Undergraduate Diploma in Computer Science

Fully Online
12 months
1500 hours | 60 ECTS
Degree
Scaler Neovarsity
Accreditation:
EQF5

About

The course teaches students comprehensive and specialized subjects in computer science; it develops skills in critical thinking and strategic planning for changing and fast-paced environments, including technological and operational analysis; and it develops competences in leadership, including autonomous decision-making, and communication with team members, stakeholders, and other members of a business.

Supporting your global mobility
Supporting your global mobility

Global Recognition

Woolf degrees align with major international qualification frameworks, ensuring global recognition and comparability. Earn your degree in the most widely recognized accreditation system in the world.

Learn More About Degree Mobility

Our accreditation through the Malta Further and Higher Education Authority (MFHEA) provides a solid foundation for credential recognition worldwide.

Success stories
Success stories

How students have found success through Woolf

"As a working parent, I needed something flexible and manageable. Woolf’s structure fit me perfectly. I was nervous at first, balancing work, parenting, and midnight classes, but the support, resources, and sense of community kept me going."
Andreia Caroll
Clinical Research Nurse
“Woolf and Scaler’s hands-on Master’s program gave me the practical skills and confidence I was missing after my undergraduate degree. Real projects, professional tools, and mentorship transformed how I think, build, and solve problems — leading me to a career as a Software Engineer.”
Bhavya Dhiman
Master’s in Computer Science
"Woolf provided me flexibility, a strong community, and high quality education. It really broadened my perspective and significantly improved my communication skills. I graduated not just more knowledgeable, but also more confident and well-rounded."
Brian Etemesi
Software Engineer
“Woolf’s flexible, accredited program gave me structure, community, and the confidence to grow. From landing my dream internship to winning a hackathon, Woolf opened doors and shaped both my career and mindset.”
Dominion Yusuf
Higher Diploma in Computer Science
"As a working parent, I needed something flexible and manageable. Woolf’s structure fit me perfectly. I was nervous at first, balancing work, parenting, and midnight classes, but the support, resources, and sense of community kept me going."
Andreia Caroll
Clinical Research Nurse
“Woolf and Scaler’s hands-on Master’s program gave me the practical skills and confidence I was missing after my undergraduate degree. Real projects, professional tools, and mentorship transformed how I think, build, and solve problems — leading me to a career as a Software Engineer.”
Bhavya Dhiman
Master’s in Computer Science
"Woolf provided me flexibility, a strong community, and high quality education. It really broadened my perspective and significantly improved my communication skills. I graduated not just more knowledgeable, but also more confident and well-rounded."
Brian Etemesi
Software Engineer
“Woolf’s flexible, accredited program gave me structure, community, and the confidence to grow. From landing my dream internship to winning a hackathon, Woolf opened doors and shaped both my career and mindset.”
Dominion Yusuf
Higher Diploma in Computer Science
- Students demonstrate some application of theoretical and practical knowledge in responding to problems. - Students formulate their ideas in clearly structured conventional formats and use appropriate evidence to support their claims. - Students will monitor, evaluate, and adjust their own learning needs in order to succeed as independent learners. - Students will also collect and analyse data to respond to both well-defined practical problems and well-specified abstract problems.

Course Structure

Emerging Technologies in AI
75 hours | 3 ECTS

About

Through the course, students will recognize emerging technologies in AI, describe their potential impact on society and industry, and discuss their ethical and social implications. By the end of the module, students will have gained a comprehensive understanding of emerging technologies in AI and their impact, preparing them to make informed decisions about the adoption and development of AI technologies in their future roles.

