using Object-Oriented methods.
• Demonstrate self-direction in research and originality in solutions developed for modern programming languages.
• Apply a professional and scholarly approach to research problems pertaining to computational complexity.
• Efficiently manage interdisciplinary issues that arise in connection to data structured in 1- and 2-dimensional arrays.

Introduction to Artificial Intelligence

125 hours | 5 ECTS

About

This course is designed to provide students with a comprehensive overview of the key concepts, techniques, and applications of AI. This course covers the history and evolution of AI, fundamental theories, and essential algorithms, including search methods, knowledge representation, machine learning, and neural networks. Students will explore the practical applications of AI in various domains such as robotics, natural language processing, computer vision, and expert systems, gaining an understanding of how AI technologies are transforming industries and society.

Through a mix of theoretical lectures and hands-on exercises, students will develop a solid grounding in AI principles and practices. They will engage in projects and case studies that illustrate real-world AI applications, enhancing their problem-solving and critical-thinking skills. By the end of the course, students will have a thorough understanding of AI fundamentals and be prepared to delve deeper into specialised AI topics, positioning themselves for success in advanced courses and professional roles within the field of artificial intelligence.

Teachers

Ken Adachi

Dheeraj Agrawal

Abdelrhman Motawea

Alberto Ivo da Costa Vieira

Thiago Meireles Grabe

Abdelrhman Hazem Wahdan

Ramkumar Manoharan

Takondwa Fanuel Sanyude

Shubham Saxena

Robinson Jose Vasquez Ferrer

SEPTI RITO TOMBE

NEO CE ZHENG (LIANG CEZHENG)

Samrat Banerjee

Ahmed Gamal Ali

Mariam hussein wagdy Elmasry

Nasim Banu

Abdulhakeem Omotolani Yaqoob

Nitish Kumar

Osama Mohammed Moustafa Hosameldeen

Shinto Theruvil Manuel

RAGHAVENDRA PRASAD SAVADA

Amit Lathiya

Shashank Shekhar

Abhishek Mann

Min Khaung Min Lwin

Lamia Hassan Mohamed Zainelabedeen Taha

Rafael Castillo Alcibar

Intended learning outcomes

Knowledge

• Compare and contrast narrow AI, general AI, and superintelligent AI, and evaluate their use cases in various industries.

• Explain the key milestones and advancements in the field of AI, from its inception to modern-day applications.

• Identify the foundational concepts of artificial intelligence including machine learning, neural networks, and natural language processing.

Skills

• Assess the accuracy, precision, recall and evaluate the performance of AI models using standard metrics.
• Utilise AI tools and frameworks for practical AI development.
• Implement and run AI algorithms, such as decision trees and k-nearest neighbours, on datasets to solve classification and regression tasks.

Competencies

• Evaluate the societal and ethical challenges posed by AI, such as bias, privacy concerns, and job displacement, and propose strategies to mitigate these issues.
• Create simple AI systems or prototypes that address specific real-world challenges, demonstrating an understanding of AI principles.
• Work effectively in groups to design, develop, and present AI solutions, showcasing strong teamwork and communication skills.

Introduction to Machine Learning

125 hours | 5 ECTS

About

This course focuses on building basic classification and regression models and understanding these models rigorously both with a mathematical and an applicative focus. The module starts with a basic introduction to high dimensional geometry of points, distance-metrics, hyperplanes and hyperspheres. We build on top this to introduce the mathematical formulation of logistic regression to find a separating hyperplane. Students learn to solve the optimization problem using vector calculus and gradient descent (GD) based algorithms. The module introduces computational variations of GD like mini-batch and stochastic gradient descent. Students also learn other popular classification and regression methods like k-Nearest Neighbours, NaiveI Bayes, Decision Trees, Linear Regression etc. Students also learn how each of these techniques under various real world situations like the presence of outliers, imbalanced data, multi class classification etc. Students learn bias and variance trade-off and various techniques to avoid overfitting and underfitting. Students also study these algorithms from a Bayesian viewpoint along with geometric intuition. This module is hands-on and students apply all these classical techniques to real world problems.

Teachers

SURVESH SATISH CHAUHAN

Venkataramarao Gottipati

Hazem Antar Taha Aly Taha

Rafael Martins Buck de Godoy

Ahmed Sabry Muhammad Mahmoud Mazroua

Alberto Ivo da Costa Vieira

Thiago Meireles Grabe

Takondwa Fanuel Sanyude

Rajat Sharma

Islam Tarek Abdelrahman Ali Hassanein

Jean Paul Barddal

Prasun Srivastava

Shinto Theruvil Manuel

Amit Lathiya

Jihwan Min

Raymond Phan

Abhishek Mann

Ehab Hamdy Mohamed Mohamed Hussein

Rafael Castillo Alcibar

Intended learning outcomes

Knowledge

• Critically evaluate diverse scholarly views on machine learning.
• Critically assess the relevance of theories for business applications in the domain of technology.
• Develop a critical knowledge of machine learning.
• Acquire knowledge of bias and variance trade-off, and various techniques to avoid overfitting and underfitting.
• Develop a specialised knowledge of key strategies related to machine learning.

Skills

• Apply an in-depth domain-specific knowledge and understanding to machine learning solutions.
• Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing.
• Creatively apply regression models to develop critical and original solutions for computational issues.
• Autonomously gather material and organise it into coherent problem sets and presentation.

Competencies

• Create synthetic contextualised discussions of key issues related to machine learning.
• Efficiently manage interdisciplinary issues that arise in connection to machine learning.
• Demonstrate self-direction in research and originality in solutions developed for machine learning.
• Solve problems and be prepared to take leadership decisions related to the methods and principles of machine learning.
• Act autonomously in identifying research problems and solutions related to machine learning.
• Apply a professional and scholarly approach to research problems pertaining to machine learning.

Ethical Artificial Intelligence Practices

125 hours | 5 ECTS

About

This course is dedicated to exploring the ethical, legal, and social implications of artificial intelligence technologies. This course examines key issues such as bias in AI algorithms, data privacy, transparency, accountability, and the impact of AI on employment and society. Students will engage with case studies and frameworks designed to address these challenges, learning how to develop and implement AI systems that align with ethical standards and promote fairness and inclusivity.

Through a combination of theoretical discussions and practical applications, the

course equips students with the knowledge and tools necessary to navigate the complex landscape of AI ethics. Students will participate in discussions on policy,

regulations, and best practices, and will work on projects that involve designing ethical AI solutions and conducting impact assessments. By the end of the course, students will be prepared to advocate for and implement ethical AI practices in their professional roles, ensuring that AI technologies are developed and used responsibly and equitably.

Teachers

Thiago Meireles Grabe

Ramkumar Manoharan

Mariam hussein wagdy Elmasry

Abdulhakeem Omotolani Yaqoob

Shashank Shekhar

Joy Hilda Mukami Kareko

Intended learning outcomes

Knowledge

• Recognize and describe common ethical challenges and dilemmas encountered in AI development, including bias, discrimination, and data privacy issues.
• Critically analyse real-world case studies of ethical failures and successes in AI, drawing lessons for future practice.
• Define and explain key ethical principles in AI, such as fairness, transparency, accountability, and privacy.

Skills

• Perform ethical risk assessments for AI projects, identifying potential harms and proposing measures to minimise them.

• Design and implement strategies to mitigate bias in AI models, using techniques such as re-sampling, fairness-aware algorithms, and interpretability tools.

• Assess AI systems for ethical compliance using established frameworks and guidelines, ensuring they align with societal values and legal requirements.

Competencies

• Demonstrate the ability to design AI solutions that prioritise ethical considerations, balancing innovation with responsibility to ensure positive societal impact.
• Lead and guide multidisciplinary teams in developing and implementing AI systems that adhere to ethical standards, fostering a culture of ethical AI within their organisations.
• Demonstrate the competency to advocate for ethical AI practices in industry and policy discussions, effectively communicating the importance of ethics in AI to diverse stakeholders.

Intelligent Systems

125 hours | 5 ECTS

About

This course is aimed at providing students with a comprehensive understanding of how to design and implement systems that exhibit intelligent behaviour. This course explores a range of topics including expert systems, autonomous agents, knowledge representation, and reasoning. Students will delve into the principles of how these systems can mimic human decision-making processes, adapt to changing environments, and perform complex tasks autonomously.

The course integrates theoretical concepts with practical applications through hands-on projects and case studies, allowing students to develop and deploy intelligent systems in real-world scenarios. By working with advanced tools and techniques, students will learn to build systems that can handle uncertainty, learn from experience, and interact effectively with users and other systems. Upon completion, students will be equipped with the skills to create sophisticated AI solutions and contribute to the development of cutting-edge intelligent technologies in their professional careers.

Teachers

Ken Adachi

Elie Varesse Wanko Pohdie

Dheeraj Agrawal

Abdelrhman Motawea

Thiago Meireles Grabe

Rajat Sharma

Abdul-Razak Bawa Yussif

Sharad Jain

Shubham Saxena

NEO CE ZHENG (LIANG CEZHENG)

Deepak Sharma

Abdulhakeem Omotolani Yaqoob

Osama Mohammed Moustafa Hosameldeen

Shubham Chhabra

Amit Lathiya

RAHUL KUMAR

Min Khaung Min Lwin

Sebastian Velasquez

Katherine Walker

Rafael Castillo Alcibar

Intended learning outcomes

Knowledge

• Analyse the architecture and functionality of different intelligent systems such as rule-based systems, neural networks, and expert systems, considering their strengths and limitations.

• Understand the principles of knowledge representation and reasoning in intelligent systems to simulate human-like decision-making.

• Identify core components of intelligent systems such as sensors, actuators, decision-making algorithms, and knowledge representation.

Skills

• Design and implement intelligent agents that can autonomously perform tasks such as navigation, data analysis, or automated decision-making.

• Integrate machine learning models into intelligent systems to improve their adaptability and accuracy in complex environments.

• Evaluate the performance of intelligent systems using real-world scenarios.

Competencies

• Design intelligent systems with adaptive learning capabilities demonstrating proficiency in adaptive algorithms and real-time learning.

• Evaluate the broader implications of deploying intelligent systems, considering issues such as automation, privacy, and the potential for bias, and will propose guidelines to ensure ethical use.

• Collaborate on the development of multi-agent systems for complex problem-solving and integrate different intelligent agents for a common goal.

Introduction to Deep Learning

125 hours | 5 ECTS

About

This course provides a strong mathematical and applicative introduction to Deep Learning. The course starts with the perceptron model as an over simplified approximation to a biological neuron. We motivate the need for a network of neurons and how they can be connected to form a Multi Layered Perceptron (MLPs). This is followed by a rigorous understanding of back-propagation algorithms and its limitations from the 1980s. Students study how modern deep learning took off with improved computational tools and data sets. We teach more modern activation units (like ReLU and SeLU) and how they overcome problems with the more classical Sigmoid and Tanh units. Students learn weight initialization methods, regularisation by dropouts, batch normalisation etc., to ensure that deep MLPs can be successfully trained.

The course teaches variants of Gradient Descent that have been specifically designed to work well for deep learning systems like ADAM, AdaGrad, RMSProp etc. Students also learn AutoEncoders, VAEs and Word2Vec as unsupervised, encoding deep-learning architectures. We apply all of the foundational theory learned to various real world problems using TensorFlow 2 and Keras. Students also understand how TensorFlow 2 works internally with specific focus on computational graph processing.

Teachers

SURVESH SATISH CHAUHAN

Aseem Arora

Ken Adachi

Rafael Martins Buck de Godoy

Abdelrhman Motawea

Alberto Ivo da Costa Vieira

Ramkumar Manoharan

Islam Tarek Abdelrahman Ali Hassanein

Jean Paul Barddal

Prasun Srivastava

Ahmed Gamal Ali

Osama Mohammed Moustafa Hosameldeen

Shinto Theruvil Manuel

Amit Lathiya

Jihwan Min

Abdelmonem mohamed abdelmonem rehab

Abhishek Mann

Sudhakar Goyal

Lamia Hassan Mohamed Zainelabedeen Taha

Rafael Castillo Alcibar

Intended learning outcomes

Knowledge

• Develop a specialised knowledge of key strategies related to Deep Learning.
• Critically evaluate diverse scholarly views on Deep Learning.
• Acquire knowledge of deep learning systems like ADAM, AdaGrad, RMSProp etc. Students also learn AutoEncoders, VAEs and Word2Vec.
• Critically assess the relevance of theories for business applications in the domain of technology.
• Develop a critical knowledge of Deep Learning.

Skills

• Apply an in-depth domain-specific knowledge and understanding to Deep Learning.
• Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing.
• Creatively apply Deep Learning techniques to develop critical and original solutions for computational problems.
• Autonomously gather material and organise it into coherent problem sets or presentation.

Competencies

• Apply a professional and scholarly approach to research problems pertaining to Deep Learning.
• Demonstrate self-direction in research and originality in solutions developed for Deep Learning.
• Create synthetic contextualised discussions of key issues related to Deep Learning.
• Solve problems and be prepared to take leadership decisions related to the methods and principles of Deep Learning.
• Act autonomously in identifying research problems and solutions related to Deep Learning.
• Efficiently manage interdisciplinary issues that arise in connection to Deep Learning.

Applied Data Analytics

125 hours | 5 ECTS

About

This course is designed to bridge the gap between data theory and real-world applications. This course focuses on the end-to-end process of data analytics, including data collection, cleaning, exploratory data analysis, and visualisation. Students will learn how to apply statistical methods and machine learning techniques to analyse and interpret complex datasets, uncovering actionable insights that drive strategic decision-making across various domains such as business, healthcare, and technology.

The course combines theoretical instruction with hands-on projects, allowing students to work with real datasets and employ state-of-the-art tools and software. By engaging in case studies and practical exercises, students will develop the skills necessary to tackle data-driven problems and present their findings effectively. Upon completion, students will be well-equipped to leverage data analytics to solve real-world challenges and contribute to data-informed decision-making processes in their professional careers.

Teachers

Mohga Soliman Emam Hewashy

Marwa Fawzy Qabeel Ibraheem

Elie Varesse Wanko Pohdie

Rafael Martins Buck de Godoy

Dipika Chadha

Alberto Ivo da Costa Vieira

Khaled Gamaleldin Mohamed Saleh Zoheir

Thiago Meireles Grabe

Oscar Mauricio Forero Carrillo

Anshul Rampal

Rajat Sharma

Amir Hassanein Mohamed El Amir Mohamed

Ankita Mehta

Rodolfo Yoshii

Jean Paul Barddal

Moamen Ramadan Abdelkader Abdelkawy

SEPTI RITO TOMBE

NEO CE ZHENG (LIANG CEZHENG)

Daniel Ceragioli Abrão

Alex Mathew

Ahmed Gamal Ali

Panse Hafez

Osama Mohammed Moustafa Hosameldeen

RAGHAVENDRA PRASAD SAVADA

Shashank Shekhar

Sarthak Gupta

Abdalla Ebrahim Said Abdelrahman

Sudhakar Goyal

Mahmoud Assaf

Balaji Murugan

Satyajeet Sindhiyani

Rafael Castillo Alcibar

Ana Maria Bastidas

Intended learning outcomes

Knowledge

• Analyse how data analytics contributes to decision-making processes within various industries and organisational contexts.
• Define and explain fundamental concepts of data analytics, including data preprocessing, statistical analysis, and data visualisation techniques.
• Recognize and differentiate between various data types and select appropriate analytical methods for analysing them.

Skills

• Build and implement analytical models using tools like Python, R, or SQL, to extract insights from complex data sets.
• Apply data cleaning and preprocessing techniques to prepare raw data for analysis, ensuring accuracy and reliability.
• Create visualisations using software such as Tableau or Power BI to effectively communicate data-driven insights to stakeholders.

Competencies

• Demonstrate the ability to integrate data analytics into broader business strategies, ensuring that analytical insights align with organisational goals.
• Display competency in leading data analytics projects within multidisciplinary teams, managing the entire analytics lifecycle from data collection to actionable insights.
• Exhibit the ability to design and implement data-driven solutions to solve complex, real-world problems, leveraging advanced analytics techniques.

2 - Elective Modules

625 hours | 25 ECTS

Artificial Intelligence in Industry Applications

125 hours | 5 ECTS

About

This course is designed to bridge the gap between theoretical AI concepts and their real-world applications across various industries. This course explores how AI technologies are implemented to solve industry-specific challenges and drive innovation in fields such as healthcare, finance, manufacturing, and retail. Students will examine case studies and practical examples of AI solutions that optimise processes, enhance decision-making, and create value for businesses and organisations.

