Programme Overview

The Master of Data Science (MDASC) programme is designed to equip students with a comprehensive understanding of data science, combining statistical theory with cutting-edge machine learning methods. The programme's interdisciplinarity promotes the applications of computer technology, operational research, statistical modelling, and simulation for problem-solving and decision-making in organisations and enterprises within the private and public sectors.

Curriculum

The curriculum of the MDASC programme adopts a well-balanced and comprehensive pedagogy of both statistical and computational concepts and methodologies, underpinning applications that are not limited to business or a single field alone. The programme is ideal for those interested in acquiring analytical skills in areas ranging from statistics to computational analytics, and those who wish to pursue further studies in data science after having completed undergraduate studies in areas such as science, engineering, medical sciences, social sciences, information systems, computing, and data analytics.

Programme Structure

Full Time: 1.5 Years
Part Time: 2.5 Years
Credits: 72

Interdisciplinary Courses

Core (24 credits) + Electives (36 credits)
Capstone Requirement: DASC7600 (12 credits) or DASC8088 (6 credits) + a List A/B 6-credits course

Programme Highlights

Interdisciplinary and Comprehensive Curriculum

Solid foundation in statistical and computational analyses
Hands-on applications of methodologies with software
Electives cover a broad range of contemporary topics about Computer Science and Statistics
Capstone project with real-life applications

Course Highlights

The core courses of the proposed MDASC programme mainly focus on both predictive and prescriptive concepts and methodologies with an effort to equip students with a solid foundation in statistical and computational analyses, e.g.

Statistical modelling
Computational intelligence

The electives cover a broad range of contemporary topics and provide students with solid training in diverse and applied techniques used in data science, including but not limited to

Blockchain data analytics
Multimedia technologies

Programme Structure

The curriculum is the same for both full-time and part-time study modes.

Compulsory Courses

24 credits

COMP7404 - Computational intelligence and machine learning (6 credits)
- This course will teach a broad set of principles and tools that will provide the mathematical, algorithmic, and philosophical framework for tackling problems using Artificial Intelligence (AI) and Machine Learning (ML).
- Topics may include a subset of the following: problem-solving by search, heuristic (informed) search, constraint satisfaction, games, knowledge-based agents, supervised learning (e.g., regression and support vector machine), unsupervised learning (e.g., clustering), dimension reduction learning theory, reinforcement learning, transfer learning, and adaptive control and ethical challenges of AI and ML.
- Pre-requisites: Nil, but knowledge of data structures and algorithms, probability, linear algebra, and programming would be an advantage.
- Assessment: coursework (50%) and examination (50%)
DASC7011 - Statistical inference for data science (6 credits)
- Computing power has revolutionized the theory and practice of statistical inference. Reciprocally, novel statistical inference procedures are becoming an integral part of data science.
- By focusing on the interplay between statistical inference and methodologies for data science, this course reviews the main concepts underpinning classical statistical inference, studies computer-intensive methods for conducting statistical inference, and examines important issues concerning statistical inference drawn upon modern learning technologies.
- Contents include method of moments estimation, least squares estimation, maximum likelihood estimation, principal component analysis, singular value decomposition, simple Monte Carlo simulation, resampling methods, and LASSO and RIDGE regressions.
- Assessment: coursework (50%) and examination (50%)
DASC7104 - Advanced database systems (6 credits)
- The course will study some advanced topics and techniques in database systems, with a focus on the aspects of database systems design & algorithms and big data processing for structured data.
- Traditional topics include: query optimization, physical database design, transaction management, crash recovery, parallel databases.
- This course will also survey some recent developments in selected areas such as NoSQL databases and SQL-based big data management systems for relational (structured) data.
- Prerequisites: A course of introduction to databases and basic programming skills.
- Assessment: coursework (55%) and examination (45%)
DASC7102 - Advanced statistical modelling for data science (6 credits)
- This course aims to build statistical models to study relationships and structures from data using popular computing software.
- It will cover both the underlying principles of each modelling approach and the model estimation procedures.
- Topics include: (i) Generalized linear models; (ii) Model selection and regularization; (iii) Generalized linear mixed effects models; (iv) Kernel density estimation; (v) Non-parametric regression; (vi) Generalized additive models.
- Assessment: coursework (50%) and examination (50%)
- Mutually exclusive with: STAT7102 Advanced statistical modelling

