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Masters Programmes in Data Science

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:

  1. Coursework component (90 credits), followed by a minor dissertation (90 credits)
  2. 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.dpu

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 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

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 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

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 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