Through hands-on projects and collaborative assignments, students will gain experience in deploying AI systems and tools tailored to industry needs. They will work with real-world datasets and use industry-standard platforms to develop and implement AI solutions, learning to address unique operational and strategic problems. By the end of the course, students will be equipped with the skills and insights needed to apply AI technologies effectively in various industrial contexts, making them valuable assets in transforming and advancing industry practices through artificial intelligence.

Teachers

Akhil Kumar

Ramkumar Manoharan

Kanika Garg

Shashank Shekhar

Amirhossein Parizi

Intended learning outcomes

Knowledge

• Explain how AI technologies are used to optimise processes like supply chain management, predictive maintenance, and customer service within different industries.
• Identify and describe the key applications of AI in various industries, such as healthcare, finance, manufacturing, and transportation.
• Analyse case studies of successful AI implementations in industry, discussing the challenges, solutions, and outcomes.

Skills

• Evaluate the impact of AI solutions on business operations, including improvements in efficiency, cost reduction, and customer satisfaction.
• Implement AI solutions in a simulated industry environment, demonstrating the application of AI tools and technologies to real-world scenarios.
• Develop AI models tailored to address specific challenges in industries such as healthcare, finance, or logistics using appropriate tools and techniques.

Competencies

• Demonstrate the ability to design AI-driven strategies that can transform industry practices, addressing current limitations and leveraging AI for competitive advantage.
• Lead cross-functional teams in the development and deployment of AI projects within an industry, ensuring collaboration and successful implementation.
• Demonstrate the ability to adapt existing AI solutions to meet emerging needs and challenges within an industry, ensuring the AI applications remain relevant and effective.

Java Web Developer

125 hours | 5 ECTS

About

This course advances your Java development skills through real-world projects in web services, security, persistence, and DevOps. Learners will work with industry-standard tools such as Spring Boot, Jenkins, Selenium, Docker, and Spring Data JPA while building scalable applications using REST APIs, GraphQL, Swagger, and modern configuration management practices.

Teachers

Norhan Mohammed Ebrahim

Moataz Mahmoud Soliman Ibrahim

Upendra Singh

Elie Varesse Wanko Pohdie

Mostafa Montaser

Dinanath Pandey

Ilia Sharipov

Prashant Kumar Lakhotia

Manmeet Singh

Rahul Dev

Nitish Kumar

Patel Aksharkumar Babulal

SHAMSELDEN MOHAMED MAHMOUD KESHK

Kirolos Nasrallah

UTKARSH SHUKLA

Intended learning outcomes

Knowledge

• Identify and explain foundational concepts in Java web development, including RESTful architecture, object-relational mapping, and web security principles.
• Compare and contrast current tools and ecosystems used in Java web development and articulate their appropriate use-cases for scalable enterprise applications.
• Describe ethical, legal, and societal implications arising from Java web development, including security, data protection, and responsible deployment practices.
• Critically evaluate design patterns and architectures for Java web services, including persistence layers, API design, and DevOps integration workflows.
• Summarise the main frameworks and tools used in Java web applications, such as Spring Boot, Spring Data JPA, GraphQL, Swagger, Jenkins, and Docker.

Skills

• Construct, evaluate, and optimise Java web systems using frameworks such as Spring Boot, Spring Data JPA, and testing tools like Selenium.
• Integrate components and APIs to build end-to-end Java web solutions, including GraphQL interfaces, Swagger documentation, and Docker-based deployment.
• Apply industry-standard tools and workflows to implement practical Java web applications, including REST APIs, persistence mechanisms, and CI/CD pipelines.
• Execute professional project workflows when developing and maintaining Java enterprise applications.
• Communicate technical results effectively to both technical and non-technical stakeholders, including system design documentation, deployment reports, and testing summaries.

Competencies

• Demonstrate adaptive learning and continuous professional development to stay current with advances in Java frameworks, DevOps tools, and web service architectures.
• Manage project resources, timelines, and risks to deliver production-ready Java web solutions using enterprise frameworks and DevOps practices.
• Strategically assess and select technologies and approaches in Java web development to align with organisational goals and performance requirements, including Spring Boot and containerization.
• Lead small cross-functional teams to plan and deliver Java web development projects involving REST APIs, persistence, and enterprise-grade deployment.
• Apply ethical reasoning and governance to guide decisions in Java-based web projects, ensuring secure, compliant, and maintainable solutions.

Cloud Developer with Microsoft Azure

125 hours | 5 ECTS

About

This course develops learners’ capability to design, build, deploy, migrate, and operate cloud-based applications on Microsoft Azure. The course introduces cloud-native fundamentals and microservices architecture basics, then focuses on practical implementation across deployment, monitoring, automation, and governance. Learners will work with Azure SQL database services, apply cloud security practices, and use Azure Monitor to implement observability for applications and infrastructure. The course also builds competence in migration planning and execution, cost management, and operational automation to support reliable and efficient cloud workloads. By the end of the course, learners will be able to deliver Azure-based solutions that are secure, observable, cost-aware, and aligned with modern cloud engineering practices.

Teachers

Dhruv Kinger

Bishui Abdal-maseeh Mikhail Gadalla

Shubham Singh Kalyanwat

Abdulhakeem Omotolani Yaqoob

Simran

Tamer Khamis Ibrahim

Intended learning outcomes

Knowledge

• Describe approaches to cloud migration, including assessment, dependency mapping, migration strategies, and post-migration validation and monitoring requirements.
• Explain governance and operations concepts in Azure, including security principles, observability (metrics/logs/traces), and cost management mechanisms and trade-offs.
• Explain Azure cloud concepts for application delivery, including compute, networking, storage, microservices basics, and how these choices influence scalability and reliability.

Skills

• Configure monitoring and observability using Azure Monitor (and related services as applicable), set up dashboards/alerts, and apply Azure automation to streamline routine operations.

• Implement security controls for Azure workloads, including identity and access practices, secure configuration, and protection of data and secrets across application components.

• Build and deploy Azure applications using appropriate services, implement microservices-oriented designs where suitable, and configure Azure SQL databases for application data needs.

Competencies

• Independently deliver an end-to-end Azure solution that includes deployment, monitoring, and operational automation, demonstrating design choices aligned with reliability and maintainability needs.

• Take responsibility for secure and compliant cloud operation by applying access control, secure configurations, and evidence-based monitoring for risk detection and response.

• Evaluate and optimize cloud workloads by balancing performance, reliability, and cost, using telemetry and cost insights to recommend and implement improvements.

Machine Learning Engineer with Microsoft Azure

75 hours | 3 ECTS

About

This course teaches learners to scale machine learning solutions in the cloud using Azure Machine Learning. Students will work with Azure ML Studio, the SDK, AutoML, and Azure Pipelines to deploy and monitor models. The program also covers pipeline automation, Kubernetes security, and API management for building robust, production-ready ML systems.

Teachers

Thiago Meireles Grabe

Ramkumar Manoharan

Rajat Sharma

Radwa Sayed Mohamed Helmy

Abdulhakeem Omotolani Yaqoob

Mohammed Samir Mahmoud

Amit Lathiya

Zohaib Khawaja

Sudhakar Goyal

Intended learning outcomes

Knowledge

• Critically evaluate common design patterns and architectures in artificial intelligence / machine learning, including scalable training, model registry, and automated pipelines.
• Identify and explain foundational concepts in artificial intelligence / machine learning, using appropriate terminology and examples aligned with cloud-based ML engineering.
• Describe ethical, legal, and societal implications arising from applied work in artificial intelligence / machine learning, including privacy, governance, and responsible AI practices in cloud environments.
• Summarise the main algorithms, models, and frameworks used in artificial intelligence / machine learning and their practical trade-offs when operationalised on cloud platforms.
• Compare and contrast current tools and ecosystems used in artificial intelligence / machine learning, and articulate their appropriate use-cases, with emphasis on Azure ML services.

Skills

• Construct, evaluate, and optimise models/systems relevant to artificial intelligence / machine learning, using cloud-based resources for training, deployment, and monitoring.
• Execute professional project workflows when developing and operationalising cloud-based ML solutions.
• Apply industry-standard tools and workflows to implement practical solutions in artificial intelligence / machine learning, demonstrating reproducible engineering practice with Azure ML Studio, SDK, and AutoML.
• Communicate technical results effectively to both technical and non-technical stakeholders, including visualisations, cloud dashboards, and model performance reports.
• Integrate components and APIs to build end-to-end solutions in artificial intelligence / machine learning, including Azure ML pipelines, Kubernetes deployment, and API endpoint management.

Competencies

• Lead small cross-functional teams to plan and deliver artificial intelligence / machine learning projects that meet business or organisational objectives.
• Apply ethical reasoning and governance to guide decisions in artificial intelligence / machine learning-focused projects, ensuring fairness, compliance, and responsible cloud-based model deployment.
• Strategically assess and select technologies and approaches in artificial intelligence / machine learning to align with organisational goals and cloud infrastructure constraints.
• Demonstrate adaptive learning and continuous professional development to stay current with advances in artificial intelligence / machine learning.
• Manage project resources, timelines, and risks to deliver production-ready artificial intelligence / machine learning solutions.

Predictive Modelling

125 hours | 5 ECTS

About

This course is dedicated to equipping students with the skills to develop and apply models that forecast future trends and behaviours based on historical data. This course covers a range of predictive techniques, including linear and logistic regression, time series analysis, ensemble methods, and advanced machine learning algorithms. Students will learn how to build, validate, and deploy predictive models to make accurate forecasts in various domains such as finance, healthcare, and marketing. The course emphasises both theoretical understanding and practical application, with students engaging in hands-on projects and case studies that demonstrate real-world uses of predictive modelling. By working with diverse datasets and employing state-of-the-art tools, students will gain experience in model selection, performance evaluation, and optimization. By the end of the course, students will be adept at creating robust predictive models that drive data-informed decision-making and contribute to strategic planning in their professional fields.

Teachers

Thiago Meireles Grabe

Abdelrahman Omran Mortaz Dabash

Dhruv Kinger

Abdelrhman Hazem Wahdan

Ramkumar Manoharan

Rajat Sharma

Ankita Mehta

Sepideh SaebiMoghaddam

Moamen Ramadan Abdelkader Abdelkawy

Shashank Shekhar

Neha Verma

Sudhakar Goyal

Intended learning outcomes

Knowledge

• Analyse and compare the strengths, weaknesses, and appropriate use cases of different predictive modelling techniques.

• Define and explain the basic principles, methodologies, and algorithms used in predictive modelling, including linear regression, decision trees, and neural networks.

• Identify and describe the types of data needed for various predictive models, including how to prepare and preprocess data for effective model building.

Skills

• Interpret the outputs of predictive models and effectively communicate the results, implications, and limitations to stakeholders.

• Refine and optimise predictive models through techniques such as hyperparameter tuning, feature selection, and model validation.

• Use appropriate tools and techniques to build and test predictive models on real-world data sets.

Competencies

• Display competency in working collaboratively in teams to develop, test, and deploy predictive models, leveraging diverse skill sets and perspectives to enhance model performance and applicability.

• Demonstrate the ability to design and implement an end-to-end predictive modelling solution, from data collection and preprocessing to model deployment and monitoring.

• Exhibit the ability to apply predictive modelling techniques creatively in new or emerging domains, addressing specific challenges and proposing innovative solutions.

Front End UI/UX Development

125 hours | 5 ECTS

About

This is a hands-on course on designing responsive, modern, and lightweight UI for

web, mobile, and desktop applications using HTML5, CSS, and Frameworks like

Bootstrap 4. This course starts with an introduction to how web browsers, mobile

apps, and web servers work. We then dive into each of the nitty-gritty details of

HTML5 to build webpages. We would start with simple web pages and then

graduate to more complex layouts and features in HTML like forms, iFrames,

multimedia playback, and using web APIs. We then go on to learn stylesheets based on CSS 4 and how browsers interpret CSS files to render web pages. Once again, we use multiple real-world example web pages to learn the internals of CSS4. We learn popular good practices for writing responsive HTML and CSS code, which is also interoperable on mobile browsers, apps, and desktop apps. We would introduce students to building desktop apps using HTML and CSS using toolkits like Electron. We also study popular frameworks for front end development like Bootstrap 4, which can speed up UI development significantly.

Teachers

Fernando Augusto de Marins

Mohga Soliman Emam Hewashy

Saw Tze Hui

Prateek Kailash Agarwal

Dipika Chadha

Alberto Ivo da Costa Vieira

Mona Muhammad Al-Gharib

Anshul Rampal

Munish Goyal

Tanuj Nagpal

Dinanath Pandey

Rachel Robin Joyce

Sagar Choudhary

Shubham Kandwal

Shubham Prakash

Dina tahoun

Ahmed Fouad Mohamed Farid Lotfy

Shubham Saxena

Manmeet Singh

Shubham Singh Kalyanwat

Raunak Singh

Swati Chandiramani

SEPTI RITO TOMBE

Megha Jain

Wong Lip Soon

Mostafa Mohamed ElShawadfy ElAlfy Ismail

Deepak Sharma

Simran

Shubham Chhabra

Mohamed Ameen Abdelhamid Allam

Ujjwal Sharma

UTKARSH SHUKLA

Sebastian Velasquez

Mostafa ElSheikh

Ehab Hamdy Mohamed Mohamed Hussein

Satyajeet Sindhiyani

Intended learning outcomes

Knowledge

• Acquire knowledge of popular frameworks/libraries in use: React.js, jQuery and AngularJS
• Critically assess the relevance of theories for business applications in the domain of technology
• Critically evaluate diverse scholarly views on front end development
• Develop a specialised knowledge of key strategies related to front end development
• Develop a critical knowledge of front end development

Skills

• Creatively apply front end development applications to develop critical and original solutions for computational problems
• Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
• Apply an in-depth domain-specific knowledge and understanding to front end development solutions
• Autonomously gather material and organise it into coherent problem sets or presentations

Competencies

• Efficiently manage interdisciplinary issues that arise in connection to front end development
• Solve problems and be prepared to take leadership decisions related to the methods and principles of front end developmen
• Demonstrate self-direction in research and originality in solutions developed for front end development
• Create synthetic contextualised discussions of key issues related to front end development
• Act autonomously in identifying research problems and solutions related to front end development
• Apply a professional and scholarly approach to research problems pertaining to front end development

Intermediate Python

75 hours | 3 ECTS

About

This course strengthens intermediate-level Python skills for real-world applications across web development, data science, and automation. Learners will work with file I/O, functional programming, data structures, object-oriented Python, command-line tools, and essential libraries to build efficient, scalable programs. By the end, students will create a portfolio that demonstrates practitioner-level Python capability.

Teachers

Ken Adachi

Upendra Singh

Elie Varesse Wanko Pohdie

Oscar Mauricio Forero Carrillo

Hossam Reda Awad Megahed

Lim Zhi Yuan

Patel Aksharkumar Babulal

Satyajeet Sindhiyani

Intended learning outcomes

Knowledge

• • Identify and explain foundational concepts in Python programming, including file I/O, data structures, functional programming, and object-oriented design.

• • Compare and contrast current Python tools and ecosystems and articulate their appropriate use-cases for building functional, efficient applications.

• • Summarise the main tools, libraries, and techniques used in intermediate Python development, and describe their practical trade-offs.

• • Critically evaluate design patterns and programming structures relevant to Python applications, considering scalability, code clarity, and maintainability.

• • Describe ethical, legal, and societal implications arising from Python-based applications, including issues related to data handling, security, and transparency.

Skills

• • Execute professional project workflows when developing and deploying Python applications.

• • Communicate technical results effectively to both technical and non-technical stakeholders, including code documentation, visualisations, and performance summaries.

• • Integrate components and APIs to build end-to-end Python applications using standard libraries and third-party modules.

• • Construct, evaluate, and optimise Python systems using data structures, functional programming approaches, and object-oriented techniques.

• • Apply industry-standard tools and workflows to implement practical Python solutions, including file handling, command-line tools, and data-processing scripts.

Competencies

• • Apply ethical reasoning and governance to guide decisions in Python development projects, ensuring responsible use of automation, data handling, and application behaviour.

• • Manage project resources, timelines, and risks to deliver production-ready Python solutions using intermediate programming techniques.

• • Strategically assess and select technologies and approaches in Python programming to align with organisational goals and software requirements.

• • Demonstrate adaptive learning and continuous professional development to stay current with advances in Python programming, libraries, and tooling.

• • Lead small cross-functional teams to plan and deliver Python-based technology projects involving data processing, automation, and application functionality.

