Masters of Science (coursework and minor dissertation)
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).
Entrance Requirements
A mark of at least 65% for a HEQSF level 8 qualification (equivalent to that of a UCT degree) in any discipline that included a substantial research component and at least a first year Statistics course and a first year Computing Course. Students may be required to register for and pass STA1000P (the summer term offering of STA1000) before being allowed to register for the degree. Academic transcripts of applicants will be assessed by a selection committee made up of representatives from the participating departments. Applicants may be called for an interview to assess whether they meet entrance requirements.
Prescribed 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 | 12 credits |
| Multivariate Analysis | STA5069Z | 15 credits |
| Data Science for Industry | STA5073Z | 15 credits |
| Statistical and High-Performance Computing | STA5075Z | 12 credits |
| Supervised Learning | STA5076Z | 18 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 |
There are two programme configurations:
- Coursework component (90 credits), followed by a minor dissertation (90 credits)
- Coursework component (120 credits), followed by a minor dissertation (60 credits)
For more information please contact Celene.Jansen-Fielies@uct.ac.za
STA5080W: Masters in Data Science
This is an interdisciplinary degree with participating departments: Statistical Sciences, Computer Science, Astronomy, Physics, and the Computational Biology group (Health Sciences Faculty). This degree is aimed at students who hold a good honours degree but who do not have an advanced background in Statistics and Computer Science although they have been exposed to mathematics and computing during their undergraduate studies. Students will learn the statistical and computing skills required to deal with Big Data from Astronomy, Physics, Medicine and Commerce. This masters degree is composed of two equally weighted components. STA5080W is the coursework component (90 credits), followed by a 50% dissertation (90 credits) on a selected research topic in one of the following: Data Science in Astronomy (AST5005H), Data Science in Bioinformatics (IBS5004H), Data Science in Computer Science (CSC5009H), Data Science in Physics (PHY5008H) or Data Science in Statistical Sciences (STA5079H). The degree will be open to students with at least 65% for an honours degree in any discipline that involved a substantial component of quantitative and computing training, as assess by a selection committee made up of representatives from the contributing departments. The successful completion of pre-courses as deemed necessary by the selection committee might be required (STA5014Z) before being allowed to register for the degree. Students will be required to pass 5 compulsory and 2 elective modules. The overall mark for the coursework component will be a weighted average (based on contribution towards total credit count) of the marks obtained for the individual modules. Students will be required to pass each individual module in order to pass the coursework component of the degree. The following core modules are compulsory:
| Databases for Data Scientists | CSC5007Z | 12 credits |
| Statistical and High Performance Computing | STA5075Z | 12 credits |
| Data Visualization | CSC5008Z | 12 credits |
| Unsupervised Learning | STA5077Z | 12 credits |
| Supervised Learning | STA5076Z | 18 credits |
In order to complete 90 credits, students can choose from the following elective modules although not all modules will be offered every year; modules offered will depend on staff availability and the course will be tailored to the interests and needs of the particular students.
| Data Science for Astronomy | AST5004Z | 12 credits |
| Data Science for Particle Physics | PHY5007Z | 12 credits |
| Bioinformatics for high-throughput biology | IBS5003Z | 15 credits |
| Data Science for Industry | STA5073Z | 12 credits |
| Decision Modelling for Prescriptive Analytics | STA5074Z | 12 credits |
| Bayesian Decision Modelling | STA5061Z | 15 credits |
Any other masters modules in Statistical Sciences or Computer Science. Specific entry requirements might apply to these modules.
- See more at: http://www.stats.uct.ac.za/stats/study/postgrad/masters#sthash.hnnbmXNZ.dpuSTA5080W: Masters in Data Science
This is an interdisciplinary degree with participating departments: Statistical Sciences, Computer Science, Astronomy, Physics, and the Computational Biology group (Health Sciences Faculty). This degree is aimed at students who hold a good honours degree but who do not have advanced background in Statistics and Computer Science although they have been exposed to mathematics and computing during their undergraduate studies. Students will learn the statistical and computing skills required to deal with Big Data from Astronomy, Physics, Medicine and Commerce. This masters degree is composed of two equally weighted components. STA5080W is the coursework component (90 credits), followed by a 50% dissertation (90 credits) on a selected research topic in one of the following: Data Science in Astronomy (AST5005H), Data Science in Bioinformatics (IBS5004H), Data Science in Computer Science (CSC5009H), Data Science in Physics (PHY5008H) or Data Science in Statistical Sciences (STA5079H). The degree will be open to students with at least 65% for an honours degree in any discipline that involved a substantial component of quantitative and computing training, as assess by a selection committee made up of representatives from the contributing departments. The successful completion of pre-courses as deemed necessary by the selection committee might be required (STA5014Z) before being allowed to register for the degree. Students will be required to pass 5 compulsory and 2 elective modules. The overall mark for the coursework component will be a weighted average (based on contribution towards total credit count) of the marks obtained for the individual modules. Students will be required to pass each individual module in order to pass the coursework component of the degree. The following core modules are compulsory:
| Databases for Data Scientists | CSC5007Z | 12 credits |
| Statistical and High Performance Computing | STA5075Z | 12 credits |
| Data Visualization | CSC5008Z | 12 credits |
| Unsupervised Learning | STA5077Z | 12 credits |
| Supervised Learning | STA5076Z | 18 credits |
In order to complete 90 credits, students can choose from the following elective modules although not all modules will be offered every year; modules offered will depend on staff availability and the course will be tailored to the interests and needs of the particular students.
| Data Science for Astronomy | AST5004Z | 12 credits |
| Data Science for Particle Physics | PHY5007Z | 12 credits |
| Bioinformatics for high-throughput biology | IBS5003Z | 15 credits |
| Data Science for Industry | STA5073Z | 12 credits |
| Decision Modelling for Prescriptive Analytics | STA5074Z | 12 credits |
| Bayesian Decision Modelling | STA5061Z | 15 credits |
Any other masters modules in Statistical Sciences or Computer Science. Specific entry requirements might apply to these modules.