Teachers

Priti Mondal
Priti Mondal
Piyali Mondal Amitava Mondal
Piyali Mondal Amitava Mondal
Anupriya J
Anupriya J

Intended learning outcomes

Knowledge
  • Learn potential future developments in AI and their anticipated effects on society and industry.
  • Evaluate the implications of AI technologies on different industries.
  • Describe how emerging AI technologies can impact societal norms, ethics, and privacy.
Skills
  • Analyze how these technologies are currently being applied in various fields.
  • Identify and describe key emerging technologies in AI, such as deep learning and natural language processing.
  • Apply knowledge of these technologies to solve practical problems in AI.
Competencies
  • Monitor and assess the ethical and social implications of AI technology adoption in professional environments.
  • Analyze the processes involved in developing and implementing AI solutions.
  • Evaluate different strategies for adopting AI technologies in professional settings.
Optimizing Your Learning
75 hours | 3 ECTS

About

Optimizing Your Learning aims to transform incoming first year students into effective and empowered self-directed learners. In the modern world, long-term academic, professional, and personal success is driven by the ability of individuals to take control of their learning. Therefore, this course helps students to develop the knowledge, skills, and mindsets necessary to take ownership of their learning and build their self-efficacy. During the course, students will develop competence in skills that are most critical for effective self-directed and self-regulated learning (i.e. self-management, self-monitoring, and self-modification), while also learning how to use learning strategies to maximize their overall learning efficiency and efficacy. They will also utilize the Emotional Intelligence framework to explore their identity, self-image, motivation, and self-regulation skills, to support their development as self-directed learners. The course culminates in the creation of a personal learning charter that will help guide students in their learning throughout their undergraduate studies, which can also be applied to their learning activities in other realms of their lives.

Teachers

Priti Mondal
Priti Mondal
Noor Un Nisa Ali Nawaz
Noor Un Nisa Ali Nawaz
Anupriya J
Anupriya J
Anesh Jayantilal Soni
Anesh Jayantilal Soni

Intended learning outcomes

Knowledge
  • Cultivate strategic and creative responses in the search for solutions to well-defined concrete and abstract problems related to self-awareness.
  • Have knowledge of self-directed learning and study-patterns, demonstrated by creating a personal learning charter that will help guide students in their learning throughout their undergraduate studies .
  • Make judgments based on knowledge of the rules and conventions for the proper use of self-awareness, and demonstrate knowledge of the social and ethical issues relevant to self-directed learning.
Skills
  • Evaluates their own learning and identifies the learning deficits to address in further learning.
  • Communicate ideas in a well-structured, coherent format, following appropriate conventions in the field of technology.
  • Ability to apply theoretical and practical knowledge for the purpose of attaining long-term academic, professional, and personal success.
  • Can select appropriate evidence when formulated responses to well-defined concrete and abstract problems of personal career and education planning and success.
Competencies
  • Display creativity and initiative in carrying out self-directed learning.
  • Possess the academic competences to undertake further studies in emotional competence with a degree of autonomy.
  • Monitor and review their own performance and the performance of others; where appropriate collaboratively train others in the correct approach to develop a reflective practice to support deep learning.
  • Independently manage external perceptions that require techniques of self-reflection and self-evaluation.
Communicating for Success
75 hours | 3 ECTS

About

Communicating for Success supports students in developing communication skills that are essential for success in their personal and professional lives. The course will focus on close reading, written communication, verbal communication, and non-verbal communication skills. An emphasis will be placed on weekly submissions, and peer and instructor feedback, to allow students to practice and improve their skills. Students will learn how to effectively read and analyze texts as a precursor to developing their own written communication skills. They will then practice crafting clear communications by learning about topics such as writing structure and organization, grammar, audience awareness, and the iterative writing process. Next, students move on to verbal communication, and will learn how to confidently and skillfully deliver effective oral presentations. Finally, students will learn about the impact of non-verbal communication on how their messages are received. The course will culminate in a project that will require students to develop and implement a strategy for communicating a technical topic to a non-technical audience.

Teachers

Rajnarayan Krishnan
Rajnarayan Krishnan
Rekha Shray Shewakramani
Rekha Shray Shewakramani
Priti Mondal
Priti Mondal
Harshini Esther
Harshini Esther
Piyali Mondal Amitava Mondal
Piyali Mondal Amitava Mondal