Google Analytics 4

75 hours | 3 ECTS

About

This course teaches learners how to track, analyze, and interpret user data using Google Analytics 4 (GA4). Students will uncover meaningful insights, optimize user experiences, and support data-driven decision-making through hands-on GA4 practice.

Teachers

Gabriel Donato

Prashant Kumar Lakhotia

SEPTI RITO TOMBE

Noha Wagdi Abdellatif Metwally Sheta

Dániel Hadházi

Intended learning outcomes

Knowledge

• Critically evaluate common design patterns and architectures in data science & analytics, including considerations for data accuracy, attribution modelling, and reporting.
• Identify and explain foundational concepts in data science & analytics, including event-based tracking and attribution within Google Analytics 4.
• Summarise the main algorithms, models, and frameworks used in data science & analytics and their practical trade-offs.
• Compare and contrast current tools and ecosystems used in data science & analytics, including GA4, Looker Studio, and related reporting platforms.
• Describe ethical, legal, and societal implications arising from applied work in data science & analytics, including issues of privacy and responsible data use.

Skills

• Apply industry-standard tools and workflows to implement practical solutions in data science & analytics, demonstrating reproducible engineering practice.
• Execute professional project workflows when developing analytics solutions.
• Communicate technical results effectively to both technical and non-technical stakeholders, including dashboards, presentations, and insights reports.
• Construct, evaluate, and optimise models/systems relevant to data science & analytics, including acquisition, attribution, and event-based reporting.
• Integrate components and APIs to build end-to-end analytics solutions, including GA4 event setups, Looker visualisations, and data interpretation workflows.

Competencies

• Lead small cross-functional teams to plan and deliver data science & analytics projects that meet business or organisational objectives.
• Strategically assess and select technologies and approaches in data science & analytics to align with organisational goals and constraints.
• Apply ethical reasoning and governance to guide decisions in data science & analytics-focused projects, ensuring fairness, privacy, and compliance.
• Manage project resources, timelines, and risks to deliver production-ready data science & analytics solutions.
• Demonstrate adaptive learning and continuous professional development to stay current with advances in data science & analytics.

Introduction to Cybersecurity

125 hours | 5 ECTS

About

This course builds a strong foundation in cybersecurity, equipping learners to protect digital assets and manage risk effectively. Students will develop core skills in threat analysis, vulnerability assessment, and network defense, while learning to implement security controls, use industry-standard tools, and apply best practices to safeguard sensitive data in modern personal and professional environments.

Teachers

Ken Adachi

Mahmoud Ahmed Abdelazim Abdelhamid Eltayeb

Ramkumar Manoharan

Abdul-Razak Bawa Yussif

Luka Ayad Michael Gerges

SEPTI RITO TOMBE

Fatma alzahra A. Alemam

Deepak Sharma

Osama Mohammed Moustafa Hosameldeen

Amit Lathiya

UTKARSH SHUKLA

Intended learning outcomes

Knowledge

• Describe ethical, legal, and societal implications of cybersecurity practices, including privacy protection, responsible data handling, and governance (GRC).
• Identify and explain foundational concepts in cybersecurity, including threat intelligence, cryptography, and risk management.
• Compare and contrast current tools and ecosystems used in cybersecurity and articulate appropriate use-cases for threat detection, vulnerability assessment, and network defense.
• Summarise the main models, frameworks, and methodologies used in cybersecurity, such as vulnerability management and security auditing.
• Critically evaluate common security architectures and defense mechanisms, including considerations for scalability, robustness, and compliance.

Skills

• Execute professional project workflows (documentation, version control, structured assessments) when conducting cybersecurity tasks.
• Construct, evaluate, and improve systems relevant to cybersecurity, using techniques such as vulnerability assessment, threat analysis, and basic cryptographic operations.
• Communicate technical security findings effectively to both technical and non-technical stakeholders, including reports on vulnerabilities, risks, and compliance status.
• Apply industry-standard tools and workflows to implement practical cybersecurity solutions, demonstrating reproducible and rigorous security practice.
• Integrate components and security controls to build end-to-end defensive systems, including monitoring mechanisms and security auditing processes.

Competencies

• Apply ethical reasoning and governance to guide decisions in cybersecurity-focused initiatives, ensuring responsible handling of sensitive data and compliance with regulations.
• Strategically assess and select technologies and approaches in cybersecurity to align with organisational goals, risk profiles, and security requirements.
• Lead small cross-functional teams to plan and deliver cybersecurity projects that meet organisational or professional objectives.
• Demonstrate adaptive learning and continuous professional development to stay current with advances in cybersecurity practices and technologies.
• Manage project resources, timelines, and risks to deliver production-ready cybersecurity solutions that strengthen digital resilience.

Relational Databases

125 hours | 5 ECTS

About

This is a core and foundational course which aims to equip the student with the ability to model, design, implement and query relational database systems for real-world data storage & processing needs. Students would start with diagrammatic tools (ER-diagram) to map a real world data storage problem into entities, relationships and keys. Then, they learn to translate the ER-diagram into a relational model with tables. SQL is then introduced as a de facto tool to create, modify, append, delete, query and manipulate data in a relational database. Due to SQL’s popularity, the course spends considerable time building the ability to write optimised and complex queries for various data manipulation tasks. The module exposes students to various real world SQL examples to build solid practical knowledge. Students then move on to understanding various trade-offs in modern relational databases like the ones between storage space and latency. Designing a database would need a solid understanding of normal forms to minimise data duplication, indexing for speedup and flattening tables to avoid complex joins in low-latency environments. These real-world database design strategies are discussed with practical examples from various domains. Most of this course uses the open source MySQL database and cloud-hosted relational databases (like Amazon RDS) to help students apply the concepts learned on real databases via assignments.

Teachers

Ken Adachi

Elie Varesse Wanko Pohdie

Akhil Kumar

Thiago Meireles Grabe

Oscar Mauricio Forero Carrillo

Shubham Kandwal

Rodolfo Yoshii

Prateek Pushkar Agarawal

Nasim Banu

Osama Mohammed Moustafa Hosameldeen

Yu Zeng

Sarthak Gupta

Shrey Datta

Satyajeet Sindhiyani

Intended learning outcomes

Knowledge

• Critically evaluate diverse scholarly views on relational databases.

• Develop a critical knowledge of relational databases.

• Acquire knowledge of SQL as a tool to create, modify, append, delete, query and manipulate data in a relational database.

• Critically assess the relevance of theories for business applications in the domain of technology.

• Develop a specialised knowledge of key strategies related to Relational Databases.

Skills

• Creatively apply Relational Databases methods to develop critical and original solutions for computational problems.

• Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing.

• Apply an in-depth domain-specific knowledge and understanding to Relational Databases.

• Autonomously gather material and organise it into a coherent presentation or essay.

Competencies

• Apply a professional and scholarly approach to research problems pertaining to Relational Databases.

• Act autonomously in identifying research problems and solutions related to Relational Databases.

• Create synthetic contextualised discussions of key issues related to Relational Databases.

• Efficiently manage interdisciplinary issues that arise in connection to implementation and query of relational databases .

• Solve problems and be prepared to take leadership decisions related to the methods and principles of Relational Databases.

• Demonstrate self-direction in research and originality in solutions developed for Relational Databases.

Neural Networks and Deep Learning

125 hours | 5 ECTS

About

This course is focused on equipping students with the skills and knowledge needed to effectively incorporate AI technologies into various business and industrial processes. This course explores a wide range of strategies for integrating AI, including deployment methodologies, system interoperability, and change management. Students will learn about the challenges and best practices for implementing AI solutions in real-world environments, ensuring seamless integration with existing systems and maximising organisational benefits.

Throughout the course, students will engage in case studies, practical projects, and interactive discussions that highlight successful AI integration across different sectors. By understanding the strategic, technical, and ethical considerations involved in AI deployment, students will be prepared to lead AI initiatives and drive innovation in their organisations. This course is essential for aspiring AI professionals aiming to bridge the gap between cutting-edge AI technologies and their practical applications, ultimately contributing to the advancement of the AI field.

Teachers

Aseem Arora

Rashmi

Thiago Meireles Grabe

Deep Chandresh Doshi

Abdelrhman Hazem Wahdan

Ramkumar Manoharan

Prasun Srivastava

Amit Lathiya

Jihwan Min

Intended learning outcomes

Knowledge

• Describe the key concepts and definitions of artificial intelligence and compare different AI integration approaches and their outcomes.

• Identify key AI integration techniques and potential applications of AI across various industries.

• Explain the principles of AI system architecture and deployment.

Skills

• Develop an AI integration plan for a specific industry application demonstrating their ability to apply theoretical knowledge to practical scenarios.

• Assess the performance of integrated AI systems identifying areas for improvement, and proposing optimization strategies.

• Implement AI integration using relevant tools and platforms to integrate AI solutions into existing systems, showcasing their technical proficiency.

Competencies

• Collaborate effectively with cross-functional teams on AI integration projects demonstrating strong communication and teamwork skills.

• Demonstrate a commitment to ethical AI practices and responsible AI development.

• Design scalable and sustainable AI integration solutions ensuring long-term viability.

Amazon Web Services Part 2

125 hours | 5 ECTS

About

This course builds on foundational AWS knowledge, diving deeper into the platform's sophisticated features and services. Students will explore advanced networking configurations, security and compliance measures, and serverless architectures. Emphasis will be placed on practical applications, allowing students to design and implement complex AWS architectures, automate infrastructure management with Infrastructure as Code (IaC) tools, and optimize costs for scalable solutions.In addition to technical skills, the course covers advanced topics in containerization, orchestration with AWS services, and the development of continuous integration/continuous deployment (CI/CD) pipelines. Students will gain hands-on experience through labs and projects that simulate real-world scenarios, ensuring they can effectively deploy, manage, and scale applications on AWS. By the end of the course, students will be proficient in leveraging AWS's full potential to meet specific business requirements, ensuring security, compliance, and cost-efficiency in cloud environments.

Teachers

Thiago Meireles Grabe

Ramkumar Manoharan

Takondwa Fanuel Sanyude

Rajat Sharma

Shinto Theruvil Manuel

Amit Lathiya

Raymond Phan

Abdalla Ebrahim Said Abdelrahman

Lamia Hassan Mohamed Zainelabedeen Taha

Intended learning outcomes

Knowledge

• Describe advanced AWS services for container orchestration (ECS), continuous integration/delivery (CodePipeline), and serverless computing (Lambda).
• Identify and differentiate between high availability and disaster recovery solutions offered by AWS for ensuring application resilience.
• Explain the concept of serverless architecture and its advantages for scalability and cost management in different workflows.

Skills

• Deploy and manage containerized applications on Amazon ECS, integrating with CI/CD pipelines for automated deployments.
• Design and implement serverless functions using AWS Lambda for specific application functionalities, triggering them based on events.
• Utilize AWS tools for monitoring and logging (CloudWatch) to analyze application performance and troubleshoot issues in the cloud environment.

Competencies

• Configure and integrate high availability and disaster recovery solutions within an AWS infrastructure to ensure application uptime and data integrity.
• Design and implement a serverless architecture for a specific application use case, considering scalability and cost-efficiency.
• Develop and deploy a CI/CD pipeline using AWS CodePipeline to automate the build, test, and deployment process for complex applications within the cloud.

AI-Powered Software Engineer

200 hours | 8 ECTS

About

This course teaches learners the engineering workflow that keeps AI-driven development speed from turning into bugs, messy architecture, or long-term maintenance pain. The course builds practical skills in test-driven development to verify correctness, design patterns to enforce clean structure, and architecture patterns to keep systems scalable, alongside detecting mismatches and common failure modes in AI-generated code. Learners develop iterative prompt development techniques, conduct rigorous code reviews, and learn to manage AI-assisted development security risks and responsibility in AI code acceptance. Bringing it all together in Vibe Engineering, learners use Claude Code in a fully hosted development environment to plan, generate, review, test, and refactor a real project. By the end, learners don't just write more code — they ship software they can stand behind, applying software maintainability heuristics to real-world engineering scenarios.

Teachers

Ken Adachi

Elie Varesse Wanko Pohdie

Dheeraj Agrawal

Alberto Ivo da Costa Vieira

Juliano Vieira Martins

Zhaowei Wang

Shinto Theruvil Manuel

Patel Aksharkumar Babulal

RAHUL KUMAR

Mahmoud Assaf

Satyajeet Sindhiyani

Intended learning outcomes

Knowledge

• Has comprehensive knowledge and understanding of AI-assisted software engineering workflows.

• Uses specialised, multi-disciplinary theoretical and practical knowledge of test-driven development, design patterns, and architecture patterns, some of which is at the forefront of the field, forming the basis of originality in developing production-ready software with AI tools such as Claude Code.

Skills

• Develops new skills in response to emerging AI-integrated development techniques, and demonstrates leadership and innovation in complex and unpredictable software engineering contexts.

• Can communicate code review findings, mismatch detection, and architectural decisions to specialist and non-specialist audiences clearly and unambiguously, reaching conclusions that are the outcome of research, self-study, or applied engineering practice;

• Demonstrates specialised, multi-disciplinary knowledge that includes reflecting on the social and ethical responsibilities linked to responsibility in AI code acceptance and security risks in AI-assisted development;

• Performs critical evaluations and analysis with incomplete or limited information to detect mismatches, common failure modes, and security risks in AI-generated code in new or unfamiliar environments, and produces original solutions;

Competencies

• Demonstrates autonomy in the direction of learning and a high level of understanding of the processes involved in planning, generating, reviewing, testing, and refactoring real-world software projects;

• Manages iterative prompt development, code reviews, and AI-integrated development workflows, demonstrating the ability to respond to the fast-changing landscape of AI-assisted software engineering;

• Has the learning skills to allow continuation of study in AI-powered software engineering in a manner that may be largely self-directed or autonomous, applying software maintainability heuristics to novel, real-world development scenarios.

• Creates a research-based diagnosis of software quality and maintainability issues by integrating knowledge from new or interdisciplinary fields (software architecture, security, AI-assisted workflows), and makes judgements with incomplete or limited information;

Flying Car and Autonomous Flight Engineer

125 hours | 5 ECTS

About

This course introduces learners to autonomous aerial systems through hands-on drone robotics and advanced control techniques. Students will design, program, and test real software for real aircraft, gaining practical skills in building intelligent, autonomous flight systems.

Teachers

Deep Chandresh Doshi

Abdelrahman Ahmad Saeed Abdelaziz

Osama Mohammed Moustafa Hosameldeen

Neha Verma

Salam El Bsat

Intended learning outcomes

Knowledge

• Summarise the main algorithms, models, and frameworks used in aerial robotics, such as PID control, extended Kalman filters, and object state estimation.
• Compare and contrast tools and ecosystems used in drone robotics and autonomous flight, articulating appropriate use-cases for simulation, control, and onboard computation.
• Identify and explain foundational concepts in autonomous flight engineering, including quadrotor dynamics, 3D robot motion control, and state estimation.
• Critically evaluate common design patterns and architectures in autonomous flight systems, including stability, robustness, and sensor integration considerations.
• Describe ethical, legal, and societal implications arising from autonomous aerial systems, including airspace regulations, safety standards, and responsible use of autonomous aircraft.

Skills

• Execute professional project workflows when developing autonomous aerial software.
• Communicate technical results effectively to both technical and non-technical stakeholders, including system performance reports, control strategy explanations, and mission demos.
• Construct, evaluate, and optimise control models and algorithms for drones, using techniques such as PID tuning, Kalman filtering, and motion control design.
• Integrate components and sensors to build end-to-end autonomous flight pipelines, including state estimation, navigation, and control execution.
• Apply industry-standard tools and workflows to implement practical autonomous flight solutions, demonstrating reproducible engineering practice for aerial systems.

Competencies

• Strategically assess and select technologies and approaches in autonomous flight engineering to align with system requirements, environmental constraints, and mission objectives.
• Apply ethical reasoning and governance to guide decisions in autonomous flight projects, ensuring safety, regulatory compliance, and responsible deployment.
• Lead small cross-functional teams to plan and deliver engineering projects focused on autonomous flight, drone robotics, and aerial control systems.
• Demonstrate adaptive learning and continuous professional development to stay current with advances in autonomous systems, aerial robotics, and control engineering.
• Manage project resources, timelines, and risks to deliver production-ready autonomous flight solutions.

Cloud DevOps Engineer

100 hours | 4 ECTS

About

This course prepares learners for the cloud DevOps field by developing skills in automation and deployment practices. Students work with logging, continuous integration and deployment (CI/CD), GitHub Actions, build and deployment automation, and Kubernetes to design and operate reliable and scalable cloud systems.