Disciplinary Electives

36 credits

List A

At least 12 credits

COMP7107 - Management of complex data types (6 credits)
- The course studies the management and analysis of data types which are not simple scalars.
- Such complex data types include spatial data, multidimensional data, time-series data, temporal and spatio-temporal data, sparse multidimensional vectors, set-valued data, strings and sequences, homogeneous and heterogeneous graphs, knowledge-base graphs, geo-textual and geo-social data.
- For each of these data types, we will learn popular queries and analysis tasks, as well as storage and indexing methods for main memory and the disk.
- Assessment: coursework (50%) and examination (50%)
COMP7305 - Cluster and cloud computing (6 credits)
- This course offers an overview of current cloud technologies, and discusses various issues in the design and implementation of cloud systems.
- Topics include cloud delivery models (SaaS, PaaS, and IaaS) with motivating examples from Google, Amazon, and Microsoft; virtualization techniques implemented in Xen, KVM, VMWare, and Docker; distributed file systems, such as Hadoop filesystem; MapReduce and Spark programming models for large-scale data analysis, networking techniques in hyper-scale data centers.
- The students will learn the use of Amazon EC2 to deploy applications on cloud, and implement a SPARK application on a Xen-enabled PC cluster as part of their term project.
- Prerequisites: Students are expected to install various open-source cloud software in their Linux cluster, and exercise the system configuration and administration.
- Basic understanding of Linux operating system and some programming experiences (C/C++, Java or Python) in a Linux environment are required.
- Assessment: coursework (50%) and examination (50%)
COMP7409 - Machine learning in trading and finance (6 credits)
- The course introduces our students to the field of Machine Learning, and help them develop skills of applying Machine Learning, or more precisely, applying supervised learning, unsupervised learning and reinforcement learning to solve problems in Trading and Finance.
- This course will cover the following topics. (1) Overview of Machine Learning and Artificial Intelligence, (2) Supervised Learning, Unsupervised Learning and Reinforcement Learning, (3) Major algorithms for Supervised Learning and Unsupervised Learning with applications to Trading and Finance, (4) Basic algorithms for Reinforcement Learning with applications to optimal trading, asset management, and portfolio optimization, (5) Advanced methods of Reinforcement Learning with applications to high-frequency trading, cryptocurrency trading and peer-to-peer lending.
- Assessment: coursework (65%) and examination (35%)
COMP7503 - Multimedia technologies (6 credits)
- This course presents fundamental concepts and emerging technologies for multimedia computing.
- Students are expected to learn how to develop various kinds of media communication, presentation, and manipulation techniques.
- At the end of the course, students should acquire proper skill set to utilize, integrate and synchronize different information and data from media sources for building specific multimedia applications.
- Topics include media data acquisition methods and techniques; nature of perceptually encoded information; processing and manipulation of media data; multimedia content organization and analysis; trending technologies for future multimedia computing.
- Assessment: coursework (50%) and examination (50%)
COMP7506 - Smart phone apps development (6 credits)
- Smart phones have become an essential part of our everyday lives.
- The number of smart phone users worldwide today surpasses six billion and is forecast to further grow by more than one billion in the next few years.
- Smart phones play an important role in mobile communication and applications.
- Smart phones are powerful as they support a wide range of applications (called apps).
- Most of the time, smart phone users just download their favorite apps remotely from the app stores.
- There is a great potential for software developers to reach worldwide users.
- This course aims at introducing the design and technical issues of smart phone apps.
- For example, smart phone screens are usually smaller than computer monitors while smart phones usually possess more hardware sensors than conventional computers.
- We have to pay special attention to these aspects in order to develop attractive and successful apps.
- Various modern smart phone apps development environments and programming techniques (such as Java for Android phones and Swift for iPhones) will also be introduced to facilitate students to develop their own apps.
- Students should have basic programming knowledge.
- Mutually exclusive with: COMP3330 Interactive mobile application design and programming
- Assessment: coursework (60%) and examination (40%)
COMP7507 - Visualization and visual analytics (6 credits)
- This course introduces the basic principles and techniques in visualization and visual analytics, and their applications.
- Topics include human visual perception; color; visualization techniques for spatial, geospatial and multivariate data, graphs and networks; text and document visualization; scientific visualization; interaction and visual analysis.
- Assessment: coursework (40%) and examination (60%)
COMP7906 - Introduction to cyber security (6 credits)
- The aim of the course is to introduce different methods of protecting information and data in the cyberworld, including the privacy issue.