Intended learning outcomes

Knowledge
  • Understand writing structure and organization, grammar, the role of audience awareness, and the iterative writing process, demonstrated by delivering effective written and oral presentations.
  • Cultivate close reading skills, written communication skills, verbal communication skills, and non-verbal communication skills.
  • Make judgments based on knowledge of the rules and conventions for the proper forms of communication, and demonstrate knowledge of the social and ethical issues relevant to communication.
Skills
  • Evaluates their own learning and identifies the learning deficits to address in further learning.
  • Having the ability to choose appropriate evidence when formulating responses to well-defined concrete and abstract problems of communicating technology to a non-technical audience.
  • Communicate ideas in a well-structured, coherent format, following appropriate conventions in the field of communication.
  • Cultivate close reading skills, written communication skills, verbal communication skills, and non-verbal communication skills.
Competencies
  • Independently manage a project requiring implementing a strategy for communicating a technical topic to a non-technical audience.
  • Possess the academic competences to undertake further studies in communication with a degree of autonomy.
  • Display creativity and initiative in carrying out complex ideas and arguments, and distill them in their components, assumptions, and evidence.
  • Monitor and review their own performance when seeking to craft clear communications.
Fundamentals of AI and ML
150 hours | 6 ECTS

About

This module introduces the fundamentals of Artificial Intelligence (AI) and Machine Learning (ML). Students will learn the definition of AI and ML, their evolution, and their applications in various fields. They will also explore the different types of machine learning algorithms, including supervised, unsupervised, and reinforcement learning. Through hands-on exercises and case studies, students will gain practical knowledge and experience in applying machine learning algorithms to real-world problems.Moreover, this module covers the principles of selecting the appropriate machine learning algorithm for a given problem. Students will learn about the factors that influence algorithm selection, such as data type, problem complexity, and performance requirements. They will also explore the principles of model training, validation, and testing, and gain practical knowledge and experience in evaluating machine learning models. By the end of this module, students will have a thorough understanding of AI and ML, be able to identify different types of machine learning algorithms, and select the appropriate algorithm for a given problem.

Teachers

Priti Mondal
Priti Mondal
Maria Monica
Maria Monica
Anupriya J
Anupriya J

Intended learning outcomes

Knowledge
  • Learn the importance of responsible AI development and the potential consequences of AI deployment in various sectors.
  • Understand key concepts such as classification, regression.
  • Gain a thorough understanding of the foundational principles of AI and ML, including the difference between AI, ML, and deep learning, and their respective roles in modern technology.
Skills
  • Learn to train, validate, and test AI and ML models, using metrics such as accuracy, precision, recall, F1-score, and confusion matrices.
  • Develop the ability to design, implement, and evaluate basic artificial intelligence (AI) and machine learning (ML) algorithms
  • Acquire skills in collecting, cleaning, and preprocessing datasets for AI and ML applications, including techniques for handling missing data, feature scaling, and data normalization.
Competencies
  • Develop the competency to innovate by adapting existing AI and ML techniques to new problems or by combining multiple techniques to achieve better results.
  • Exhibit the ability to critically evaluate AI and ML models, identifying their strengths and limitations.
  • Competently design and execute end-to-end AI and ML projects, from problem definition and data collection to model deployment and performance evaluation.
Web Application Development
150 hours | 6 ECTS

About

This course builds on Web Foundations, and provides a comprehensive introduction to client and server-side development for the web. In this project-based course, students will work independently to build a web application, and progressively apply new knowledge to their application. Students deepen their knowledge of HTML and learn advanced CSS, including how to use CSS variables and modern frameworks for motion and interaction. They learn about accessible web design, and how to create websites and apps that work well on mobile devices, and that support use of assistive technologies like screen readers. Students will build the front-end of a web application using HTML, CSS and JavaScript then write a supporting back-end using either a JavaScript or Python framework. In doing so, they will demonstrate knowledge of the request-response structure, database management, and JSON-based APIs. Students will also apply technical communication skills by writing technical specs, drafting architecture diagrams, and documenting APIs.