Teachers

Ken Adachi

Thiago Meireles Grabe

Munish Goyal

Bishui Abdal-maseeh Mikhail Gadalla

Shubham Kandwal

Sepideh SaebiMoghaddam

Shubham Singh Kalyanwat

Zhaowei Wang

Radwa Sayed Mohamed Helmy

Daniel Ceragioli Abrão

Prasun Srivastava

Simran

Shinto Theruvil Manuel

Shubham Chhabra

Tamer Khamis Ibrahim

Patel Aksharkumar Babulal

Ahmed Abdelazeim Omar

Ujjwal Sharma

RAHUL KUMAR

Abdel-Aziz BINGUITCHA-FARE

Mostafa ElSheikh

Mahmoud Assaf

Joy Hilda Mukami Kareko

Ehab Hamdy Mohamed Mohamed Hussein

Satyajeet Sindhiyani

Intended learning outcomes

Knowledge

• Compare tools and ecosystems related to deployment automation, continuous integration, and Kubernetes.

• Critically evaluate architectures involving Kubernetes, CI/CD, and cloud logging systems.

• Identify concepts in cloud DevOps including continuous integration, continuous deployment, logging, and Kubernetes.

• Describe implications of applying continuous deployment, logging, and GitHub Actions within cloud environments.

• Summarise key frameworks and workflows such as CI/CD pipelines, GitHub Actions, and deployment automation.

Skills

• Construct and evaluate systems using continuous integration, continuous deployment, and Kubernetes orchestration.

• Apply CI/CD pipelines, logging tools, and deployment automation to cloud-based development workflows.

• Execute professional workflows using deployment automation, CI/CD, and Kubernetes practices.

• Integrate GitHub Actions, logging solutions, and automation scripts into end-to-end DevOps pipelines.

• Communicate technical results involving logging, continuous integration, and cloud deployment automation to stakeholders.

Competencies

• Apply ethical reasoning to cloud DevOps decisions involving logging, deployment automation, and continuous integration.

• Lead teams applying logging, continuous integration, continuous deployment, and deployment automation in cloud DevOps projects.

• Strategically assess tools such as Kubernetes, GitHub Actions, CI/CD workflows, and logging systems for organisational goals.

• Manage resources and risks when delivering cloud systems using continuous deployment, logging, and deployment automation.

• Demonstrate continuous learning in CI/CD, Kubernetes, GitHub Actions, and modern deployment automation.

Data Visualisation Tools

125 hours | 5 ECTS

About

This course is aimed to build a strong foundational knowledge of Data Analytics tools used extensively in the Data Science field. There now are powerful data visualisation tools used in the business analytics industry to process and visualise raw business data in a very presentable and understandable format. A good example is Tableau, used by all data analytics departments of companies and in data analytics companies in various fields for its ease of use and efficiency. Tableau uses relational databases, Online Analytical Processing Cubes, Spreadsheets, cloud databases to generate graphical type visualisations. Course starts with visualisations and moves to an in-depth look at the different chart and graph functions, calculations, mapping and other functionality. Students will be taught quick table calculations, reference lines, different types of visualisations, bands and distributions, parameters, motion chart, trends and forecasting, formatting, stories, performance recording and advanced mapping. At the end of this course, students will be prepared, if they desire, to earn such industry desktop certifications as a Tableau Desktop Specialist, a Tableau Certified Associate, or a Tableau Certified Professional.

Teachers

SURVESH SATISH CHAUHAN

Elie Varesse Wanko Pohdie

Rashmi

Oscar Mauricio Forero Carrillo

Anshul Rampal

Amir Hassanein Mohamed El Amir Mohamed

Luka Ayad Michael Gerges

SEPTI RITO TOMBE

Alex Mathew

Mohammad Ehshan khan

Intended learning outcomes

Knowledge

• Develop a critical understanding of key data science concepts as implemented in common software packages
• Critically evaluate diverse scholarly views on advanced visualisation strategies
• Develop a specialised knowledge of such concepts as bands and distributions, parameters, motion chart, trends and forecasting, formatting, stories, performance recording and advanced mapping
• Critically assess the relevance of theories of data visualisation for business applications in the realm of software engineering
• Acquire knowledge of various methods for telling stories with data across different formats

Skills

• Autonomously gather material and organise it into a coherent presentation or essay
• Creatively apply various visual and written methods for developing data visualisations
• Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
• Apply an in-depth domain-specific knowledge and understanding of the importance of data storytelling in software engineering

Competencies

• Solve problems and be prepared to take leadership decisions related to data visualisation strategies
• Efficiently manage interdisciplinary issues that arise in connection to advanced visual analytics
• Demonstrate self-direction in research and originality in solutions developed for data visualisation
• Create synthetic contextualised discussions of key issues related to time and space complexity in data science
• Apply a professional and scholarly approach to research problems pertaining to data visualisations, including dashboards and storytelling
• Act autonomously in identifying research problems and solutions related to implementing data science visualisations from scratch

Sensor Fusion Engineer

100 hours | 4 ECTS

About

The Sensor Fusion Engineer Nanodegree program teaches core concepts in sensor fusion and perception for self-driving cars. It covers lidar, radar, cameras, and Kalman filters, with hands-on work involving real-world data, filtering, segmentation, clustering, and object tracking. Learners complete a capstone project focused on building a complete sensor fusion pipeline for autonomous vehicles.

Teachers

Khaled Gamaleldin Mohamed Saleh Zoheir

Abdelrahman Ahmad Saeed Abdelaziz

Raymond Phan

Salam El Bsat

Intended learning outcomes

Knowledge

• Describe ethical, legal, and societal implications of deploying autonomous sensing systems, including safety, data usage, and environmental considerations.
• Summarise the main algorithms, models, and frameworks used in sensor fusion, including filtering, clustering, segmentation, and object tracking.
• Critically evaluate common perception architectures and fusion strategies, considering robustness, uncertainty, and real-time constraints.
• Identify and explain foundational concepts in sensor fusion, including lidar, radar, digital cameras, Kalman filters, and point cloud processing.
• Compare and contrast tools and ecosystems used in sensor fusion, and articulate appropriate use-cases for radar processing, camera-based perception, and point cloud analysis.

Skills

• Integrate lidar, radar, and camera components to build end-to-end fusion pipelines, including clustering, segmentation, and object-level tracking.
• Construct, evaluate, and optimise perception models and filtering systems, including Kalman filters and radar clutter thresholding methods.
• Communicate technical results effectively to both technical and non-technical stakeholders, including visualisations, performance summaries, and system reports.
• Execute professional project when developing perception and fusion systems.
• Apply industry-standard tools and workflows to implement practical sensor fusion solutions, demonstrating reproducible engineering practices with real-world data.

Competencies

• Lead small cross-functional teams to plan and deliver sensor fusion and autonomous systems projects that meet defined technical objectives.
• Demonstrate adaptive learning and continuous professional development to stay current with advances in perception, filtering, and autonomous systems technology.
• Apply ethical reasoning and governance to guide decisions in autonomous perception projects, ensuring safety, compliance, and responsible system behavior.
• Manage project resources, timelines, and risks to deliver production-ready sensor fusion solutions for complex environments.
• Strategically assess and select technologies and approaches in sensor fusion to align with organisational goals and system performance requirements.

Databases and Computer Networks

125 hours | 5 ECTS

About

This core foundational course equips students with knowledge of Database Management Systems (DBMS) and Computer Networks.The course starts with Entity-Relationship (ER) diagrams, a visual tool for mapping real-world data storage problems.Students learn to translate ER diagrams into a relational model with tables. SQL, the standard language for relational databases, is then introduced. Students will spend significant time building proficiency in writing optimised and complex SQL queries for various data manipulation tasks. Real-world examples will be used to solidify practical knowledge.

Next, the course explores trade-offs in modern relational databases, such as storage space versus latency. Designing efficient databases requires understanding normal forms to minimise data duplication, indexing for speed improvements,and flattening tables to avoid complex joins in low-latency environments. These real-world database design strategies are discussed with practical examples.The course utilises open-source MySQL databases and cloud-hosted relational databases (like Amazon RDS) for assignments, allowing students to apply learned concepts on real databases.Following the DBMS section, the course transitions to Computer Networks. Here, students will delve into foundational concepts like the OSI model, TCP/IP model, TCP/UDP protocols, subnetting, DNS (Domain Name System), Network Address Translation (NAT), private networks, Secure Sockets Layer (SSL), and network security principles.

Teachers

Bill Joe Hinostroza

Upendra Singh

Dipika Chadha

Alberto Ivo da Costa Vieira

Dinanath Pandey

Amir Hassanein Mohamed El Amir Mohamed

Rodolfo Yoshii

Ahmed Fouad Mohamed Farid Lotfy

Sepideh SaebiMoghaddam

Juliano Vieira Martins

Rahul Dev

Daniel Ceragioli Abrão

Nitish Kumar

Ahmed Tarek Montaser

Ujjwal Sharma

Mostafa ElSheikh

Satyajeet Sindhiyani

Intended learning outcomes

Knowledge

• Identify and differentiate between various database models and their strengths and weaknesses for different applications.
• Describe different network security threats and basic security principles in network design.
• Explain the fundamental concepts of computer networks, including network topologies, protocols, and communication models.

Skills

• Design and implement basic database queries using Structured Query Language (SQL) to retrieve and manipulate data.

• Configure basic network security measures like firewalls and access control lists (ACLs) to mitigate security risks.

• Analyze network traffic using tools like Wireshark and identify potential network performance issues.

Competencies

• Design and implement distributed database architectures for high availability and fault tolerance in varied environments.

• Select and configure appropriate database management systems for deployment pipelines based on data requirements and scalability needs.

• Integrate network security solutions with different tools and workflows to ensure secure communication and data transfer within applications.

Advanced Artificial Intelligence Concepts

125 hours | 5 ECTS

About

This course is aimed at deepening students' understanding of cutting-edge topics in artificial intelligence. This course delves into advanced methodologies such as generative adversarial networks (GANs), meta-learning, and advanced reinforcement learning techniques. Students will explore the theoretical underpinnings and practical implementations of these sophisticated AI concepts, focusing on their applications in complex problem-solving and innovation across various domains. Through a blend of advanced theoretical discussions and hands-on projects, students will engage with state-of-the-art tools and techniques, working on real-world problems and research projects. The course encourages critical thinking and problem-solving, preparing students to tackle the challenges of implementing and advancing AI technologies. By the end of the course, students will have a robust understanding of advanced AI concepts and be well-equipped to contribute to cutting-edge research and development in the field of artificial intelligence.

Teachers

Shubham Tripathi

Elie Varesse Wanko Pohdie

Dheeraj Agrawal

Robinson Jose Vasquez Ferrer

Ahmed Gamal Ali

Nasim Banu

Osama Mohammed Moustafa Hosameldeen

Jihwan Min

Raymond Phan

Rafael Castillo Alcibar

Intended learning outcomes

Knowledge

• Dissect and analyse complex AI architectures, including their components, interactions, and applications in solving realworld problems.
• Explain the underlying theories and principles behind advanced AI techniques, such as reinforcement learning, generative adversarial networks (GANs), and deep reinforcement learning.
• Identify and discuss emerging trends in advanced AI, including new algorithms, frameworks, and their potential impact on various industries.

Skills

• Implement advanced AI algorithms, such as GANs, reinforcement learning models, and deep neural networks, using programming languages like Python and frameworks like TensorFlow or PyTorch.
• Assess the performance of advanced AI systems by using metrics such as accuracy, precision, recall, and computational efficiency to fine-tune and optimise models.
• Design and develop custom AI solutions tailored to solve complex problems in fields like healthcare, finance, or autonomous systems.

Competencies

• Lead and manage innovative AI research projects that explore cutting-edge AI concepts, contributing to the academic and industry knowledge base.
• Demonstrate the competency to adapt advanced AI technologies to address new and unforeseen challenges in various domains, ensuring that AI solutions remain relevant and effective.
• Demonstrate the ability to integrate advanced AI techniques into existing software systems, ensuring compatibility, scalability, and performance optimization.

Agile Software Development

100 hours | 4 ECTS

About

This course introduces the principles and practices of Agile software development. Learners develop practical skills in Agile methodologies, sprint planning, and iterative improvement, while gaining experience with team communication, metrics, and real-world Agile workflows.

Teachers

Ken Adachi

Dipika Chadha

Abdul-Razak Bawa Yussif

Wong Lip Soon

Bavithra Duraisamy

Ahmed Yasser Maher

Amal Khalid Alshaikh

Sudhakar Goyal

Shrey Datta

Amir Serry

Intended learning outcomes

Knowledge

• • Compare and contrast Agile tools and ecosystems, articulating appropriate use-cases for project planning and communication.

• • Critically evaluate Agile design patterns and team structures, including considerations for scalability and continuous improvement.

• • Identify and explain foundational concepts in Agile software development, including sprints, metrics, and iterative workflows.

• • Summarise major frameworks and practices used in Agile development and their practical trade-offs.

• • Describe ethical, legal, and societal implications in Agile project environments, including communication transparency and team well-being.

Skills

• • Integrate Agile collaboration practices—such as sprint planning, stand-ups, and retrospectives—into project delivery pipelines.

• • Apply Agile workflows and tools to implement practical solutions in software development, ensuring reproducible and iterative progress.

• • Construct, evaluate, and optimise Agile processes using sprint metrics and feedback loops.

• • Communicate technical and project information effectively to both technical and non-technical stakeholders using Agile communication practices.

• • Execute professional Agile project workflows in real-world environments.

Competencies

• • Apply ethical reasoning and governance to guide decisions in Agile-focused projects, ensuring fairness, transparency, and accountability.

• • Lead small cross-functional teams to plan and deliver software engineering and professional projects using Agile methodologies.

• • Manage project resources, timelines, and risks to deliver production-ready software solutions in Agile environments.

• • Strategically assess and select Agile tools and approaches to align with organisational goals and constraints.

• • Demonstrate adaptive learning and continuous professional development to stay current with advances in Agile practices.

DevOps Tools Part 2

125 hours | 5 ECTS

About

DevOps Tools Part 2 is an advanced course designed for students building on the foundational knowledge from DevOps Tools Part 1. his course delves deeper into advanced DevOps tools and techniques that drive modern software development and operations. Students will explore sophisticated CI/CD pipelines with tools like GitLab CI/CD and Azure DevOps, and master infrastructure as code (IaC) with Terraform and AWS CloudFormation. The course emphasizes practical, hands-on experience, enabling students to automate and manage complex cloud environments effectively.In addition to advanced CI/CD and IaC, students will learn about service mesh architectures with Istio, advanced container orchestration with Kubernetes, and continuous monitoring and observability with tools such as Grafana and Jaeger. The course also covers security practices in DevOps, including integrating security tools into the CI/CD pipeline and managing secrets with HashiCorp Vault. By the end of the course, students will have the expertise to design, implement, and manage scalable, secure, and efficient DevOps workflows, preparing them for leadership roles in the field.

Teachers

Ken Adachi

Elie Varesse Wanko Pohdie

Ahmed Sabry Muhammad Mahmoud Mazroua

Thiago Meireles Grabe

Rajat Sharma

Abdulhakeem Omotolani Yaqoob

Juan Carlos Kuri Pinto

Mohammad Ehshan khan

Raymond Phan

Lamia Hassan Mohamed Zainelabedeen Taha

Intended learning outcomes

Knowledge

• Identify and differentiate between continuous monitoring and logging tools used in varied workflows for performance analysis and troubleshooting.
• Describe advanced features of DevOps tools (e.g., Git branching strategies, containerization technologies like Docker, container orchestration platforms like Kubernetes)
• Explain the concepts of infrastructure as code (IaC) and its role in automating infrastructure provisioning and management.

Skills

• Implement advanced Git workflows, including branching strategies, merging, and conflict resolution.
• Utilize continuous monitoring and logging tools to analyze application performance data and identify potential issues in production environments.
• Build and manage Docker containers for software deployment and isolate applications and their dependencies.

Competencies

• Design and deploy containerized applications using Docker and orchestrate them using platforms like Kubernetes for scalability and high availability.
• Develop IaC scripts using tools like Ansible or Terraform to automate complex infrastructure provisioning and management tasks.
• Integrate advanced monitoring and logging tools within a DevOps pipeline to provide real-time feedback on application health and performance, enabling proactive troubleshooting and incident management.

Introduction of Programming

100 hours | 4 ECTS

About

This course introduces foundational programming concepts used across software development, web development, and data analysis. Learners gain hands-on experience with Python programming, control flow, functions, object-oriented concepts, and basic command-line usage. The program also covers essential web technologies such as HTML and CSS to provide a well-rounded introduction to programming.