- Topics include introduction to security; cyber attacks and threats; cryptographic algorithms and applications; network security and infrastructure.
- Mutually exclusive with: ICOM6045 Fundamentals of e-commerce security.
- Assessment: coursework (50%) and examination (50%)
DASC7606 - Deep learning (6 credits)
- Machine learning is a fast-growing field in computer science and deep learning is the cutting-edge technology that enables machines to learn from large-scale and complex datasets.
- Ethical implications of deep learning and its applications will be covered and the course will focus on how deep neural networks are applied to solve a wide range of problems in areas such as natural language processing, and image processing.
- Other applications such as financial predictions, game playing and robotics may also be covered.
- Topics covered include linear and logistic regression, artificial neural networks and how to train them, recurrent neural networks, convolutional neural networks, generative models, deep reinforcement learning and unsupervised feature learning.
- Prerequisites: Basic programming skills, e.g., Python is required.
- Assessment: coursework (50%) and examination (50%)
FITE7410 - Financial fraud analytics (6 credits)
- This course aims at introducing various analytics techniques to fight against financial fraud.
- These analytics techniques include, descriptive analytics, predictive analytics, and social network learning.
- Various data sets will also be introduced, including labeled or unlabeled data sets, and social network data set.
- Students learn the fraud patterns through applying the analytics techniques in financial frauds, such as, insurance fraud, credit card fraud, etc.
- Key topics include: Handling of raw data sets for fraud detection; Applications of descriptive analytics, predictive analytics and social network analytics to construct fraud detection models; Financial Fraud Analytics challenges and issues when applied in business context.
- Required to have basic knowledge about statistics concepts.
- Assessment: coursework (50%) and examination (50%)
ICOM6044 - Data science for business (6 credits)
- The emerging discipline of data science combines statistical methods with computer science to solve problems in applied areas.
- In this case, we focus on how data science can be used to solve business problems especially those enabled by electronic commerce.
- By its very nature, e-commerce is able to generate large amounts of data and data mining methods are quite helpful for managers in turning this data into knowledge which in turn can be used to make better decisions.
- These data sets and their accompanying quantitative methods have the potential to dramatically change decision making in many areas of business.
- For example, ideas like interactive marketing, customer relationship management, and database marketing are pushing companies to utilize the information they collect about their customers in order to make better marketing decisions.
- This course focuses on how data science methods can be applied to solve managerial problems in business.
- Our emphasis is developing a core set of principles that embody data science: empirical reasoning, exploratory and visual analysis, and predictive modeling.
- We use these core principles to understand many methods used in data mining and machine learning.
- Our strategy in this course is to survey several popular techniques and understand how they map into these core principles.
- These techniques are illustrated with case studies that involve decisions about targeting, product recommendation, customer retention and financial lending.
- The class takes a learning-by-doing approach to analyze data and make decisions from these analyses.
- However, the emphasis is not on the software for implementing these techniques but on understanding the inputs and outputs of these techniques and how they are used to solve business problems, and effectively communicate them to managers.
- Assessment: coursework (65%) and examination (35%)
ICOM7128 - Knowledge graphs (6 credits)
- Knowledge graphs, a powerful method for organizing and managing complex information, offers comprehensive benefits and advanced capabilities for organizations to effectively manage their data platforms and creating a network of connected and meaningful relationships that drives smarter business decisions.
- Knowledge graphs empower businesses to unlock the full potential of their data, enabling enhanced data integration, improved data analytics, contextual semantic understanding, efficient data governance, agile data quality rules management, connected visualizations, and generative artificial intelligence support.
- By harnessing the power and technological advantages of knowledge graphs, enterprises can drive innovation, gain a competitive advantage, and revolutionize the operational landscape of businesses in the digital era.
- This comprehensive course provides in-depth coverage and technical understanding of the fundamental principles, techniques, and tools employed in constructing and leveraging knowledge graph platforms.
- The course encompasses topics such as knowledge graph modeling, representation and reasoning with ontologies, vocabulary and taxonomy management, graph querying, large language models in graphs, and graph conformance rules.
- Practical real-world applications and hands-on exercises are integrated to reinforce theoretical concepts, ensuring practical application of knowledge.
- Assessment: coursework (50%) and examination (50%)

