UCT Data Science
UCT Data Science,
Masters in Data Science
Masters in Data Science
This is an interdisciplinary programme with participating departments: Statistical Sciences, Computer Science, Astronomy, Physics, and the Computational Biology group (Health Sciences 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 as a Master of Science specialising in Data Science.
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
Short Course: Data Science for Industry
Date: 23 July 2021 – 5 September 2021(lectures Monday, Wednesday 4-6pm)
Venue: UCT upper campus
Course fee: ZAR 6000
“Data Science for Industry” is a 24-lecture module in the MSc in Data Science program at the University of Cape Town. This year a small number of places are available for those who would like to take the module as a short course but are not registered at UCT.
The course provides an applied, hands-on overview of selected topics useful in the working world of data science that are not covered by other modules in the program. Broadly speaking these topics fall into two themes: workflow/productivity tools and skills (GitHub, data wrangling, visualization, creating R packages, Shiny applications) and modelling (recommender systems, text mining, neural networks). For a full list of topics covered and last year’s lecture materials, see https://github.com/iandurbach/datasci-fi.
The course consists of 12 double lectures, taking place on Monday and Wednesday 4-6pm for six weeks, starting 23 July 2021 and ending 5 September. Roughly half of the lectures are in traditional lecture format, with the rest taking the format of a practical/tutorial. In these you would be expected to work through a video lecture before the class meeting, with the lecture time being used for computer practicals and discussion.
The course is conducted in R and to get the most out of the course you should already have at least a working knowledge of R, meaning you would have some experience with reading in data, running statistical analyses (e.g. lm, anova, glm), plotting results, and writing your own functions.
For more information email firstname.lastname@example.org.
To apply for a place on the short course, please complete the application form at https://goo.gl/forms/Nf64RKLz0UvP62243
Applications close 5 July 2020 but as limited places are available you are advised to apply as early as possible.