Overview

This MA programme aims to train postgraduate students in the use of statistical methods to conduct data-driven research in the social sciences and humanities and will create opportunities for students to develop an interdisciplinary perspective on the emerging fields of Data Science.

The curriculum prepares candidates in Social Sciences and Humanities for the growing areas of data science. This contributes to national priorities such as improving service delivery and developing a competitive knowledge economy, and it creates opportunities for graduates as professional researchers in academia and the public and private sectors.

The programme forms part of the DSTI-funded National e-Science Postgraduate Teaching and Training Platform (NEPTTP). Students will register with their Home Institution but will attend the coursework component at Wits University in Johannesburg, Gauteng, in the first year. On completion of the coursework modules, students will move back to their Home Institutions for their second year of study.

The Masters programme extends over eighteen to twenty-four months of full-time study. 

Funding

Competitive DSTI-NICIS bursaries, covering tuition, accommodation and stipend, are made available by the Department of Science, Technology, and Innovation (DSTI) to qualifying offer holders with a record of excellent academic achievement. Priority for bursaries will be given to South African Citizens and Permanent Residents.

Click here for more information.

Career Opportunities

Career opportunities vary depending on graduates’ areas of specialisation in the social sciences or humanities, but they fall in two main categories. 

The first consists of newly emerging data-oriented research positions that explicitly target those with expertise in the social sciences and humanities — in academic institutions, social and policy research organisations (governmental and non-governmental), and the private sector (for example, in the legal, finance, health care, and technology industries).

The second consists of positions that have traditionally targeted social science and humanities graduates, but in which data and computing expertise is increasingly valued as a complementary “scarce skill.”  The competitive advantage of MA graduates is their unique ability to combine expertise in the social sciences or humanities with data-oriented research skills.

Curriculum

The programme comprises compulsory and elective modules (with alternative MSc courses available by special permission to students who meet the prerequisites). Cross-disciplinary data-driven projects are offered both within the University and from a wide range of industry partners. A candidate must undertake modules to the value of 180 credits and must successfully complete the following courses to obtain a Master of Science by Coursework and Research Report in the field of e-Science.

Compulsory Coursework Modules (Year 1 at Wits University)

• Research Methods and Capstone Project in Data Science (15 credits)
  This course gives the students the theoretical and practical skills to plan, conduct, analyse and present a scientific assignment (Capstone Project) in the area of Data Science by introducing them to research methodology, ethics and sustainability. The course is comprised of three parts: 1) scientific writing; 2) research methodology; and 3) scientific assignment. These three parts are integrated in a capstone project.
• Data Privacy and Ethics (15 credits)
  This course introduces the students to the ethical and legal foundations of data science governance. The topics covered include technical processes of data collection, storage, exchange and access; ethical aspects of data management; legal and regulatory frameworks in South Africa and in relevant jurisdictions; data policy; data privacy; data ownership; legal liabilities of analytical decisions, and discrimination; algorithms and technical approaches to enhance data privacy; and relevant case studies.
• Statistical Computing and Inference for the Social Sciences and Humanities (30 credits)
  This course introduces statistical social research, with applications in the social sciences and humanities. It emphasises the development of practical skills for conducting quantitative research using statistical software.
• Statistical Modelling for the Social Sciences and Humanities (15 credits)*
  This course focuses on statistical modelling methods applied in the social sciences and humanities. These include multiple regression models, generalised linear models, multilevel models, and structural equation models. It emphasises the ability to identify appropriate models based on the type of data and research objective, and to replicate and critically analyse applications in the students’ substantive areas of expertise.
• Applied Data Science for the Social Sciences and Humanities (15 credits)
  This course focuses on applying data science methods in the social sciences and humanities, including relevant programming skills. The emphasis is on practical applications, such as compiling and analysing textual and georeferenced data sets.
• Alternative MSc courses are available by special permission to students who meet the prerequisites.

Research Report (Year 2 at Home Institution)

• Research Report: Data Science (90 credits)
  The ability to do research is an essential skill for an individual pursuing a career in Data Science, and forms the basis for further post-graduate study. This module provides practical training for the development of research skills and bridges the gap between theory and practice, and established work and novel research. By working within established research structures in the Institution under the guidance of an expert, students will receive exposure to the methods, philosophy and ethos of research in the field of Data Science.

Entry Requirements

Applicants are required to have a Bachelor’s degree with Honours (NQF level 8 qualification) from a relevant discipline or field in the social sciences or humanities. Along with strong substantive knowledge in a relevant discipline or field, they must have a demonstrable knowledge of basic principles of quantitative social research (but need not have a previous specialisation in statistics or statistical computing).

Applicants require a minimum of 65 percent in their NQF level 8 qualification to be considered, and they must fulfil any additional institutional application requirements of the institution through which they are applying, and must be co-approved by the Consortium. 

University Application Process

University Fees and Funding

Click here to see the current average tuition fees. The Fees site also provides information about the payment of fees and closing dates for fees payments. Once you have applied you will be able to access the fees estimator on the student self-service portal.

For information about postgraduate funding opportunities, including the postgraduate merit award, click here. Please also check your School website for bursary opportunities. NRF bursaries: The National Research Foundation (NRF) offers a wide range of opportunities in terms of bursaries and fellowships to students pursuing postgraduate studies. External bursaries portal: The Bursaries South Africa website provides a comprehensive list of bursaries in South Africa.