Teachers

Priti Mondal
Priti Mondal
Piyali Mondal Amitava Mondal
Piyali Mondal Amitava Mondal
Anupriya J
Anupriya J

Intended learning outcomes

Knowledge
  • Have knowledge of web development tools, demonstrated by writing technical specification documentation.
  • Gain exposure to accessible web design, understanding the principles of how to create websites and apps that work well on mobile devices, and that support use of assistive technologies like screen readers.
  • Cultivate strategic and creative responses in the search for solutions to well-defined concrete and abstract problems related to web development tools.
  • Demonstrate knowledge of the request-response structure, along with database management and JSON-based APIs.
Skills
  • Communicate ideas in a well-structured, coherent format, following appropriate conventions in the field of technology.
  • Work independently to build a web application, trouble-shooting problems as they rise using self-directed research techniques.
  • Ability to solve front-end web application problems related to design requirements using HTML, CSS and JavaScript.
  • Evaluates their own learning and identifies the learning deficits to address in further learning
Competencies
  • Possess the academic competences to undertake further studies in web application development with a degree of autonomy.
  • Monitor and review their own performance and the performance of others; where appropriate collaboratively train others in the correct approach to computer web application development.
  • Build the front-end of a web application using HTML, CSS and JavaScript then write a supporting back-end using either a JavaScript or Python framework.
  • Independently manage projects that require techniques related to building web applications where the correct use of client and server-side development for the web is essential.
Mathematical Thinking
150 hours | 6 ECTS

About

This course helps students develop the ability to think logically and mathematically. It prepares students for more advanced courses in algorithms and discrete mathematics. An emphasis is placed on the ability to reason logically, and effectively communicate mathematical arguments. The course begins with a brief review of number systems, and their relevance to digital computers. Students review the algebraic operations necessary to perform programming functions. In the unit on logic and proofs, students learn to identify, evaluate, and make convincing mathematical arguments. They are introduced to formal logic, and methods for determining the validity of an argument (truth tables, proofs, Venn Diagrams). Students learn to decompose problems using recursion and induction, and how these methods are used in real-world computational problems. The final unit is an introduction to counting and probability. Topics covered include principles of counting, permutations, combinations, random variables, and probability theory. Throughout the course, students apply their knowledge by solving logic puzzles and creating programs in Python.

Teachers

Priti Mondal
Priti Mondal
Anupriya J
Anupriya J

Intended learning outcomes

Knowledge
  • Cultivate strategic and creative responses in the search for solutions to well-defined concrete and abstract problems related to logic and proofs.
  • Make judgments, relevant to real-world computational problems, based on knowledge of the principles of counting, permutations, combinations, random variables, and probability theory.
  • Display knowledge of algebraic operations in order to perform programming functions that builds upon advanced general education, though at a level still supported by advanced textbooks.
Skills
  • Ability to apply theoretical and practical knowledge in the creation of solutions for problems related to mathematical arguments.
  • Can select appropriate evidence when formulated responses to well-defined concrete and abstract problems of algebraic operations.
  • Identify, evaluate, and make convincing mathematical arguments which are communicated in a well-structured, coherent format, following appropriate conventions.
  • Decompose problems using recursion and induction in the context of real-world computational problems.
Competencies
  • Develop the ability to think logically and mathematically at a level that prepares students for more advanced courses in algorithms and discrete mathematics.
  • Monitor and review their own performance and the performance of others; where appropriate collaboratively train others in the correct approach to effectively communicate mathematical arguments.
  • Display creativity and initiative in carrying out algebraic operations necessary to perform programming functions.
  • Possess the academic competences to undertake further studies in advanced courses in algorithms and discrete mathematics with a degree of autonomy.
Industry Experience 1
300 hours | 12 ECTS

About

Industry Experience is a form of experiential learning that enables students to apply their academic knowledge in a professional context. Students work to build software that meets the needs of a professional organization by completing either (1) an approved internship, or (2) a product studio. During the internship, students work on tasks that meet the needs of the organization, guided by an on-site supervisor. Internships must entail significant, substantial computer science. In the studio, external clients (e.g., businesses, non-profits) sponsor a software development project completed by students. A typical end result is a prototype of or a fully functional software system ready for use by the clients. These projects are completed by teams of 4-6 students, who meet with the client weekly to share progress and get feedback. Students complete online modules under the supervision of a faculty advisor. Pre-work includes instruction in communication, goal-setting, and professional development. During the industry experience, students submit bi-weekly written reflections on their personal goals, challenges, and, for the studio, team feedback. At the end of the term, students obtain written feedback from their organization supervisor.  They also submit a final report which describes the problem statement, approaches/methods used, deliverables, and skills gained. Industry Experience culminates in a final presentation which is shared as a public blog post.

