
MSc in Data Science
Cape Town, South Africa
DURATION
LANGUAGES
English
PACE
Full time
APPLICATION DEADLINE
30 Sep 2025
EARLIEST START DATE
Feb 2026
TUITION FEES
ZAR 171,450 *
STUDY FORMAT
On-Campus
* for Rest of the World| SADC Fees: From ZAR 110,550| Non-SADC African Students Fees: From ZAR 156,050
Introduction
The interdisciplinary Master's course with a specialisation in Data Science, is offered in collaboration with the departments of Statistical Sciences, Computer Science, Astronomy, the Computation Biology Group (Faculty of Health Sciences) and the departments of Finance and Tax, Economics and AIFMRM (Commerce Faculty).
There is huge interest and focus on the availability of lots of data, generated through financial systems, medical research, astronomical investigation, social media and the realisation that a special skills set is needed to makes sense of this data and allow information to lead to knowledge and decisions. As a result universities over the world have started programs in either Data Science, Analytics, Machine Learning, or Artificial Intelligence and Institutes have been formed. The Alan Turing Institute, established in 2015 in the UK with headquarters in the British Library, describes Data Science as follows:
“As the amount of data we generate increases, so does our need to understand and use that data. Data science is the fundamental science behind data analytics, and draws on various existing sciences in response to that need: the mathematical sciences, the computing sciences, the social sciences, software engineering and domain expertise from multiple industries and sectors.”
The focus is also shifting to include Artificial Intelligence. Data Science and AI are connected in that through its extraction of knowledge and insights from data, Data Science helps AI to find solutions to problems. Machine Learning is the process of learning from data over time and provides the link between Data Science and AI.
In South Africa, many universities have established programs in Data Science, including the National e-Science Postgraduate Teaching and Training Platform (NEPTTP) offered by a consortium of 5 universities (UP,WITS, NWI, UL and UNIVEN), the undergraduate program in Data Science offered by Sol Plaatje university, the Big Data Science program offered by University of Pretoria, honours and masters programs in Data Science offered by the University of the Western Cape and the new Data Science and Computational Thinking School of the University of Stellenbosch.
Here at UCT we have a masters program in Data Science, offered by the Department of Statistical Sciences, in collaboration with the departments of Computer Science, Information Systems, Astronomy, Physics, Integrated Medical Sciences, Finance and Tax and Economics.
The features of the program is based on the premise that Data Science refers to the art of learning from data and that the required knowledge come from three areas, Statistical Sciences, Computer Science and the Domain Science. It is thus an interdisciplinary program where the core methodological modules are taught by the departments of Statistical Sciences and Computer Science, while the elective modules are taught by the domain experts in Astromomy, Physics, Computational Biology (Health Sciences), Finance and Tax and Economics.
Entry to the program requires a good honours degree with a significant quantitative component. The program is structured so that we can accept a range of students with varying backgrounds in Statistics, Mathematics and Computers Science and we are able to tailor each student’s curriculum to the background skills with which they enter the program.
It is a coursework masters program where students do coursework during their first year, followed by a dissertation component. The main structure allows for 90 credits of coursework and a 90 credit dissertation. We are offering an additional alternative option of 120 credits coursework and 60 credit dissertation from 2020. The structure of the program is attached.
The masters program in Advanced Analytics is closely aligned to the Data Science program and is aimed at students who come in excellent honours level Statistics.
Ideal Students
Honours or four-year Bachelor graduates from across the spectrum of disciplines, who want to gain a broad understanding of the issues involved in climate change and sustainable development from an African and developing world perspective, and those wanting a comprehensive introduction to climate change issues before embarking on a PhD. The course will equip its graduates for employment in government, local authorities, businesses with a sustainability agenda, consultancies, NGOs and international development organisations. It is aimed at both recent graduates and those with several years' experience who wish to engage with these pressing issues within the context of their vocations.
Admissions
Curriculum
Courses should be selected subject to meeting entrance requirements and consent of Programme convenor.