List B

At least 12 credits

STAT6008 - Advanced statistical inference (6 credits)
- This course covers the advanced theory of point estimation, interval estimation, and hypothesis testing.
- Using a mathematically-oriented approach, the course provides a formal treatment of inferential problems, statistical methodologies, and their underlying theory.
- It is suitable in particular for students intending to further their studies or to develop a career in statistical research.
- Contents include: (1) Decision problem – frequentist approach: loss function; risk; decision rule; admissibility; minimaxity; unbiasedness; Bayes’ rule; (2) Decision problem – Bayesian approach: prior and posterior distributions, Bayesian inference; (3) Estimation theory: exponential families; likelihood; sufficiency; minimal sufficiency; completeness; UMVU estimators; information inequality; large-sample theory of maximum likelihood estimation; (4) Hypothesis testing: uniformly most powerful (UMP) test; monotone likelihood ratio; UMP unbiased test; conditional test; large-sample theory of likelihood ratio; confidence set; (5) Nonparametric inference; bootstrap methods.
- Assessment: coursework (40%) and examination (60%)
STAT6013 - Financial data analysis (6 credits)
- This course aims at introducing statistical methodologies in analyzing financial data.
- Financial applications and statistical methodologies are intertwined in all lectures.
- Contents include: classical portfolio theory, portfolio selection in practice, single index market model, robust parameter estimation, copula and high-frequency data analysis.
- Assessment: coursework (40%) and examination (60%)
STAT6015 - Advanced quantitative risk management (6 credits)
- This course covers statistical methods and models of risk management, especially of Value-at-Risk (VaR).
- Contents include: Value-at-risk (VaR) and Expected Shortfall (ES); univariate models (normal model, log-normal model, and stochastic process model) for VaR and ES; models for portfolio VaR; time series models for VaR; extreme value approach to VaR; back-testing and stress testing.
- Assessment: coursework (50%) and examination (50%)
STAT6016 - Spatial data analysis (6 credits)
- This course covers statistical concepts and tools involved in modeling data which are correlated in space.
- Applications can be found in many fields including epidemiology and public health, environmental sciences and ecology, economics, and others.
- Covered topics include: (1) Outline of three types of spatial data: point-level (geostatistical), areal (lattice), and spatial point process. (2) Model-based geostatistics: covariance functions and the variogram; spatial trends and directional effects; intrinsic models; estimation by curve fitting or by maximum likelihood; spatial prediction by least squares, by simple and ordinary kriging, by trans-Gaussian kriging. (3) Areal data models: introduction to Markov random fields; conditional, intrinsic, and simultaneous autoregressive (CAR, IAR, and SAR) models. (4) Hierarchical modeling for univariate spatial response data, including Bayesian kriging and lattice modeling. (5) Introduction to simple spatial point processes and spatio-temporal models.
- Real data analysis examples will be provided with dedicated R packages such as geoR.
- Assessment: coursework (50%) and examination (50%)
STAT6019 - Current topics in statistics (6 credits)
- This course may include modules such as:
  - Causal Inference, is an introduction to key concepts and methods for causal inference.
  - Contents include: (1) the counterfactual outcome, randomized experiment, observational study; (2) Effect modification, mediation, and interaction; (3) Causal graphs; (4) Confounding, selection bias, measurement error, and random variability; (5) Inverse probability weighting and the marginal structural models; (6) Outcome regression and the propensity score; (7) The standardization and the parametric g-formula; (8) G-estimation and the structural nested model; (9) Instrumental variable method; (10) Machine learning methods for causal inference; (11) Other topics as determined by the instructor.
  - Functional data analysis, covers topics from: (1) Base functions; (2) Least squares estimation; (3) Constrained functions; (4) Functional PCA; (5) Regularized PCA; (6) Functional linear model; (7) Other topics as determined by the instructor.
- Assessment: coursework (100%)
STAT7008 - Programming for data science (6 credits)
- Capturing and utilizing essential information from big datasets poses both statistical and programming challenges.
- This course is designed to equip students with the fundamental computing skills required to use Python for addressing these challenges.
- The course will cover a range of topics, including programming syntax, files IO, object-oriented programming, scientific data processing and analysis, data visualization, data mining, and web scraping, programming techniques for machine learning, deep learning, computer vision, and natural language processing, etc.
- Assessment: coursework (100%)
STAT8003 - Time series forecasting (6 credits)
- Discrete time series are integer-indexed sequences of random variables.
- Such series arise naturally in climatology, economics, finance, environmental research, and many other disciplines.