Teachers

Harshini Esther
Harshini Esther
Piyali Mondal Amitava Mondal
Piyali Mondal Amitava Mondal
Anupriya J
Anupriya J
Anesh Jayantilal Soni
Anesh Jayantilal Soni

Intended learning outcomes

Knowledge
  • Make judgments based on knowledge of the rules and conventions for the proper use of communication and demonstrate knowledge of the social and ethical issues relevant to technology.
  • Understand a range of tools and techniques used in professional settings.
  • Utilize detailed theoretical and practical knowledge essential to industry experience.
  • Have industry-relevant knowledge that goes beyond advanced general education textbooks and is applicable to the field of technology.
Skills
  • Translate business requirements that meet the needs of the organization into actionable software development tasks.
  • 1. Implement knowledge and understanding in a way that demonstrates professionalism in a field of technology.
  • Communicate academic knowledge and skills in a well-structured, coherent format, following appropriate conventions in the field of technology.
Competencies
  • Possess the academic competences to undertake further studies in professional development with a high degree of autonomy.
  • Demonstrates administrative planning, resource management, and team management as well as handling unpredictable and complex issues in a professional setting.
  • Show creativity and initiative to develop projects with effective communication.
Computer Systems
150 hours | 6 ECTS

About

This course explores computing beyond software. Students will go a level deeper to better understand the hardware and see how computers are built and programmed.  It is modelled on the popular, project based “Nand to Tetris” textbook, which walks learners through building a computer from scratch.  It aims to help students become better programmers by teaching the concepts underlying all computer systems. The course integrates many of the topics covered in other computer science courses, including algorithms, computer architecture, operating systems, and software engineering.

Students will learn how to build a computer system using progressive steps.  The course starts with a brief review of Boolean algebra, and an introduction to logic gates. Students design a set of elementary logic gates using a Hardware Description Language. They then build chips to perform arithmetic and logical operations and build the computer’s main memory unit. Subsequently, students learn to write low-level machine language, and build a CPU to create a fully functional computer system. Finally, students implement a virtual machine, compiler, and basic operating system. Projects are spread out evenly throughout the course, and are completed in pairs.

By the end of the course, students will develop a strong understanding of the relationships between the architecture of computers, and software that runs on them.

Teachers

Piyali Mondal Amitava Mondal
Piyali Mondal Amitava Mondal
Rupal
Rupal
Anupriya J
Anupriya J

Intended learning outcomes

Knowledge
  • Computer systems, including algorithms, computer architecture, operating systems, and software engineering.
  • The computer science disciplines essential to designing and building general purpose computer systems.
  • Boolean algebra, and logic gates.
Skills
  • Complete projects related to computer system building, while working collaboratively.
  • Write low-level machine language.
  • Consistently evaluates own learning and identifies learning needs.
  • Communicate with clarity about the relationships between the architecture of computers, and software that runs.
  • Build a computer from scratch
Competencies
  • Understanding the relationships between the architecture of computers, and the software that runs on them, to the extent of building a CPU to create a fully functional computer system.
  • Design a set of elementary logic gates using a Hardware Description Language.
  • Possess the academic competences to undertake further studies of the concepts underlying all computer systems.
Database Management
150 hours | 6 ECTS

About

The module's primary learning outcomes are for students to identify different types of database management systems, describe their components, and explain the importance of database normalization. Through the course, students will learn about database design, normalization, and optimization. They will also learn how to use SQL to manipulate and retrieve data from databases. The module emphasizes hands-on learning through database design and development projects. By the end of the module, students will have gained a comprehensive understanding of database management systems and their importance in AI and ML applications. They will be able to identify different types of database management systems and their components, and apply the concepts of database normalization to design and develop efficient databases. This knowledge will prepare them for more advanced courses in the curriculum and for database management roles in the industry.