Teachers

Moataz Mahmoud Soliman Ibrahim

Fernando Augusto de Marins

Ken Adachi

Alberto Ivo da Costa Vieira

Oscar Mauricio Forero Carrillo

Tanuj Nagpal

Sagar Choudhary

Rajat Sharma

Juliano Vieira Martins

Manmeet Singh

Rahul Dev

Daniel Ceragioli Abrão

Deepak Sharma

Abdulhakeem Omotolani Yaqoob

Panse Hafez

Naveen Panwar

‪Moamen Mohamed Mohasseb Eltaieb‬‏

RAGHAVENDRA PRASAD SAVADA

Amal Khalid Alshaikh

Marwa Emad Mohammed

Patel Aksharkumar Babulal

Mohamed Ameen Abdelhamid Allam

Ujjwal Sharma

RAHUL KUMAR

Kirolos Nasrallah

Mostafa ElSheikh

Ehab Hamdy Mohamed Mohamed Hussein

Intended learning outcomes

Knowledge

• • Identify and explain foundational concepts in technology / professional, using appropriate terminology and examples.

• • Summarise the main algorithms, models, and frameworks used in technology / professional and their practical trade-offs.

• • Compare and contrast current tools and ecosystems used in technology / professional, and articulate their appropriate use-cases.

• • Critically evaluate common design patterns and architectures in technology / professional, including considerations for scalability and robustness.

• • Describe ethical, legal, and societal implications arising from applied work in technology / professional, including issues of bias, privacy, and transparency.

Skills

• • Execute professional project workflows when developing solutions in technology / professional.

• • Communicate technical results effectively to both technical and non-technical stakeholders, including visualisations and reports.

• • Integrate components and APIs to build end-to-end solutions in technology / professional, including deployment and monitoring pipelines.

• • Apply industry-standard tools and workflows to implement practical solutions in technology / professional, demonstrating reproducible engineering practice.

• • Construct, evaluate, and optimise models/systems relevant to technology / professional, using data-driven testing and performance metrics.

Competencies

• • Manage project resources, timelines, and risks to deliver production-ready technology / professional solutions.

• • Lead small cross-functional teams to plan and deliver technology / professional projects that meet business or research objectives.

• • Strategically assess and select technologies and approaches in technology / professional to align with organisational goals and constraints.

• • Demonstrate adaptive learning and continuous professional development to stay current with advances in technology / professional.

• • Apply ethical reasoning and governance to guide decisions in technology / professional–focused projects, ensuring fairness and compliance.

Data Product Manager

125 hours | 5 ECTS

About

This course teaches learners how to use data to shape and optimize product strategy. Students will build data pipelines, analyze key product metrics, and apply data-driven insights to guide product innovation and drive successful outcomes.

Teachers

Dipika Chadha

Thiago Meireles Grabe

Abdul-Razak Bawa Yussif

Sahib Kalra

Wong Lip Soon

Abdalla Ebrahim Said Abdelrahman

Intended learning outcomes

Knowledge

• Compare and contrast current tools and ecosystems used in data science and analytics, and articulate their appropriate use-cases within product development contexts.

• Summarise the main algorithms, models, and frameworks used in data science and analytics and their practical trade-offs when applied to product metrics and user behaviour analysis.

• Identify and explain foundational concepts in data science and analytics, using appropriate terminology and examples relevant to product management.

• Describe ethical, legal, and societal implications arising from data-driven work, including privacy, transparency, and responsible handling of user data.

• Critically evaluate common design patterns and architectures in data science and analytics, including data pipeline design, relational data models, and product experimentation.

Skills

• Integrate components and APIs to build end-to-end product data workflows, including data pipelines and dashboards for key performance indicators.
• Construct, evaluate, and optimise models/systems relevant to product analytics, using data-driven methods and performance metrics.
• Communicate technical and analytical results effectively to both technical and non-technical stakeholders, including product insights, dashboards, and narrative-driven presentations.
• Execute professional project workflows when developing data-driven product solutions.
• Apply industry-standard tools and workflows to implement practical data solutions, demonstrating reproducible practice in metrics tracking, cohort analysis, and data storytelling.

Competencies

• Lead small cross-functional teams to plan and deliver data science and analytics projects that support product decision-making and business objectives.
• Manage project resources, timelines, and risks to deliver production-ready data solutions that inform product strategies.
• Apply ethical reasoning and governance to guide decisions in data-focused product initiatives, ensuring fairness, compliance, and responsible use of data.
• Strategically assess and select technologies and approaches in data science and analytics to align with organisational goals, product needs, and resource constraints.
• Demonstrate adaptive learning and continuous professional development to stay current with advances in data-driven product management.

Robotics and Automation

125 hours | 5 ECTS

About

This course is designed to provide students with a comprehensive understanding of the principles and practices of robotics and automated systems. This course covers key topics such as robot kinematics, dynamics, control systems, and sensor integration. Students will explore the development and deployment of robotic systems for various applications, including manufacturing, logistics, and autonomous vehicles, gaining insights into the design, programming, and operation of robots.

Combining theoretical concepts with practical experience, the course features hands-on projects and case studies that illustrate the real-world implementation of robotics and automation technologies. Students will work with simulation tools and robotic platforms to design, test, and refine robotic systems, developing the skills necessary to address complex automation challenges. By the end of the course, students will be adept at integrating robotics and automation into various industries, driving innovation and efficiency through advanced technology solutions.

Teachers

Aseem Arora

Bill Joe Hinostroza

Hazem Antar Taha Aly Taha

Khaled Gamaleldin Mohamed Saleh Zoheir

Thiago Meireles Grabe

Deep Chandresh Doshi

Rachel Robin Joyce

Juan Carlos Kuri Pinto

Neha Verma

Sebastian Velasquez

Intended learning outcomes

Knowledge

• Describe various automation systems and their applications across different industries, such as manufacturing and logistics.

• Define and explain key concepts and components of robotics, including sensors, actuators, and control systems.

• Examine the integration of AI in robotics and how it enhances the capabilities of autonomous systems.

Skills

• Conduct kinematic and dynamic analysis of robotic systems to optimise their performance and ensure stability.

• Design and implement control systems for robots using simulation software or physical hardware.

• Create and optimise algorithms for autonomous navigation, enabling robots to perform tasks with minimal human intervention.

Competencies

• Demonstrate the competency to apply ethical considerations and sustainable practices in the design and deployment of robotics and automation systems, ensuring responsible use of technology.

• Exhibit the ability to lead teams in developing custom robotic solutions tailored to specific challenges, leveraging interdisciplinary knowledge and skills.

• Demonstrate the ability to integrate robotic systems with automation processes in an industrial setting, ensuring seamless operation and efficiency.

Agentic AI Engineering with LangChain and LangGraph

125 hours | 5 ECTS

About

This course teaches Python developers to build autonomous AI agents using LangChain and LangGraph. Learners start with core concepts—prompt templates, chains, memory, and single-tool agents—and progress to multi-tool planning, self-critique loops, and deployment. The program concludes with advanced capabilities, including retrieval-augmented generation, long-term memory, and multi-agent collaboration.

Teachers

Ken Adachi

Elie Varesse Wanko Pohdie

Dheeraj Agrawal

Alberto Ivo da Costa Vieira

Thiago Meireles Grabe

Ramkumar Manoharan

Sharad Jain

Sepideh SaebiMoghaddam

Shubham Saxena

SEPTI RITO TOMBE

Abdulhakeem Omotolani Yaqoob

Osama Mohammed Moustafa Hosameldeen

Shinto Theruvil Manuel

Amit Lathiya

Jihwan Min

Shashank Shekhar

Patel Aksharkumar Babulal

Mohamed Ameen Abdelhamid Allam

Kirolos Nasrallah

Rafael Castillo Alcibar

Intended learning outcomes

Knowledge

• Identify and explain foundational concepts in agentic AI engineering, including prompt templates, chains, memory, and single-tool agents.
• Compare and contrast current tools and ecosystems used in agentic AI, and articulate their appropriate use-cases for retrieval-augmented generation and long-term memory.
• Describe ethical, legal, and societal implications arising from applied work in autonomous agent development, including risks related to model outputs and external tool use.
• Summarise the main frameworks and components used in LangChain and LangGraph, including their roles in planning, memory, and multi-tool agent behaviour.
• Critically evaluate common design patterns and architectures in developing LLM-driven agents, including self-critique loops, multi-tool planning, and multi-agent collaboration.

Skills

• Execute professional project workflows when developing and deploying agentic AI applications.
• Apply industry-standard tools and workflows to implement practical solutions in agentic AI development using LangChain and LangGraph.
• Integrate components and APIs to build end-to-end autonomous agent workflows, including retrieval modules, multi-tool planning, and multi-agent collaboration.
• Construct, evaluate, and optimise agents and systems relevant to agentic AI, using prompt design, chain composition, memory integration, and tool-interaction techniques.
• Communicate technical results effectively to both technical and non-technical stakeholders, including explanations of agent behaviour, workflow logic, and system performance.

Competencies

• Manage project resources, timelines, and risks to deliver production-ready agentic AI solutions based on LangChain and LangGraph.
• Lead small cross-functional teams to plan and deliver agentic AI applications using LangChain and LangGraph, including prompt templates, chains, memory, and agent workflows.
• Apply ethical reasoning and governance to guide decisions in developing autonomous LLM agents, ensuring responsible and appropriate use of agent capabilities.
• Demonstrate adaptive learning and continuous professional development to stay current with advances in LLM-based agent development, including multi-tool planning, self-critique loops, and deployment practices.
• Strategically assess and select technologies and approaches in agentic AI to align with organisational goals and constraints, including the use of retrieval-augmented generation and long-term memory.

Digital Marketing

125 hours | 5 ECTS

About

This course builds job-ready digital marketing skills through hands-on practice and expert guidance. Learners will run live campaigns, analyze performance, and apply proven strategies to engage audiences and strengthen brand presence. Topics include social media advertising, content planning, audience targeting, and campaign evaluation—preparing students for real-world digital marketing roles.

Teachers

Gabriel Donato

Rey Gervacio Rosales

Swati Chandiramani

GULNIHAL ZILELI

Dániel Hadházi

Wessam Zakria Messak

Ana Maria Bastidas

Intended learning outcomes

Knowledge

• • Critically evaluate common design patterns and architectures in business / marketing, including considerations for scalability and impact.

• • Summarise the main models, frameworks, and strategies used in business / marketing and their practical trade-offs.

• • Describe ethical, legal, and societal implications arising from applied work in business / marketing, including issues of privacy, transparency, and responsible communication.

• • Compare and contrast current tools and ecosystems used in business / marketing, and articulate their appropriate use-cases.

• • Identify and explain foundational concepts in business / marketing, using appropriate terminology and examples.

Skills

• • Execute professional project workflows when developing solutions in business / marketing.

• • Integrate components, platforms, and APIs to build end-to-end solutions in business / marketing, including deployment and monitoring pipelines.

• • Construct, evaluate, and optimise models/systems relevant to business / marketing, using data-driven testing and performance metrics.

• • Apply industry-standard tools and workflows to implement practical solutions in business / marketing, demonstrating reproducible professional practice.

• • Communicate marketing results effectively to both technical and non-technical stakeholders, including visualisations and reports.

Competencies

• • Manage project resources, timelines, and risks to deliver production-ready business / marketing solutions.
• • Demonstrate adaptive learning and continuous professional development to stay current with advances in business / marketing.
• • Strategically assess and select technologies and approaches in business / marketing to align with organisational goals and constraints.
• • Apply ethical reasoning and governance to guide decisions in business / marketing–focused projects, ensuring fairness and compliance.
• • Lead small cross-functional teams to plan and deliver business / marketing projects that meet organisational or market objectives.

Natural Language Processing

50 hours | 2 ECTS

About

This course builds practical expertise in Natural Language Processing through hands-on projects guided by industry experts. Learners will work on sentiment analysis, machine translation, speech recognition, and voice interface design, gaining the skills to develop real-world NLP applications.

Teachers

Shubham Tripathi

Elie Varesse Wanko Pohdie

Dheeraj Agrawal

Ahmed Sabry Muhammad Mahmoud Mazroua

Sepideh SaebiMoghaddam

Mostafa Ahmed Asaad Abdelmegeed

Osama Mohammed Moustafa Hosameldeen

Raymond Phan

Salam El Bsat

Satyajeet Sindhiyani

Rafael Castillo Alcibar

Intended learning outcomes

Knowledge

• Describe ethical, legal, and societal implications arising from applied work in natural language processing, including issues of bias, privacy, and transparency.
• Critically evaluate common design patterns and architectures in natural language processing, including considerations for scalability and robustness.
• Compare and contrast current tools and ecosystems used in natural language processing, and articulate their appropriate use-cases.
• Identify and explain foundational concepts in natural language processing, using appropriate terminology and examples.
• Summarise the main algorithms, models, and frameworks used in natural language processing and their practical trade-offs.

Skills

• Integrate components and APIs to build end-to-end solutions in natural language processing, including deployment pipelines.
• Execute professional project workflows when developing solutions in natural language processing.
• Communicate technical results effectively to both technical and non-technical stakeholders, including visualisations and reports.
• Apply industry-standard tools and workflows to implement practical solutions in natural language processing, demonstrating reproducible engineering practice.
• Construct, evaluate, and optimise models/systems relevant to natural language processing, using data-driven testing and performance metrics.

Competencies

• Apply ethical reasoning and governance to guide decisions in artificial intelligence / machine learning–focused projects, ensuring fairness and compliance.
• Lead small cross-functional teams to plan and deliver artificial intelligence / machine learning projects that meet business or research objectives.
• Demonstrate adaptive learning and continuous professional development to stay current with advances in artificial intelligence / machine learning.
• Strategically assess and select technologies and approaches in artificial intelligence / machine learning to align with organisational goals and constraints.
• Manage project resources, timelines, and risks to deliver production-ready artificial intelligence / machine learning solutions

Data Science Principles

125 hours | 5 ECTS

About

This course is designed to introduce students to the core concepts and methodologies of data science. This course covers a broad range of topics, including data collection, cleaning, and preprocessing, as well as statistical analysis, data visualisation, and exploratory data analysis. Students will learn how to apply various data science techniques to extract valuable insights from large datasets, empowering them to make data-driven decisions in diverse fields such as business, healthcare, and technology. Throughout the course, students will engage in practical exercises and projects that emphasise the application of data science principles to real-world problems. By working with actual datasets and using state-of-the-art tools and software, students will develop the skills necessary to analyse, interpret, and present data effectively. Upon completion of the course, students will have a strong foundation in data science, enabling them to leverage data to solve complex problems and drive innovation in their professional careers within the realm of artificial intelligence.

Teachers

SURVESH SATISH CHAUHAN

Bill Joe Hinostroza

Marwa Fawzy Qabeel Ibraheem

Ken Adachi

Hazem Antar Taha Aly Taha

Elie Varesse Wanko Pohdie

Dipika Chadha

Alberto Ivo da Costa Vieira

Khaled Gamaleldin Mohamed Saleh Zoheir

Thiago Meireles Grabe

Oscar Mauricio Forero Carrillo

Ramkumar Manoharan

Rajat Sharma

Amir Hassanein Mohamed El Amir Mohamed

Rodolfo Yoshii

Paawan Mukker

Daniel Ceragioli Abrão

Prasun Srivastava

Osama Mohammed Moustafa Hosameldeen

Shashank Shekhar

Raymond Phan

Mahmoud Assaf

Satyajeet Sindhiyani

Rafael Castillo Alcibar

Ana Maria Bastidas

Intended learning outcomes

Knowledge

• List and describe essential data science principles, including data wrangling, statistical analysis, and predictive modelling.
• Explain how data science techniques are applied to extract insights that inform strategic business decisions across various industries.
• Analyse different types of data and their impact on model selection.

Skills

• Create and evaluate statistical models, such as linear regression and logistic regression, to analyse datasets and derive meaningful insights.
• Apply data cleaning and preprocessing techniques to real-world datasets.
• Assess the accuracy, precision, recall, and other performance metrics of various models, comparing their effectiveness for different types of data.

Competencies

• Work effectively with team members from diverse backgrounds to design, implement, and present data science solutions, demonstrating strong teamwork and communication skills.
• Critically assess and evaluate the ethical implications of data science techniques.
• Create comprehensive workflows that include data collection, preprocessing, modelling, and evaluation, tailored to solve particular real-world challenges.