- The course covers statistical modeling and forecasting of time series.
- Topics may include stationary and nonstationary time series, ARMA models, identification based on autocorrelation and partial autocorrelation, GARCH models, goodness-of-fit, forecasting, and nonlinear time series modeling.
- Assessment: coursework (50%) and examination (50%)
STAT8017 - Data mining techniques (6 credits)
- With the rapid developments in computer and data storage technologies, the fundamental paradigms of classical data analysis are mature for change.
- Data mining techniques aim at helping people to work smarter by revealing underlying structure and relationships in large amounts of data.
- This course takes a practical approach to introduce the new generation of data mining techniques and show how to use them to make better decisions.
- Topics include data preparation, feature selection, association rules, decision trees, bagging, random forests, and gradient boosting, cluster analysis, neural networks, introduction to text mining.
- Assessment: coursework (100%)
STAT8019 - Marketing analytics (6 credits)
- This course aims to introduce various statistical models and methodology used in marketing research.
- Special emphasis will be put on marketing analytics and statistical techniques for marketing decision making including market segmentation, market response models, consumer preference analysis, conjoint analysis, and extracting insights from text data.
- Contents include statistical methods for segmentation, targeting, and positioning, statistical methods for new product design, text mining techniques, and market response models.
- Assessment: coursework (50%) and examination (50%)
STAT8300 - Career development and communication workshop (Non-credit-bearing)
- The course is specially designed for students who wish to sharpen their communication and career preparation skills through a variety of activities including but not limited to lectures, skill-based workshops, small group discussion, mock interviews, role plays.
- All of which aim to (i) facilitate students in making informed career choices; (ii) provide students with practical training to enrich communication, presentation, time management, and advanced interview skills.
- The course is designed to enhance students’ overall competitiveness in the employment markets and the readiness in job hunting.
- Assessment: coursework (100%), assessment of this course is on a pass or fail or distinction basis
STAT8306 - Statistical methods for network data (3 credits)
- The six degree of separation theorizes that human interactions could be easily represented in the form of a network.
- Examples of networks include router networks, the World Wide Web, social networks (e.g., Facebook or Twitter), genetic interaction networks, and various collaboration networks (e.g., movie actor collaboration network and scientific paper collaboration network).
- Despite the diversity in the nature of sources, the networks exhibit some common properties.
- For example, both the spread of disease in a population and the spread of rumors in a social network are in sub-logarithmic time.
- This course aims at discussing the common properties of real networks and the recent development of statistical network models.
- Topics may include common network measures, community detection in graphs, preferential attachment random network models, exponential random graph models, models based on random point processes, and the hidden network discovery on a set of dependent random variables.
- Assessment: coursework (100%)
STAT8307 - Natural language processing and text analytics (6 credits)
- Natural Language Processing (NLP) is a core area of artificial intelligence and data science that focuses on enabling machines to understand, process, and generate human language.
- This course introduces fundamental NLP techniques and text analytics methods, covering topics such as information retrieval, text classification, word embeddings, neural networks, sequence models, encoder-decoder architectures, transformers, contextualized word representations, and modern language models.
- The course emphasizes practical applications and hands-on experience in analyzing textual data.
- Proficiency in Python programming is required.
- Pre-requisites: Pass in STAT7008 Programming for data science or equivalent [for MStat and MDASC students] Pass in ARIN7102 Applied data mining and text analytics or equivalent [for MSc(AI) students]
STAT8308 - Blockchain data analytics (3 credits)
- In this course, we start by studying the basic architecture of a blockchain.
- Then we move on to several major applications including (but not limited to) cryptocurrencies, fintech, and smart contracts.
- We conclude by examining the cybersecurity issues facing the blockchain ecosystems.
- Assessment: coursework (100%)
STAT8309 - Monte Carlo simulation in finance (6 credits)
- This course covers basic knowledge of Monte Carlo simulation and its application to finance.
- Course contents include random variate generation, Monte Carlo methods, simulation stock prices under different models, risk-neutral probabilities, evaluation of European options, evaluation of American options, evaluation of Asian options, variance reduction techniques, and efficiency improvement.
- Assessment: coursework (100%)