Core Courses:
Databases for Data Scientists | CSC5007Z | 12 credits |
Visualization | CSC5008Z | 12 credits |
MIT: Programming in Python | CSC5011Z | 15 credits |
Multivariate Analysis | STA5069Z | 15 credits |
Data Science for Industry | STA5073Z | 12 credits |
Statistical and High-Performance Computing | STA5075Z | 18 credits |
Supervised Learning | STA5076Z | 12 credits |
Unsupervised Learning | STA5077Z | 12 credits |
Exploratory Data Analysis | STA5092Z | 12 credits |
Elective Courses:
Data Science for Astronomy | AST5004Z | 12 credits |
Data Science for Particle Physics | PHY5007Z | 12 credits |
Bioinformatics for high-throughput biology | IBS5004Z | 15 credits |
Data Science for Industry | STA5073Z | 12 credits |
Decision Modelling for Prescriptive Analytics | STA5074Z | 12 credits |
Bayesian Decision Modelling | STA5061Z | 15 credits |
Data Analysis for High-Frequency Trading | STA5091Z | 15 credits |
Data Visualization | CSC5008Z | 12 credits |
Programming in Python | CSC5011Z | 12 credits |
Advanced Regression | STA5090Z | 15 credits |
Machine Learning | STA5068Z | 15 credits |
Advanced Portfolio Theory | STA5086Z | 15 credits |
Simulation & Optimization | STA5071Z | 15 credits |
Longitudinal Data Analysis | STA5067Z | 15 credits |
Survival Analysis | STA5072Z | 15 credits |
South African Financial Markets | FTX5040F | 15 credits |
Risk Management of Financial Instruments | FTX5051S | 15 credits |
Financial Systems Design | INF5006S | 15 credits |
Topics in Financial Management | FTX5028W | 30 credits |
Capital Markets & Financial Instruments | FTX5043F | 30 credits |
Empirical Finance | FTX5044H | 30 credits |
Fintech & Cryptocurrencies | ECO5037S | 24 credits |
Applied Time Series Analysis | ECO5096S | 15 credits |
Microeconomics | ECO5070S | 15 credits |
Advanced Econometrics | ECO5046F | 15 credits |
Programme Configurations
There are two programme configurations:
Data Science Programme (90 credit coursework/90 credit dissertation)
The curriculum comprises of core courses adding to 66 credits, elective courses adding to at least 24 credits and a minor dissertation counting 90 credits.
Students will choose a minimum of 2 elective courses to bring the total number of elective coursework credits to a minimum of 24 NQF credits. Available electives will depend on staff availability and not all electives will be offered each year. Students may choose to take electives from the list of core courses, or from the list of elective courses subject to satisfying the entrance requirements for the chosen courses and consent of course and programme conveners, or from courses from other departments subject to consent of the course and programme conveners.
The minor dissertation component (90 NQF credits) is a research project based on a selected research topic. Students may register for a minor dissertation from the available options listed below. Students registering for the dissertation component in a Faculty other than the host Faculty (which administers the course) will be subject to the examination criteria of that Faculty.
Minor Dissertation options include:
Data Science in Statistical Sciences | STA5079W | 90 credits |
Data Science in Astronomy | AST5005W | 90 credits |
Data Science in Bioinformatics | IBS5005W | 90 credits |
Data Science in Computer Science | CSC5009W | 90 credits |
Data Science in Physics | PHY5008W | 90 credits |
Minor Dissertation in Finance | FTX5003W | 90 credits |
Data Science Programme (120 credit coursework/60 credit dissertation)
The curriculum comprises of core courses adding to 66 credits, elective courses adding to at least 24 credits and a minor dissertation counting 90 credits.
Students will choose a minimum of 2 elective courses to bring the total number of elective coursework credits to a minimum of 24 NQF credits. Available electives will depend on staff availability and not all electives will be offered each year. Students may choose to take electives from the list of core courses, or from the list of elective courses subject to satisfying the entrance requirements for the chosen courses and consent of course and programme conveners, or from courses from other departments subject to consent of the course and programme conveners.
The minor dissertation component (90 NQF credits) is a research project based on a selected research topic. Students may register for a minor dissertation from the available options listed below. Students registering for the dissertation component in a Faculty other than the host Faculty (which administers the course) will be subject to the examination criteria of that Faculty.
Minor Dissertation options include:
Minor Dissertation in Financial Management | FTX5029W | 60 credits |
Minor Dissertation | ECO5023W | 60 credits |
Minor Dissertation in Statistical Sciences | STA5093W | 60 credits |