Amazon Web Services Part 1

125 hours | 5 ECTS

About

This course provides a comprehensive overview of Amazon Web Services (AWS), focusing on core services and best practices for building and managing cloud-based infrastructure. Students will learn the fundamentals of cloud computing, explore key AWS services such as EC2, S3, RDS, and VPC, and gain hands-on experience in deploying and managing applications on the AWS platform. The course emphasizes practical skills, enabling students to design scalable, secure, and cost-effective cloud solutions.In addition to foundational AWS services, the course covers essential topics such as identity and access management (IAM), networking and security configurations, and monitoring and logging with CloudWatch. Students will also be introduced to Infrastructure as Code (IaC) using AWS CloudFormation and gain insights into setting up continuous integration/continuous deployment (CI/CD) pipelines with AWS tools. By the end of the course, students will have a solid understanding of AWS basics, equipping them with the knowledge and skills to effectively utilize AWS services in their DevOps practices and prepare for more advanced AWS coursework.

Teachers

SURVESH SATISH CHAUHAN

Ken Adachi

Rafael Martins Buck de Godoy

Alberto Ivo da Costa Vieira

Oscar Mauricio Forero Carrillo

Anshul Rampal

Takondwa Fanuel Sanyude

Zhaowei Wang

Paawan Mukker

Ahmed Gamal Ali

Abdulhakeem Omotolani Yaqoob

Jin-Hyuk Kwon

Shinto Theruvil Manuel

Shashank Shekhar

Kirolos Nasrallah

Mostafa ElSheikh

Joy Hilda Mukami Kareko

Balaji Murugan

Satyajeet Sindhiyani

Rafael Castillo Alcibar

Intended learning outcomes

Knowledge

• Differentiate between various AWS security mechanisms (IAM roles, security groups) and their role in securing cloud resources.
• Explain the concept of Infrastructure as a Service (IaaS) and its benefits for scalability and cost optimization in varied environments.
• Identify and describe core AWS services for compute (EC2), storage (S3), and networking (VPC) within a cloud architecture.

Skills

• Design and implement a simple VPC network on AWS, including subnets, security groups, and internet gateways.
• Upload, manage, and access data objects within S3 buckets, utilizing appropriate access control mechanisms.
• Provision and configure basic EC2 instances on AWS, including instance types, storage options, and security group settings.

Competencies

• Integrate AWS services with DevOps tools and workflows for automated provisioning, configuration management, and deployment of applications within the cloud.
• Design and deploy a scalable and secure cloud infrastructure for a simple application on AWS, considering factors like cost optimization and fault tolerance.
• Select and configure appropriate AWS services based on specific application requirements for compute, storage, and networking.

Computer Vision

75 hours | 3 ECTS

About

This course builds core computer vision skills used in modern robotics and automation. Learners will write programs to process and analyze images, implement feature extraction techniques, and apply deep learning models for object recognition.

Teachers

Ramkumar Manoharan

Mostafa Ahmed Asaad Abdelmegeed

Paawan Mukker

NEO CE ZHENG (LIANG CEZHENG)

Alaa Ahmed Mohamed Alshafai

Osama Mohammed Moustafa Hosameldeen

Jin-Hyuk Kwon

Juan Carlos Kuri Pinto

Mohammad Ehshan khan

Raymond Phan

Dwayne Lance Elahie

Sebastian Velasquez

Lamia Hassan Mohamed Zainelabedeen Taha

Intended learning outcomes

Knowledge

• • Identify and explain foundational concepts in artificial intelligence and machine learning, using appropriate terminology and examples.

• • Critically evaluate common design patterns and architectures in artificial intelligence and machine learning, including considerations for scalability and robustness.

• • Compare and contrast current tools and ecosystems used in artificial intelligence and machine learning, and articulate their appropriate use-cases.

• • Summarise the main algorithms, models, and frameworks used in artificial intelligence and machine learning, including techniques for object detection, object localization, object tracking, SLAM, recurrent neural networks, and attention mechanisms.

• • Describe ethical, legal, and societal implications arising from applied work in artificial intelligence and machine learning, including issues of bias, privacy, and transparency.

Skills

• • Execute professional project workflows when developing solutions in artificial intelligence and machine learning.

• • Construct, evaluate, and optimise models and systems relevant to artificial intelligence and machine learning, including deep learning–based computer vision pipelines.

• • Apply industry-standard tools and workflows to implement practical solutions in artificial intelligence and machine learning, demonstrating reproducible engineering practice.

• • Communicate technical results effectively to both technical and non-technical stakeholders, including visualisations and reports.

• • Integrate components and APIs to build end-to-end solutions in artificial intelligence and machine learning, including deployment and monitoring pipelines.

Competencies

• • Lead small cross-functional teams to plan and deliver artificial intelligence and machine learning projects that meet business or research objectives.

• • Strategically assess and select technologies and approaches in artificial intelligence and machine learning to align with organisational goals and constraints.

• • Demonstrate adaptive learning and continuous professional development to stay current with advances in artificial intelligence and machine learning.

• • Apply ethical reasoning and governance to guide decisions in artificial intelligence and machine learning–focused projects, ensuring fairness and compliance.

• • Manage project resources, timelines, and risks to deliver production-ready artificial intelligence and machine learning solutions.

Machine Learning Model Optimization

125 hours | 5 ECTS

About

This course provides advanced, hands-on training in optimizing machine learning models for performance and scalability. Learners will apply techniques such as quantization, pruning, profiling, low-rank compression, and knowledge distillation to both traditional ML models and large language models (LLMs). The program also covers hardware-efficient architectures using TensorRT and ONNX, enabling students to design fast inference pipelines and deploy highly optimized models for real-world applications.

Teachers

Moataz Mahmoud Soliman Ibrahim

Dheeraj Agrawal

Ahmed Sabry Muhammad Mahmoud Mazroua

Thiago Meireles Grabe

Ramkumar Manoharan

Sharad Jain

Shinto Theruvil Manuel

Abdelmonem mohamed abdelmonem rehab

Intended learning outcomes

Knowledge

• Identify and explain foundational concepts in model optimisation, including quantization, pruning, profiling, and performance–scalability trade-offs.
• Critically evaluate design patterns and architectures for efficient model deployment, considering resource constraints, scalability, and hardware acceleration.
• Compare and contrast current tools and ecosystems used in optimisation of machine learning and LLM models, and articulate their appropriate use-cases.
• Summarise the main techniques and frameworks used in model optimisation, including low-rank compression, knowledge distillation, TensorRT, and ONNX.
• Describe ethical, legal, and societal implications arising from applied work in model optimisation, including concerns related to fairness, transparency, and deployment reliability.

Skills

• Apply industry-standard tools and workflows to implement practical solutions in model optimisation, including quantization, pruning, profiling, and compression.
• Communicate technical results effectively to both technical and non-technical stakeholders, including performance metrics, optimisation trade-offs, and deployment considerations.
• Construct, evaluate, and optimise models and systems using techniques such as low-rank compression, knowledge distillation, hardware-accelerated inference, and pipeline optimisation.
• Integrate components and APIs to build end-to-end optimised model pipelines, including TensorRT, ONNX, and other deployment frameworks.
• Execute professional project workflows when developing and deploying optimised machine learning and LLM-based solutions.

Competencies

• Manage project resources, timelines, and risks to deliver production-ready optimised machine learning and LLM-based solutions.
• Lead small cross-functional teams to plan and deliver machine learning model optimization projects, including quantization, pruning, profiling, and compression workflows.
• Demonstrate adaptive learning and continuous professional development to stay current with advances in model compression, LLM optimisation, and hardware-accelerated inference.
• Apply ethical reasoning and governance to guide decisions in model optimisation projects, ensuring responsible handling of model behaviour, data usage, and deployment impacts.
• Strategically assess and select technologies and approaches in model optimisation, including compression methods, inference frameworks, and tools such as TensorRT and ONNX, to align with organisational goals.

Data Streaming

100 hours | 4 ECTS

About

This course teaches learners to process data in real time using modern data engineering tools. Students will build practical skills with Apache Spark, Kafka, Spark Streaming, and Kafka Streaming to develop fast, scalable, and reliable real-time data pipelines.

Teachers

Thiago Meireles Grabe

Anshul Rampal

Shilpa Bamboria

Lim Zhi Yuan

‪Moamen Mohamed Mohasseb Eltaieb‬‏

TCHUENKAM Ulrich

Intended learning outcomes

Knowledge

• • Describe ethical, legal, and societal implications arising from applied work in data engineering, including issues of bias, privacy, and transparency.

• • Identify and explain foundational concepts in data engineering, using appropriate terminology and examples.

• • Critically evaluate common design patterns and architectures in data engineering, including considerations for scalability and robustness in real-time systems.

• • Compare and contrast current tools and ecosystems used in data engineering, and articulate their appropriate use-cases.

• • Summarise the main algorithms, models, and frameworks used in data engineering, including stream processing with Apache Spark, Kafka, Faust, KSQL, and related ecosystems.

Skills

• • Execute professional project workflows when developing solutions in data engineering.

• • Construct, evaluate, and optimise real-time data streaming systems using modern frameworks such as Apache Spark, Kafka, Faust, and KSQL.

• • Communicate technical results effectively to both technical and non-technical stakeholders, including visualisations and reports.

• • Apply industry-standard tools and workflows to implement practical solutions in data engineering, demonstrating reproducible engineering practice.

• • Integrate components and APIs to build end-to-end data streaming pipelines, including deployment and monitoring processes.

Competencies

• • Strategically assess and select technologies and approaches in data engineering to align with organisational goals and constraints.

• • Lead small cross-functional teams to plan and deliver data engineering projects that meet business or research objectives.

• • Manage project resources, timelines, and risks to deliver production-ready data engineering solutions.

• • Demonstrate adaptive learning and continuous professional development to stay current with advances in data engineering.

• • Apply ethical reasoning and governance to guide decisions in data engineering–focused projects, ensuring fairness and compliance.

Marketing Analyst

100 hours | 4 ECTS

About

This course teaches learners to transform marketing data into actionable insights. Students will collect, analyze, and model data, then communicate findings using Excel and Tableau to support informed business and marketing decisions.

Teachers

Marwa Fawzy Qabeel Ibraheem

Elie Varesse Wanko Pohdie

Prateek Kailash Agarwal

Amir Hassanein Mohamed El Amir Mohamed

Gabriel Donato

Rodolfo Yoshii

Jean Paul Barddal

Prashant Kumar Lakhotia

Tarek Ashraf Mohamed Elhanafy Abdelmegid

Sahib Kalra

Jin-Hyuk Kwon

Engy Hassan Kamel Elbardissy

Salahaldin Hussein

Martina Maged William Ghali

RAHUL KUMAR

Rafael Castillo Alcibar

Intended learning outcomes

Knowledge

• Identify and explain foundational concepts in data science and analytics, using appropriate terminology and examples.
• Compare and contrast current tools and ecosystems used in data science and analytics, and articulate their appropriate use-cases.
• Summarise the main algorithms, models, and frameworks used in data science and analytics, including those relevant to marketing analytics and metrics.
• Critically evaluate common design patterns and architectures in data science and analytics, including considerations for scalability and robustness.
• Describe ethical, legal, and societal implications arising from applied work in data science and analytics, including issues of bias, privacy, and transparency.

Skills

• Integrate components and APIs to build end-to-end solutions in data science and analytics, including deployment and monitoring pipelines.
• Construct, evaluate, and optimise models and analyses relevant to data science and analytics, including marketing metrics, marketing channels, and media performance.
• Communicate technical results effectively to both technical and non-technical stakeholders, including visualisations and reports.
• Execute professional project workflows when developing solutions in data science and analytics.
• Apply industry-standard tools and workflows to implement practical solutions in data science and analytics, demonstrating reproducible engineering practice.

Competencies

• Manage project resources, timelines, and risks to deliver production-ready data science and analytics solutions.
• Demonstrate adaptive learning and continuous professional development to stay current with advances in data science and analytics.
• Strategically assess and select technologies and approaches in data science and analytics to align with organisational goals and constraints.
• Apply ethical reasoning and governance to guide decisions in data science and analytics–focused projects, ensuring fairness and compliance.
• Lead small cross-functional teams to plan and deliver data science and analytics projects that meet business or research objectives.

Business Leadership and Strategy

75 hours | 3 ECTS

About

This module examines how leaders can most effectively use the resources of their team members to achieve business outcomes. The module develops managerial and leadership competences, focussing on how key improvements in the general strategy and techniques of managing people can produce outcomes more significant than isolated improvements to employee performance.

The module provides students with concepts to support them across their careers as they continue to develop effective delegation, management strategy, and engagement with people inside of an organisation.

Teachers

Ken Adachi

Kinjal Doshi

Thiago Meireles Grabe

Abdelrhman Hazem Wahdan

Ramkumar Manoharan

Rujuta Nitesh Heda

Rajat Sharma

Islam Tarek Abdelrahman Ali Hassanein

Megha Jain

Wong Lip Soon

Abdulhakeem Omotolani Yaqoob

Yu Zeng

Shinto Theruvil Manuel

Shubham Chhabra

Amit Lathiya

Jihwan Min

Shashank Shekhar

Sarthak Gupta

Sebastian Velasquez

Katherine Walker

Rafael Castillo Alcibar

Intended learning outcomes

Knowledge

• Critical knowledge of business leadership strategy.
• Why improvements in the general strategy and techniques of managing people can produce outcomes more significant than isolated improvements to employee performance.
• The relevance of theories of management and concepts of delegation and performance tracking.
• Key strategies for effective delegation.
• Select topics for the advanced management of human resources.

Skills

• Communicate an in-depth domain-specific knowledge and understanding to business leadership and strategy.
• Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing.
• Creatively apply the theories learned in the module to develop critical and original solutions for the challenges of business leadership and strategy including: • Assessing the resources of a team to show how those resources could be used to achieve business outcomes. • Assessing the performance of a team to show whether they are on track to meet business goals.
• Autonomously gather material and organise it into a coherent, comprehensive presentation.

Competencies

• Efficiently manage interdisciplinary issues that arise when assessing human resources and delegating effectively to achieve business goals.
• Solve problems and be prepared to take leadership decisions related to business leadership and strategy
• Create synthetic contextualised discussions of key issues related to leadership and management strategy.
• Apply a professional and scholarly approach to research problems pertaining to the effective use of team resources and delegation of personnel.
• Demonstrate self-direction in research and originality in solutions for management and leadership.
• Act autonomously in identifying research problems and solutions related to business leadership and strategy.

Introduction to Self-Driving Cars

125 hours | 5 ECTS

About

This course blends C++, Python, and machine learning to build the core skills behind autonomous driving. Learners will transform complex math and code into motion by teaching vehicles how to perceive their environment, make decisions, and navigate safely and intelligently.

Teachers

Aseem Arora

Duong Hai Long

Khaled Gamaleldin Mohamed Saleh Zoheir

Abdelrahman Ahmad Saeed Abdelaziz

Juan Carlos Kuri Pinto

Zohaib Khawaja

Neha Verma

Intended learning outcomes

Knowledge

• Compare and contrast current tools and programming ecosystems used in autonomous systems, including C++, Python, and related libraries.
• Critically evaluate design patterns and architectures relevant to autonomous systems, considering scalability, robustness, and real-time constraints.
• Identify and explain foundational concepts in autonomous systems, including probability, perception, and decision-making.
• Summarise key algorithms, models, and frameworks used in self-driving applications, including computer vision and object localization.
• Describe ethical, legal, and societal implications of autonomous vehicle technologies, including safety, privacy, and transparency.

Skills

• Construct, evaluate, and optimise models and components relevant to perception, localization, and decision-making for self-driving applications.
• Communicate technical results effectively to both technical and non-technical stakeholders, including visualisations, simulations, and reports.
• Apply industry-standard tools and workflows to implement practical solutions in autonomous systems, demonstrating reproducible engineering practice.
• Integrate components and APIs to build end-to-end autonomous workflows, including computer vision pipelines and sensor-based decision systems.
• Execute professional project workflows when developing autonomous system solutions.

Competencies

• Manage project resources, timelines, and risks to deliver production-ready autonomous system solutions.
• Strategically assess and select technologies and approaches in autonomous systems to align with organisational goals and constraints.
• Demonstrate adaptive learning and continuous professional development to stay current with advances in autonomous systems and related technologies.
• Apply ethical reasoning and governance to guide decisions in autonomous systems projects, ensuring safety, fairness, and regulatory compliance.
• Lead small cross-functional teams to plan and deliver autonomous systems and robotics projects that meet business or research objectives.

iOS Development with SwiftUI and SwiftData

250 hours | 10 ECTS

About

This course develops the skills needed to build high-quality iOS applications from concept to launch. Learners will gain proficiency in Swift, UI design, data management, data integration, and project planning, enabling them to create polished, fully functional iOS apps.