Teachers

Piyali Mondal Amitava Mondal
Piyali Mondal Amitava Mondal
Mohamed Irfan Shaikh
Mohamed Irfan Shaikh
Anupriya J
Anupriya J

Intended learning outcomes

Knowledge
  • Learn about data consistency and integrity constraints, and how they are enforced within a DBMS.
  • Understand the core principles of database management systems, including relational databases, data models, and database architecture.
  • Gain in-depth knowledge of data storage mechanisms, indexing, and file organization techniques, and how they impact database performance.
Skills
  • Acquire skills in database administration, including tasks such as backup, recovery, user management, and performance tuning.
  • Gain proficiency in constructing efficient and normalized database schemas that meet specific requirements.
  • Master the use of SQL (Structured Query Language) to create, manipulate, and query databases effectively.
Competencies
  • Demonstrate the ability to design and implement robust and efficient databases that meet the needs of various applications and industries.
  • Apply knowledge of database management to real-world scenarios, such as developing databases for business applications, managing large-scale databases, and implementing data-driven solutions.
  • Exhibit the ability to solve complex problems related to database management, such as optimizing query performance, managing large datasets, and ensuring data security.
Programming 1
150 hours | 6 ECTS

About

The course helps students develop an appreciation for programming as a problem-solving tool. It teaches students how to think algorithmically and solve problems efficiently, and serves as the foundation for further computer science studies.

Using a project-based approach, students will learn to manipulate variables, expressions, and statements in Python, and understand functions, loops, and iterations. Students will then dive deep into data structures such as strings, files, lists, dictionaries, tuples, etc. to write complex programs. Over the course of the term, students will learn and apply basic data structures and algorithmic thinking. Finally, the course will explore design and implementation of web apps in Python using the Flask framework.

Throughout the course, students will be exposed to abstraction and will learn a systematic way of constructing solutions to problems. They will work on team projects to practice pair programming, code reviews, and other collaboration methods common to industry. The course culminates in a final group project and presentation during which students demonstrate and reflect on their learning.

Teachers

Piyali Mondal Amitava Mondal
Piyali Mondal Amitava Mondal
Anupriya J
Anupriya J

Intended learning outcomes

Knowledge
  • Make judgments based on knowledge of an abstract, systematic way of constructing solutions to problems.
  • Cultivate strategic and creative responses to problems for which the solutions require a knowledge of data structures such as strings, files, lists, dictionaries, or tuples.
  • Have an introductory knowledge of programming as a problem-solving tool, demonstrated by identifying the jobs to be done and implementing software solutions, such as web-based apps in Python using a Flask framework.
Skills
  • Can select appropriate evidence and formulate code reviews to support the work of others.
  • Evaluates their own learning and identifies the learning deficits to address in further learning.
  • Ability to use abstraction and systematically construct solutions to problems.
  • Communicate ideas in a well-structured, coherent format, following appropriate conventions pair programming and online code collaboration.
Competencies
  • Possess the academic competences to undertake further studies in computer science with a degree of autonomy.
  • Display creativity and initiative in writing complex programs requiring application of a knowledge of basic data structures and algorithmic thinking.
  • Independently manage projects that require programming as a problem-solving tool, requiring the manipulation of variables, expressions, and statements.
  • Monitor and review their own performance and the performance of others; where appropriate collaboratively train others in the correct approach to programming.
Operating Systems
75 hours | 3 ECTS

About

The module's primary learning outcomes are for students to classify different types of operating systems, including Windows, macOS, and Linux, and to describe the functions of an operating system, such as process and memory management.Through the course, students will learn about the architecture and components of operating systems, including user interfaces, device drivers, and file systems. They will also gain an understanding of system calls and APIs, and how to use them to interact with an operating system. By the end of the module, students will have gained a comprehensive understanding of operating systems, their functions, and their importance in computer science and AI. They will be able to identify the different types of operating systems and describe their functions and features. This knowledge will prepare them for more advanced courses in the curriculum that involve developing AI and ML applications on different operating systems.