- See more at: http://www.stats.uct.ac.za/stats/study/postgrad/masters#sthash.hnnbmXNZ.dpufSTA5080W: Masters in Data Science
This is an interdisciplinary degree with participating departments: Statistical Sciences, Computer Science, Astronomy, Physics, and the Computational Biology group (Health Sciences Faculty). This degree is aimed at students who hold a good honours degree but who do not have advanced background in Statistics and Computer Science although they have been exposed to mathematics and computing during their undergraduate studies. Students will learn the statistical and computing skills required to deal with Big Data from Astronomy, Physics, Medicine and Commerce. This masters degree is composed of two equally weighted components. STA5080W is the coursework component (90 credits), followed by a 50% dissertation (90 credits) on a selected research topic in one of the following: Data Science in Astronomy (AST5005H), Data Science in Bioinformatics (IBS5004H), Data Science in Computer Science (CSC5009H), Data Science in Physics (PHY5008H) or Data Science in Statistical Sciences (STA5079H). The degree will be open to students with at least 65% for an honours degree in any discipline that involved a substantial component of quantitative and computing training, as assess by a selection committee made up of representatives from the contributing departments. The successful completion of pre-courses as deemed necessary by the selection committee might be required (STA5014Z) before being allowed to register for the degree. Students will be required to pass 5 compulsory and 2 elective modules. The overall mark for the coursework component will be a weighted average (based on contribution towards total credit count) of the marks obtained for the individual modules. Students will be required to pass each individual module in order to pass the coursework component of the degree. The following core modules are compulsory:
| Databases for Data Scientists | CSC5007Z | 12 credits |
| Statistical and High Performance Computing | STA5075Z | 12 credits |
| Data Visualization | CSC5008Z | 12 credits |
| Unsupervised Learning | STA5077Z | 12 credits |
| Supervised Learning | STA5076Z | 18 credits |
In order to complete 90 credits, students can choose from the following elective modules although not all modules will be offered every year; modules offered will depend on staff availability and the course will be tailored to the interests and needs of the particular students.
| Data Science for Astronomy | AST5004Z | 12 credits |
| Data Science for Particle Physics | PHY5007Z | 12 credits |
| Bioinformatics for high-throughput biology | IBS5003Z | 15 credits |
| Data Science for Industry | STA5073Z | 12 credits |
| Decision Modelling for Prescriptive Analytics | STA5074Z | 12 credits |
| Bayesian Decision Modelling | STA5061Z | 15 credits |
Any other masters modules in Statistical Sciences or Computer Science. Specific entry requirements might apply to these modules.
- See more at: http://www.stats.uct.ac.za/stats/study/postgrad/masters#sthash.hnnbmXNZ.dpufSTA5080W: Masters in Data Science
This is an interdisciplinary degree with participating departments: Statistical Sciences, Computer Science, Astronomy, Physics, and the Computational Biology group (Health Sciences Faculty). This degree is aimed at students who hold a good honours degree but who do not have advanced background in Statistics and Computer Science although they have been exposed to mathematics and computing during their undergraduate studies. Students will learn the statistical and computing skills required to deal with Big Data from Astronomy, Physics, Medicine and Commerce. This masters degree is composed of two equally weighted components. STA5080W is the coursework component (90 credits), followed by a 50% dissertation (90 credits) on a selected research topic in one of the following: Data Science in Astronomy (AST5005H), Data Science in Bioinformatics (IBS5004H), Data Science in Computer Science (CSC5009H), Data Science in Physics (PHY5008H) or Data Science in Statistical Sciences (STA5079H). The degree will be open to students with at least 65% for an honours degree in any discipline that involved a substantial component of quantitative and computing training, as assess by a selection committee made up of representatives from the contributing departments. The successful completion of pre-courses as deemed necessary by the selection committee might be required (STA5014Z) before being allowed to register for the degree. Students will be required to pass 5 compulsory and 2 elective modules. The overall mark for the coursework component will be a weighted average (based on contribution towards total credit count) of the marks obtained for the individual modules. Students will be required to pass each individual module in order to pass the coursework component of the degree. The following core modules are compulsory:
| Databases for Data Scientists | CSC5007Z | 12 credits |
| Statistical and High Performance Computing | STA5075Z | 12 credits |
| Data Visualization | CSC5008Z | 12 credits |
| Unsupervised Learning | STA5077Z | 12 credits |
| Supervised Learning | STA5076Z | 18 credits |
In order to complete 90 credits, students can choose from the following elective modules although not all modules will be offered every year; modules offered will depend on staff availability and the course will be tailored to the interests and needs of the particular students.
| Data Science for Astronomy | AST5004Z | 12 credits |
| Data Science for Particle Physics | PHY5007Z | 12 credits |
| Bioinformatics for high-throughput biology | IBS5003Z | 15 credits |
| Data Science for Industry | STA5073Z | 12 credits |
| Decision Modelling for Prescriptive Analytics | STA5074Z | 12 credits |
| Bayesian Decision Modelling | STA5061Z | 15 credits |
Any other masters modules in Statistical Sciences or Computer Science. Specific entry requirements might apply to these modules.
- See more at: http://www.stats.uct.ac.za/stats/study/postgrad/masters#sthash.hnnbmXNZ.dpuf