Teachers

Fernando Augusto de Marins

Shubham Tripathi

Duong Hai Long

Martin Nabil Shafik Sorsok

Dipika Chadha

Abdul-Razak Bawa Yussif

Ayman Atallah

Megha Jain

Intended learning outcomes

Knowledge

• Summarise main algorithms, models, and frameworks used in software engineering / web & mobile and their practical trade-offs.
• Identify and explain foundational concepts in software engineering / web & mobile, using appropriate terminology and examples.
• Critically evaluate common design patterns and architectures in software engineering / web & mobile, including considerations for scalability and robustness.
• Compare and contrast current tools and ecosystems used in software engineering / web & mobile, and articulate their appropriate use-cases.
• Describe ethical, legal, and societal implications arising from applied work in software engineering / web & mobile, including issues of bias, privacy, and transparency.

Skills

• Execute professional project workflows when developing software engineering / web & mobile solutions.
• Apply industry-standard tools and workflows to implement practical solutions in software engineering / web & mobile, demonstrating reproducible engineering practice.
• Communicate technical results effectively to both technical and non-technical stakeholders, including visualisations and reports.
• Integrate components and APIs to build end-to-end solutions in software engineering / web & mobile, including network requests, authentication flows, and data handling with Swift and SwiftUI.
• Construct, evaluate, and optimise models/systems relevant to software engineering / web & mobile, using data-driven testing and performance metrics.

Competencies

• Demonstrate adaptive learning and continuous professional development to stay current with advances in software engineering / web & mobile.
• Lead small cross-functional teams to plan and deliver software engineering / web & mobile projects that meet business or research objectives.
• Apply ethical reasoning and governance to guide decisions in software engineering / web & mobile-focused projects, ensuring fairness and compliance.
• Manage project resources, timelines, and risks to deliver production-ready software engineering / web & mobile solutions.
• Strategically assess and select technologies and approaches in software engineering / web & mobile to align with organisational goals and constraints.

Data Science for Business Leaders

50 hours | 2 ECTS

About

This course equips business leaders with the strategic, human capital, and technical foundation knowledge needed to lead data science initiatives. Learners progress through core principles of data science, team building, data architecture, and risk management.

Teachers

Rashmi

Rajat Sharma

Wong Lip Soon

Yu Zeng

Intended learning outcomes

Knowledge

• Explain the core principles of data science and articulate the business case for adopting data-driven strategies within an organization.
• Identify key components of data and machine learning infrastructure, including architectural considerations and governance requirements.
• Describe the roles, skills, and structures required to build effective data science teams and foster a data-driven culture.

Skills

• Communicate data science strategy proposals clearly to stakeholders, incorporating stakeholder management and product management principles.
• Conduct project risk assessments and develop mitigation strategies for data science initiatives.
• Apply structured problem-solving methods to evaluate and prioritize data science use cases aligned with business objectives.

Competencies

• Design and present an actionable 100-day data science implementation plan that demonstrates readiness to deploy data-driven solutions at scale.
• Develop a comprehensive organizational data science strategy that integrates technical, human capital, and business requirements.
• Lead cross-functional data science initiatives by aligning team capabilities with strategic priorities and operational goals.

Front End Development

125 hours | 5 ECTS

About

This course builds upon the introductory JavaScript course to acquaint students of popular and modern frameworks to build the front end. We focus on three very popular frameworks/libraries in use: React.js, jQuery and AngularJS. We start with React.js, one of the most popular and advanced ones amongst the three. students learn various components and data flow to learn to architect real world front end using React.js. This would be achieved via multiple code examples and code-walkthroughs from scratch. We would also dive into React Native which is a cross platform Framework to build native mobile and smart-TV apps using JavaScript. This helps students to build applications for various platforms using only JavaScript. jQuery is one of the oldest and most widely used JavaScript libraries, which students cover in detail. Students specifically focus on how jQuery can simplify event handling, AJAX, HTML DOM tree manipulation and create CSS animations. We also provide a hands-on introduction to AngularJS to architect model-view-controller (MVC) based dynamic web pages.

Teachers

Ananthakrishna H S

Moataz Mahmoud Soliman Ibrahim

Mayur Raina

Alberto Ivo da Costa Vieira

Dinanath Pandey

Shubham Prakash

Dina tahoun

Ahmed Fouad Mohamed Farid Lotfy

Manmeet Singh

Simran

Ahmed Tarek Montaser

Mohamed Ameen Abdelhamid Allam

Ujjwal Sharma

UTKARSH SHUKLA

Ehab Hamdy Mohamed Mohamed Hussein

Satyajeet Sindhiyani

Intended learning outcomes

Knowledge

• Acquire knowledge of HTML5, CSS and Frameworks like Bootstrap 4
• Develop a specialised knowledge of key strategies related to Front end UI/UX development
• Critically assess the relevance of theories for business applications in the domain of technology
• Critically evaluate diverse scholarly views on Front end UI/UX development
• Develop a critical knowledge of Front end UI/UX development

Skills

• Apply an in-depth domain-specific knowledge and understanding to technology
• Creatively apply Front end UI/UX development applications to develop critical and original solutions for computational problems.
• Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
• Autonomously gather material and organise into a coherent problem sets or presentation

Competencies

• Efficiently manage interdisciplinary issues that arise in connection to Front end UI/UX development
• Demonstrate self-direction in research and originality in solutions developed for Front end UI/UX development
• Create synthetic contextualised discussions of key issues related to Front end UI/UX development
• Solve problems and be prepared to take leadership decisions related to the methods and principles of Front end UI/UX development
• Apply a professional and scholarly approach to research problems pertaining to Front end UI/UX development
• Act autonomously in identifying research problems and solutions related to Front end UI/UX developmen

Back End Development

125 hours | 5 ECTS

About

This is a foundational course on building server-side (or backend) applications using popular JavaScript runtime environments like Node.js. Students will learn event driven programming for building scalable backend for web applications. The module teaches various aspects of Node.js like setup, package manager, client- server programming and connecting to various databases and REST APIs. Most of these concepts would be covered in a hands-on manner with real world examples and applications built from scratch using Node.js on Linux servers. This course also provides an introduction to Linux server administration and scripting with special focus on web-development and networking. Students learn to use Linux monitoring tools (like Monit) to track the health of the servers. The module also provides an introduction to Express.js which is a popular light-weight framework for Node.js applications. Given the practical nature of this course, this would involve building actual website backends via assignments/projects for ecommerce, online learning and/or photo-sharing.

Teachers

Mayur Raina

Oscar Mauricio Forero Carrillo

Rachel Robin Joyce

Ahmed Fouad Mohamed Farid Lotfy

Shubham Saxena

Shubham Singh Kalyanwat

Daniel Ceragioli Abrão

Dániel Hadházi

Intended learning outcomes

Knowledge

• Critically assess the relevance of theories for business applications in the domain of technology
• Acquire knowledge of key aspects of Node.js like setup, package manager, client-server programming and connecting to various databases and REST
• Develop a critical knowledge of Back End Development
• Critically evaluate diverse scholarly views on Back End Development
• Develop a specialised knowledge of key strategies related to Back End Development

Skills

• Apply an in-depth domain-specific knowledge and understanding to Back End Development applications
• Autonomously gather material and organise it into coherent problem sets or presentations
• Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
• Creatively apply Back End Development tools to develop critical and original solutions for computational problems

Competencies

• Act autonomously in identifying research problems and solutions related to Back End Development
• Create synthetic contextualised discussions of key issues related to Back End Development
• Efficiently manage interdisciplinary issues that arise in connection to Back End Development
• Apply a professional and scholarly approach to research problems pertaining to Back End Development
• Demonstrate self-direction in research and originality in solutions developed for Back End Development
• Solve problems and be prepared to take leadership decisions related to the methods and principles of Back End Development

Business Analytics

125 hours | 5 ECTS

About

This course builds essential skills in data analysis and business analytics. Learners will use Excel, SQL, and Power BI to collect, clean, model, and visualize data, developing the ability to generate clear insights that support effective decision-making.

Teachers

Mohga Soliman Emam Hewashy

Marwa Fawzy Qabeel Ibraheem

Ken Adachi

Elie Varesse Wanko Pohdie

Prateek Kailash Agarwal

Oscar Mauricio Forero Carrillo

Ramkumar Manoharan

Rajat Sharma

Amir Hassanein Mohamed El Amir Mohamed

Rodolfo Yoshii

Luka Ayad Michael Gerges

Jean Paul Barddal

Swati Chandiramani

Sahib Kalra

Prateek Pushkar Agarawal

Nasim Banu

RAGHAVENDRA PRASAD SAVADA

Shubham Chhabra

Engy Hassan Kamel Elbardissy

Salahaldin Hussein

Sarthak Gupta

RAHUL KUMAR

Noha Wagdi Abdellatif Metwally Sheta

Balaji Murugan

Nilesh Kumar Srivastava

Satyajeet Sindhiyani

Rafael Castillo Alcibar

Intended learning outcomes

Knowledge

• Compare and contrast current tools and ecosystems used in data science and analytics, and articulate their appropriate use-cases.

• Describe ethical, legal, and societal implications arising from applied work in data science and analytics, including issues of bias, privacy, and transparency.

• Critically evaluate common design patterns and architectures in data science and analytics, including considerations for scalability and robustness.

• Identify and explain foundational concepts in data science and analytics, using appropriate terminology and examples.

• Summarise the main algorithms, models, and frameworks used in data science and analytics and their practical trade-offs.

Skills

• Construct, evaluate, and optimise models/systems relevant to data science and analytics, using data-driven testing and performance metrics.

• Execute professional project workflows when developing solutions in data science and analytics.

• Communicate business analytics results effectively to both technical and non-technical stakeholders, including visualisations and reports.

• Integrate components and APIs to build end-to-end solutions in data science and analytics, including deployment and monitoring pipelines.

• Apply industry-standard tools and workflows to implement practical solutions in data science and analytics, demonstrating reproducible engineering practice.

Competencies

• Manage project resources, timelines, and risks to deliver production-ready data science and analytics solutions.

• Apply ethical reasoning and governance to guide decisions in data science and analytics-focused projects, ensuring fairness and compliance.

• Strategically assess and select technologies and approaches in data science and analytics to align with organisational goals and constraints.

• Demonstrate adaptive learning and continuous professional development to stay current with advances in data science and analytics.

• Lead small cross-functional teams to plan and deliver data science and analytics projects that meet business or research objectives.

Azure Generative AI Engineer

75 hours | 3 ECTS

About

This course trains learners to build and deploy generative AI solutions on Azure using OpenAI models, GPT Vision, and DALL·E. Students will design RAG pipelines, engineer prompts, automate workflows, and integrate multimodal applications using Azure and OpenAI APIs.

Teachers

Ken Adachi

Alberto Ivo da Costa Vieira

Ramkumar Manoharan

Abdulhakeem Omotolani Yaqoob

Amit Lathiya

Shashank Shekhar

Zohaib Khawaja

Intended learning outcomes

Knowledge

• Summarise the main algorithms, models, and frameworks used in artificial intelligence and machine learning and their practical trade-offs.
• Critically evaluate common design patterns and architectures in artificial intelligence and machine learning, including considerations for scalability and robustness.
• Compare and contrast current tools and ecosystems used in artificial intelligence and machine learning, and articulate their appropriate use-cases.
• Identify and explain foundational concepts in artificial intelligence and machine learning, using appropriate terminology and examples.
• Describe ethical, legal, and societal implications arising from applied work in artificial intelligence and machine learning, including issues of bias, privacy, and transparency.

Skills

• Apply industry-standard tools and workflows to implement practical solutions in artificial intelligence and machine learning, demonstrating reproducible engineering practice.
• Construct, evaluate, and optimise models and systems relevant to artificial intelligence and machine learning, using data-driven testing and performance metrics.
• Integrate components and APIs to build end-to-end solutions in artificial intelligence and machine learning, including deployment and monitoring pipelines.
• Communicate technical results effectively to both technical and non-technical stakeholders, including visualisations and reports.
• Execute professional project workflows when developing solutions in artificial intelligence and machine learning.

Competencies

• Manage project resources, timelines, and risks to deliver production-ready artificial intelligence and machine learning solutions.
• Demonstrate adaptive learning and continuous professional development to stay current with advances in artificial intelligence and machine learning.
• Lead small cross-functional teams to plan and deliver artificial intelligence and machine learning projects that meet business or research objectives.
• Strategically assess and select technologies and approaches in artificial intelligence and machine learning to align with organisational goals and constraints.
• Apply ethical reasoning and governance to guide decisions in artificial intelligence and machine learning-focused projects, ensuring fairness and compliance.

AI Engineering with Claude

100 hours | 4 ECTS

About

This course prepares developers to build and ship production-grade AI agent systems using Claude, the Claude Agent SDK, the Model Context Protocol (MCP), and Claude Code. The course goes beyond simple demos to engineer the "harness" that runs an agent in production — controlling the agentic loop, governing MCP tools, and enforcing rules in code through bounded autonomy and human-in-the-loop guardrails. Learners build systems where multiple specialised agents collaborate and hand off tasks, and instrument these systems with observability and evaluation pipelines that surface silent failures, test reasoning quality, and catch drift before it reaches customers. Through hands-on work with the Claude Agent SDK and MCP architecture, learners design, secure, and govern tools that connect agents to external data and systems, ultimately building real conversational agents capable of completing tasks autonomously. Portfolio projects are anchored in real industry scenarios from insurance, retail, financial services, and manufacturing, including an MCP-powered "Agentic Analyst" and an enterprise multi-agent code-review orchestrator.

Teachers

Ken Adachi

Abdul-Razak Bawa Yussif

Shinto Theruvil Manuel

Amit Lathiya

Rafael Castillo Alcibar

Intended learning outcomes

Knowledge

• Has comprehensive knowledge and understanding of agentic AI system design, founded upon and extending software engineering knowledge
• Uses specialised, multi-disciplinary theoretical and practical knowledge of the agentic loop, the Model Context Protocol (MCP), and the Claude Agent SDK, some of which is at the forefront of the field, forming the basis of originality in engineering production-grade AI agent systems.

Skills

• Can communicate architectural decisions and evaluation findings to specialist and non-specialist audiences clearly and unambiguously, reaching conclusions that are the outcome of research, experimentation, or applied engineering practice;
• Performs critical evaluations and analysis with incomplete or limited information to diagnose silent failures and unpredictable behaviour in agentic systems operating in new or unfamiliar environments, and produces original solutions;
• Demonstrates specialised, multi-disciplinary knowledge that includes reflecting on the social and ethical responsibilities linked to deploying autonomous AI agents, including governance, safety, and human-in-the-loop accountability;
• Develops new skills in response to emerging agentic AI knowledge and techniques (e.g. multi-agent orchestration, tool governance, agent observability), and demonstrates leadership and innovation in complex and unpredictable production contexts.

Competencies

• Manages agentic systems, multi-agent orchestration, and cross-functional AI projects, demonstrating the ability to respond to the fast-changing agentic AI landscape;

• Creates a research-based diagnosis of agent system failures by integrating knowledge from new or interdisciplinary fields (software engineering, AI evaluation, risk governance), and makes judgements with incomplete or limited information;

• Demonstrates autonomy in the direction of learning and a high level of understanding of the processes involved in building, evaluating, and governing production AI agents;

• Has the learning skills to allow continuation of study in agentic AI engineering in a manner that may be largely self-directed or autonomous, applying bounded autonomy and governance principles to novel, real-world industry scenarios (insurance, retail, financial services, manufacturing).

Data Analysis and Visualization with Power BI

175 hours | 7 ECTS

About

This course equips learners with in-demand skills in data pre-processing, visualization, and analysis using Power BI. Students gain hands-on experience with Data transformation, Power Query, DAX, and Power BI Report Design while developing strong Data visualization design and Power BI Report Customization capabilities to build effective analytical dashboards.

Teachers

Thiago Meireles Grabe

Oscar Mauricio Forero Carrillo

Amir Hassanein Mohamed El Amir Mohamed

Rodolfo Yoshii

Shubham Chhabra

Salahaldin Hussein

Intended learning outcomes

Knowledge

• Compare analytical methods involving DAX, Data visualization design, and Power BI Report Design.

• Identify concepts in Data visualization design, Power Query, Data transformation, and Power BI Report Customization.

• Summarise core elements of DAX, Power BI Report Design, and Data transformation for analytical reporting.