Teachers

Piyali Mondal Amitava Mondal
Piyali Mondal Amitava Mondal
Mohamed Irfan Shaikh
Mohamed Irfan Shaikh
Maria Monica
Maria Monica
Anupriya J
Anupriya J

Intended learning outcomes

Knowledge
  • Recognize and describe the characteristics of major operating systems like Windows, macOS, and Linux.
  • Analyze the architectural differences and common features among various operating systems.
  • Understand how different operating systems support specific application environments and user interfaces.
Skills
  • Learn how operating systems allocate and manage memory resources effectively.
  • Understand how operating systems handle the creation, scheduling, and termination of processes.
  • Apply logical reasoning to explain how operating systems optimize resource usage through process and memory management.
Competencies
  • Develop and apply skills to use system calls and APIs for performing basic tasks and interacting with the operating system effectively.
  • Create and implement a plan to monitor the development and impact of AI technologies, making adjustments as necessary to optimize outcomes.
  • Apply APIs to facilitate communication between applications and the operating system.
AI and Business Analytics
25 hours | 1 ECTS

About

Upon completion of this course, you will gain a deep understanding of how business analytics supports data-driven decision-making in an evolving business landscape. You will explore key analytics frameworks, learning how organisations leverage data to navigate uncertainty and drive strategic growth. Through practical applications, you will differentiate between various data-driven techniques and examine their real-world implementation across industries such as banking and healthcare. Additionally, you will critically assess the challenges and ethical considerations of integrating analytics tools into business processes, equipping you to apply these insights effectively in your organisation.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Assess the evolution of business analytics and its role in data-driven decision-making.
Skills
  • Analyse business analytics and AI concepts to real-world case study, focussing on enhancing strategic and operational outcomes.
Competencies
  • Evaluate emerging trends, ethical considerations, and risk mitigation strategies in AI and business analytics.
Basics of Financial Valuation
25 hours | 1 ECTS

About

Upon completion of this programme, you will develop a customer-centric and future-oriented marketing mindset to promote sustainable growth in your organisation, or organisations you might work with in the future. Additionally, you will delve into the foundational topic of finance and economics-valuation. You will gain a comprehensive understanding of how key concepts are applied in financial decision-making and investment strategies.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a customer-centric marketing mindset to drive sustainable business growth.
Skills
  • Analyse company valuation using comparables analysis and financial modelling techniques, including LBO.
  • Apply segmentation, targeting, positioning (STP), and the marketing mix (4Ps) to optimise brand strategies.
Competencies
  • Evaluate key financial valuation methods, including NPV and DCF, to inform investment decisions.
Fundamentals of Operations Management
25 hours | 1 ECTS

About

Upon completion of this programme, you will develop fluency in the fundamental frameworks and analytical tools needed to effectively assess an organisation's strategic landscape. Through a blend of theoretical exploration and practical application, you'll gain the ability to develop insightful strategic recommendations for organisational success. Additionally, you will develop the knowledge and skills to analyse and improve how work is performed in your organisation.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Understand and assess an organisation’s environment using key frameworks.
Skills
  • Develop strategic recommendations through analysis and research.
  • Apply frameworks to enhance operational efficiency.
Competencies
  • Optimise processes using operations management principles.
Digital Transformation Essentials for Tech Leaders
25 hours | 1 ECTS

About

In this course, you will develop the strategic awareness and practical skills needed to lead digital transformation effectively within your organisation. You will explore the drivers of digital disruption, learn how to critically assess emerging technologies, and understand how to deliver transformation projects that align with organisational goals. You will also gain essential insights into cyber risk: how

to anticipate, mitigate, and respond to threats, and learn how to embed cyber resilience into your leadership approach. Through case studies, frameworks, and reflection exercises, you will build the confidence to lead digital initiatives in an informed, strategic, and future-ready way.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Identify and mitigate cyber risks to ensure secure digital environments.
  • Analyse the opportunities and risks associated with digital transformation.
Skills
  • Develop and apply strategies to successfully deliver digital transformation initiatives.
  • Critically evaluate emerging technology trends and their organisational impact.
Competencies
  • Lead digital transformation through a cyber resilience lens, aligning with strategic goals and stakeholder expectations.

Entry Requirements

Tuition Cost
20,000 AED
Student education requirement
High School

Application Process

1

Submit initial Application

Complete the online application form with your personal information

2

Documentation Review

Submit required transcripts, certificates, and supporting documents

3

Assessment

Your application will be evaluated against program requirements

4

Interview

Selected candidates may be invited for an interview

5

Decision

Receive an admission decision

6

Enrollment

Complete registration and prepare to begin your studies

Ready to advance your education with a globally recognised degree?

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