Capstone Requirement

12 credits

DASC7600 - Data science project (12 credits)
- Candidate will be required to carry out independent work on a major project under the supervision of an individual staff member.
- A written report is required.
- Note: Students should not be taking or have taken DASC8088 Data science practicum
- Assessment: written report (70%) and oral presentation (30%)
DASC8088 - Data science practicum (6 credits) + a 6-credit course (from List A or List B)
- This course provides students with first-hand experience in applying academic knowledge in a real-life work environment.
- To be eligible, students should be undertaking a data-science-related practicum with no less than 160 hours in at least 20 working days spent in a paid or unpaid position.
- It is allowed for part-time students to complete their practicum within their current place of employment.
- The practicum will normally take place in the summer semester for full-time students or during the summer semester of the second year for part-time students.
- Note: Students should not be taking or have taken DASC7600 Data science project. Students who opt for DASC8088 should take a 6-credit elective course from List A or List B to fulfill the 12-credit capstone requirement.
- Assessment: Upon completion of the practicum, each student is required to submit a written report (60%) and to give an oral presentation (40%) on his/her practicum experience. Supervisors will assess the students based on their performance during the practicum period. Assessment of this course is on a Pass, Fail, or Distinction basis with three criteria: (1) supervisor’s evaluation, (2) written report, (3) oral presentation. Failing in fulfilling any of the three criteria satisfactorily leads to a “Fail” grade in the course.

Congregation

From 2025, two Congregations will be held annually (in July and December respectively) which is to align with international practices and to facilitate the convenience for students.

Graduands who are eligible for graduation will be assigned to the Congregation nearest to the completion date of their studies for conferral of degree. The Graduation Certificate normally could be collected on the day of the Congregation.

Expected graduation time for normative study period

Full Time: Summer (July 2028)
Part Time: Summer (July 2029)

Tuition Fee and Scholarships

Programme Fees (subject to approval)

This is a self-funded programme. The full tuition fee is HK$324,000 for local students / HK$339,840 for Non-local students (for FT & PT students admitted in September 2026). The full fee would normally be paid by full-time students in 3 installments, and part-time students in 5 installments.

Prizes/Scholarships

Belt and Road Scholarship in Statistics and Data Science (Taught Postgraduate)
Entrance Scholarship for the Master of Data Science
Lifelong Learning Prizes in Data Science
Master of Data Science Outstanding Performance Award

Hong Kong Future Talents Scholarship Scheme for Advanced Studies

Master of Data Science (MDASC) has been selected by the University Grants Council (UGC) to be part of the "Hong Kong Future Talents Scholarship Scheme for Advanced Studies (FTSS)" in recognition of the School’s strategic contribution to capacity building in Hong Kong.

Local* students who are admitted (accepted the admission offer with deposit paid) to the MDASC programme (full-time/part-time mode) in the academic year 2025-26 are eligible for application. Applicants are required to prepare a one-page summary on how they can contribute to the priority areas (i.e., STEM) of Hong Kong after completing MDASC. More application details will be released to the eligible candidates by email in due course.

Successful applicants will each receive an award of HK$100,000. Please note that if the awardees cannot complete MDASC for any reasons or are not able to obtain satisfactory results, they will be required to refund the full amount of the award.

*Non-local students holding (a) student visa / entry permit; (b) visa / entry permit under the Immigration Arrangements for Non-local Graduates; or (c) dependent visa / entry permit who were 18 years old or above when they were issued with such visa / entry permit by the Director of Immigration are NOT eligible for the Scheme.

Continuing Education Fund (CEF)

The following courses have been included in the list of reimbursable courses for Continuing Education Fund (CEF) purposes:

COMP7503 Multimedia Technologies
COMP7506 Smart Phone Apps Development
COMP7507 Visualization and Visual Analytics
COMP7906 Introduction to Cyber Security
STAT6013 Financial Data Analysis
STAT7008 Programming for Data Science
STAT8003 Time Series Forecasting
STAT8017 Data Mining Techniques
STAT8019 Marketing Analytics

All CEF applicants are required to attend at least 70% of the courses before they are eligible for fee reimbursement under the CEF.

*The mother programme (Master of Data Science) of these courses is recognized under the Qualifications Framework (QF Level 6).

Master of Data Science

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