• Critically evaluate Data visualization design choices and DAX models used within Power BI Report Customization.

• Describe considerations in Data transformation, Data fluency, and Power Query for creating accurate reports.

Skills

• Communicate insights clearly through Power BI Report Design using Data fluency, Data transformation, and DAX.

• Build effective visuals using Data visualization design, Power BI Report Design, and Data fluency principles.

• Execute professional reporting processes using Power BI Report Customization, Data visualization design, and DAX.

• Integrate DAX, Power Query, and Data transformation into complete Power BI Report Design workflows.

• Apply Data transformation, Power Query, and DAX to create analytical dashboards with Power BI Report Customization.

Competencies

• Demonstrate autonomous learning when applying DAX, Data fluency, and Data visualization design in Power BI workflows.

• Manage analytical processes using Data transformation, Power Query, and DAX within structured Power BI Report Design tasks.

• Lead analytical tasks by applying Data transformation, Power Query, and Power BI Report Customization in Power BI Report Design.

• Apply ethical judgement when creating visual insights using Power BI Report Design, Data transformation, and Power Query.

• Evaluate analytical approaches using DAX, Data visualization design, and Power BI Report Customization to support decision making.

C++

100 hours | 4 ECTS

About

This course builds practical, modern C++ skills for high-performance software development. Learners will work with memory management, concurrency, and advanced programming techniques used across robotics, autonomous systems, web browsers, media platforms, servers, and video games.

Teachers

Upendra Singh

Deep Chandresh Doshi

Munish Goyal

Dinanath Pandey

Shubham Kandwal

Mohamed Abdulaal Morsi Bakr

Gilles R. Kepnang

Juan Carlos Kuri Pinto

Shubham Chhabra

Patel Aksharkumar Babulal

Satyajeet Sindhiyani

Intended learning outcomes

Knowledge

• Summarise key programming models, memory management techniques, and concurrency approaches used in modern C++.
• Compare and contrast tools, compilers, and development ecosystems used in C++ programming, articulating their appropriate use-cases.
• Identify and explain foundational concepts in software engineering and C++ programming using appropriate terminology and examples.
• Critically evaluate design patterns and software architectures in C++, including considerations for performance, efficiency, and scalability.
• Describe ethical, legal, and societal implications arising from applied work in systems programming, including issues of safety, reliability, and data integrity.

Skills

• Execute professional project workflows when developing C++ software.
• Construct, evaluate, and optimise C++ programs using threading, dynamic memory allocation, and modern semantics such as copy and move operations.
• Communicate technical results effectively to both technical and non-technical stakeholders, including code reviews and technical reports.
• Integrate components, libraries, and APIs to build end-to-end C++ applications, including testing, debugging, and performance tuning.
• Apply industry-standard tools and workflows to implement practical C++ software solutions, demonstrating reproducible engineering practice.

Competencies

• Demonstrate adaptive learning and continuous professional development to stay current with advances in software engineering and systems programming.
• Strategically assess and select technologies and approaches in software engineering to align with organisational goals and system constraints.
• Apply ethical reasoning and governance to guide decisions in software-focused projects, ensuring safety, reliability, and compliance.
• Manage project resources, timelines, and risks to deliver production-ready software engineering solutions.
• Lead small cross-functional teams to plan and deliver software engineering projects that meet business or research objectives.

Android Kotlin Developer

125 hours | 5 ECTS

About

This course equips learners with advanced skills in Kotlin-based Android development. Students will build feature-rich mobile apps using Firebase, testing frameworks, animations, custom views, notifications, RecyclerView patterns, and Google Maps. Guided by insights from leading Android engineers, the program emphasizes creating polished, high-performance mobile experiences aligned with modern Android standards.

Teachers

AHMED ATEF AHMED ABD RABOUH

Ilia Sharipov

Ahmed Tarek Montaser

Marwa Emad Mohammed

SHAMSELDEN MOHAMED MAHMOUD KESHK

Mahmoud Assaf

Ehab Hamdy Mohamed Mohamed Hussein

Satyajeet Sindhiyani

Intended learning outcomes

Knowledge

• • Critically evaluate design patterns and architectures in Android applications, considering scalability, app performance, user experience, and lifecycle management.

• • Summarise the main frameworks and tools used in modern Android development, including Firebase, Google Maps integration, testing frameworks, and animation libraries.

• • Compare and contrast current tools and ecosystems used in Android and Kotlin-based development, and articulate their appropriate use-cases for enterprise and consumer apps.

• • Describe ethical, legal, and societal implications arising from mobile application development, including privacy, data permissions, and responsible location access.

• • Identify and explain foundational concepts in Android development, including the activity lifecycle, RecyclerView, notifications, custom views, and Kotlin programming constructs.

Skills

• • Execute professional project workflows when developing and deploying Android applications.

• • Communicate technical results effectively to both technical and non-technical stakeholders, including app behavior summaries, UI/UX documentation, and testing reports.

• • Construct, evaluate, and optimise Android applications using RecyclerView, custom views, notifications, lifecycle-aware components, and Google Maps integrations.

• • Apply industry-standard tools and workflows to implement practical Android solutions using Kotlin, Firebase, testing frameworks, and UI components.

• • Integrate components and APIs to build end-to-end Android workflows, including cloud services, location features, app architecture patterns, and animation-driven UI.

Competencies

• • Lead small cross-functional teams to plan and deliver Kotlin-based Android development projects involving mobile UI design, lifecycle management, and cloud-enabled features.

• • Strategically assess and select technologies and approaches in Android development to align with organisational goals, including UI components, lifecycle structures, and mapping services.

• • Apply ethical reasoning and governance to guide decisions in mobile development projects, ensuring privacy, responsible data handling, and secure mobile interactions.

• • Demonstrate adaptive learning and continuous professional development to stay current with advances in Android development, Kotlin language features, Firebase, and mobile testing frameworks.

• • Manage project resources, timelines, and risks to deliver production-ready Android mobile applications using modern tools and frameworks.

Computational Intelligence

125 hours | 5 ECTS

About

This course is aimed at providing students with an in-depth understanding of the techniques and algorithms that enable intelligent behaviour in computational systems. This course covers a wide array of topics, including neural networks, fuzzy logic, evolutionary computation, and swarm intelligence. Students will explore how these methods can be applied to solve complex problems in optimization, pattern recognition, and adaptive systems, emphasising both theoretical foundations and practical implementations.

Through a combination of lectures, hands-on projects, and case studies, students will gain experience in designing and applying computational intelligence algorithms to real-world scenarios. They will learn to develop adaptive systems that can perform tasks such as classification, prediction, and decision-making with high accuracy and efficiency. By the end of the course, students will be proficient in using computational intelligence techniques to create innovative AI solutions, positioning themselves for advanced roles in research and industry where intelligent systems are pivotal.

Teachers

Aseem Arora

Ahmed Sabry Muhammad Mahmoud Mazroua

Thiago Meireles Grabe

Ramkumar Manoharan

Robinson Jose Vasquez Ferrer

Deepak Sharma

Sudhakar Goyal

Intended learning outcomes

Knowledge

• Understand the differences between traditional AI and computational intelligence approaches.

• Analyse various computational intelligence paradigms and their use cases identifying their suitability for solving specific problems.

• Define and explain key concepts such as fuzzy logic, genetic algorithms, and neural networks.

Skills

• Assess the accuracy, efficiency, and robustness of different computational intelligence models by applying performance metrics and comparing results.

• Develop and optimise models using computational intelligence techniques.

• Apply computational intelligence algorithms to real-world problems.

Competencies

• Critically evaluate the ethical implications of computational intelligence in decision-making systems.

• Create systems that integrate multiple computational intelligence techniques to address complex, multidisciplinary problems.

• Collaborate on interdisciplinary projects involving computational intelligence demonstrating effective communication and teamwork skills.

AWS Cloud Architect

100 hours | 4 ECTS

About

This course provides a comprehensive pathway to mastering AWS cloud architecture. Learners will design scalable solutions, optimize performance, manage multi-account environments, and modernize systems through hands-on, real-world projects.

Teachers

Ken Adachi

Elie Varesse Wanko Pohdie

Munish Goyal

Dhruv Kinger

Tanuj Nagpal

Shubham Kandwal

Sepideh SaebiMoghaddam

Shubham Saxena

Zhaowei Wang

Bavithra Duraisamy

Shinto Theruvil Manuel

Ahmed Abdelazeim Omar

Abdel-Aziz BINGUITCHA-FARE

Dániel Hadházi

UTKARSH SHUKLA

Intended learning outcomes

Knowledge

• Critically evaluate common design patterns and architectures in cloud systems, including considerations for scalability, performance, and resilience.
• Summarise the main architectural models, frameworks, and services used in cloud engineering, including serverless computing and infrastructure as code.
• Identify and explain foundational concepts in cloud engineering and architecture, using appropriate terminology and examples.
• Describe ethical, legal, and societal implications arising from cloud adoption, including issues of data privacy, compliance, and security.
• Compare and contrast current AWS tools and ecosystems used in cloud architecture, and articulate their appropriate use-cases.

Skills

• Apply industry-standard tools and workflows to implement practical solutions in cloud engineering and architecture, demonstrating reproducible and secure engineering practice.
• Communicate technical results effectively to both technical and non-technical stakeholders, including visualisations, architectural diagrams, and reports.
• Execute professional project workflows when developing cloud-based solutions.
• Construct, evaluate, and optimise cloud architectures relevant to AWS environments, using performance metrics and cost management strategies.
• Integrate components and services to build end-to-end cloud solutions on AWS, including serverless, multi-account setups, and migration pipelines.

Competencies

• Lead small cross-functional teams to plan and deliver cloud engineering and architecture projects that meet business or organisational objectives.
• Apply ethical reasoning and governance to guide decisions in cloud-focused projects, ensuring compliance, security, and responsible use.
• Demonstrate adaptive learning and continuous professional development to stay current with advances in cloud engineering and architecture.
• Manage project resources, timelines, and risks to deliver production-ready cloud solutions.
• Strategically assess and select cloud technologies and architectural approaches to align with organisational goals and constraints.

Data Engineering

125 hours | 5 ECTS

About

Data is the fuel driving all major organisations. This course helps you understand how to process data at scale. From understanding the fundamentals of distributed processing to designing data warehousing and writing ETL (Extract Transform Load) pipelines to process batch and streaming data. Students will learn a comprehensive view of the complete Data Engineering lifecycle.

Teachers

SURVESH SATISH CHAUHAN

Ken Adachi

Akhil Kumar

Thiago Meireles Grabe

Oscar Mauricio Forero Carrillo

Anshul Rampal

Rajat Sharma

Shubham Saxena

Shubham Chhabra

Ujjwal Sharma

Mostafa ElSheikh

Intended learning outcomes

Knowledge

• Critically assess the relevance of theories of data modelling for efficient pipeline creation.
• Critically evaluate diverse scholarly views on best practices in developing data-intensive applications.
• Acquire knowledge of various methods for warehousing data.
• Develop a specialised knowledge of standard tools for data processing, such as Apache Kafka, Airflow, and Spark (with PySpark), and the Hadoop Ecosystem.
• Develop a critical understanding of data engineering.

Skills

• Autonomously gather material and organise it into a coherent presentation or essay
• Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
• Apply an in-depth domain-specific knowledge and understanding of orchestrating complete ETL pipelines.
• Creatively apply various visual and written methods for dashboarding data with Grafana/Tableau.

Competencies

• Solve problems and be prepared to take leadership decisions related to developing pipelines to handle massive datasets for engineering purposes.
• Create synthetic contextualised discussions of key issues related to the data engineering lifecycle.
• Apply a professional and scholarly approach to research problems pertaining to data warehousing and modelling.
• Act autonomously in identifying research problems and solutions related to developing for data at scale.
• Demonstrate self-direction in research and originality in creating advanced SQL queries.
• Efficiently manage interdisciplinary issues that arise in connection to developing cloud solutions for data engineering problems.

Blockchain Developer: Foundations

125 hours | 5 ECTS

About

This course provides a practical introduction to blockchain technology, covering core concepts such as networks, wallets, and consensus mechanisms. Students will develop hands-on skills in Solidity, smart contract design patterns, dApp interactions, and Hardhat-based testing. Through real-world projects—including blockchain use-case evaluation and building a collateralized loan smart contract—learners gain the technical foundation to design and deploy secure, functional blockchain solutions.

Teachers

Oscar Mauricio Forero Carrillo

Sepideh SaebiMoghaddam

Shubham Saxena

Daniel Ceragioli Abrão

Osama Mohammed Moustafa Hosameldeen

Sarthak Gupta

Satyajeet Sindhiyani

Intended learning outcomes

Knowledge

• Identify and explain foundational concepts in blockchain, distributed ledgers, and decentralised architectures using appropriate terminology.

• Compare and contrast blockchain tools, ecosystems, and development frameworks, articulating their appropriate use-cases.

• Describe ethical, legal, and societal implications of blockchain applications, including privacy, transparency, and responsible decentralisation.

• Critically evaluate blockchain design patterns, network architectures, and security considerations, including scalability and robustness.

• Summarise key algorithms, models, and frameworks used in blockchain systems, including consensus mechanisms and smart contract platforms.

Skills

• Communicate technical blockchain concepts and results effectively to both technical and non-technical stakeholders.
• Apply industry-standard tools (Hardhat, Solidity, testnets) and workflows to implement practical blockchain solutions.
• Integrate blockchain components, APIs, and on-chain/off-chain interactions to build end-to-end decentralised applications.
• Construct, evaluate, and optimise smart contracts and blockchain systems using data-driven testing and performance metrics.
• Execute professional project workflows when developing blockchain solutions.

Competencies

• Lead small cross-functional teams to plan and deliver blockchain projects that meet business or organisational objectives.
• Demonstrate adaptive learning and continuous professional development to stay current with advances in blockchain and distributed systems.
• Manage project resources, timelines, and risks to deliver production-ready blockchain solutions, including smart contract deployments.
• Apply ethical reasoning and governance to guide decisions in blockchain-focused projects, ensuring fairness, security, and compliance.
• Strategically assess and select blockchain technologies, architectures, and approaches that align with organisational goals and constraints.

3 - Capstone

750 hours | 30 ECTS

Capstone: Advanced Applied Computer Science

750 hours | 30 ECTS

About

Advanced Applied Computer Science is a capstone project, an end-to-end deployable solution to a real-world computational problem that students build in the last phase of the programme. Its objective is to help students rigorously solve a technically challenging problem where they would apply all of the concepts, techniques and tools learned in the programme. Students typically pick a problem from their specialisation after discussing it with the course instructor(s). Students also have the option of working on a real-world problem in their company/organization/institution. They can be mentored by an expert supervisor from their organization along with an academic supervisor from Woolf. All external expert-supervisors and projects need to be approved by the instructor(s) to ensure that the project is technically challenging and the solution being built is rigorous and of high quality. Students start with identifying a technically challenging problem. Once approved by the instructor(s), they start the literature survey to read research papers and technical reports of prior related work. Then, they build the system design and write a design document to solve the problem. This would be followed by designing and implementing individual modules and testing them. This would be followed by deploying the solution and making it available to end users while satisfying the problem’s real-world constraints and objectives. Students then document their work into a detailed technical report.

Teachers

Moataz Mahmoud Soliman Ibrahim

Akhil Kumar

Thiago Meireles Grabe

Deepak Sharma

Amit Lathiya

Mohammad Ehshan khan

Ahmed Abdelazeim Omar

Abhishek Mann

Balaji Murugan

Intended learning outcomes

Knowledge

• Acquire knowledge of an end-to-end deployable solution to a real-world computational problem
• Develop a specialised knowledge of key strategies related to modern computational applications
• Develop a critical knowledge of modern computational applications
• Critically evaluate diverse scholarly views on modern computational applications
• Critically assess the relevance of theories for business applications in the domain of technology

Skills

• Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
• Creatively apply computational applications to develop critical and original solutions for computational problems
• Apply an in-depth domain-specific knowledge and understanding of modern day computational applications
• Autonomously gather material and organise it into coherent problem sets or presentations

Competencies

• Demonstrate self-direction in research and originality in solutions developed for robust and reliable systems using interacting modules
• Act autonomously in identifying research problems and solutions related to modern computational tools and methods
• Apply a professional and scholarly approach to research problems pertaining to real-world modern computational complexities
• Solve problems and be prepared to take leadership decisions related to the methods and principles of modern computational applications
• Efficiently manage interdisciplinary issues that arise in connection to modern day computational methods and principles
• Create synthetic contextualised discussions of key issues related to real-world computational problems

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