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Masters in Data Science

Masters in Data Science (STA5080W & AST5005H/IBS5005W/CSC5009H/PHY5008H/STA5079H)

This is an interdisciplinary programme with participating departments: Statistical Sciences, Computer Science, Astronomy, Physics, and the Computational Biology group (Health Sciences Faculty) and AIFMRM (Commerce Faculty).  This programme 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 programme 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 (IBS5005W), Data Science in Computer Science (CSC5009H), Data Science in Physics (PHY5008H) or Data Science in Statistical Sciences (STA5079H).  The programme 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 programme.  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 programme. The degree will be awarded either as a Master of Science or a Master of Philosophy specialising in Data Science, depending on the student's academic background and the size of the dissertation component.

There are two streams for this masters: a General stream and a stream specialising in FinTech.

General Stream Structure

The structure of the General stream has more flexibility with the following compulsory core modules:

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 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 12 credits

Any other masters modules in Statistical Sciences or Computer Science. Specific entry requirements might apply to these modules. For more information about the general stream please contact Celene.Jansen-Fielies@uct.ac.za

FinTech Stream Structure (offered jointly with AIFMRM)

The coursework component in this stream comprises core and elective modules to the total of 120 credits.  Students who register for the FinTech stream will graduate with an MPhil specialising in Data Science of Financial Technology.

Core Courses: (66 credits)

Database for Data Scientists CSC5007Z 12 credits
Statistical and High Performance Computing STA5075Z 12 credits
Data Visualization CSC5008Z 12 credits
Supervised Learning STA5076Z 18 credits
Unsupervised Learning STA5077Z 12 credits

Elective Modules: (54 credits)

South African Financial Markets DOC5032F 15 credits
New: Financial Software Engineering DOC5039F 15 credits
New: Fintech and Cryptocurrencies DOC5037F 24 credits

DOC5005W Minor Dissertation (60 credits)

For more information on this stream, go to FinTech Information or contact Co-Pierre.Georg@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 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

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