Masters in Data Science

This program was designed to address the gap in the skills required by industry by producing graduates who are analytically equipped for the task of deriving and effectively communicating actionable insights from a vast quantity and variety of data emanating from various sectors.

About the Program and Curriculum:

This is a two-year program that results in a Masters degree (NQF Exit Level 9). The modules cover a range of advanced statistical techniques, customer intelligence analytics and data-driven Internet-of-Things analysis as well as statistical software. The program has a balance between theory and application. Furthermore, bootcamps are held to expose the students to a range of software.

This program is facilitated by the Discipline of Statistics, which is home to a diverse group of academics specializing in a wide array of Statistics and Data Science teaching and research areas.

How the program runs:

A hybrid approach has been adopted where online lectures take place after hours (from 6pm) on weekdays, with on-campus sessions every few weeks. These contact sessions can be used for teaching, practicals and assessments. All exams take place on campus. Therefore, this program is not fully online.

Cost of the program:

Fees are charged per module. Click on the following for the fees for year 1 and year 2 in 2025. Please note that these fees will be subject to a slight increase each year.

International students are required to pay an additional levy. More information on this can be found here.

IMPORTANT: If accepted into the program, a registration fee of approximately R5950 (amount subject to change each year) must be paid by the deadline (typically mid-February) to complete your registration into the program. This registration fee gets deducted off your fees for the year. See more info about financial clearance here. International students are required to pay full fees as well as an international levy.

Curriculum:

Two modules are taken per semester for the first 3 semesters. In addition, a research project (mini-dissertation), which contributes to half of the credits for the degree, is done in year 2. The curriculum is as follows:

Year 1

Semester 1:

Optimization and Recommendation Analysis (STAT802)

Aim: To introduce and demonstrate through hands-on practice how to use different tools of optimization and recommendation applied to an array of events in data.

Content: Optimization methods; network algorithms; queuing analysis; the transportation model; incremental response analysis; recommendation systems.

Data Reduction and Latent variable analysis (STAT804)

Aim: To provide students with hands-on experience in understanding when and how to utilize the multivariate data reduction techniques and latent variable analytic tools.

Content: Data reduction methods; partial least squares; principal covariates regression; latent variable models; item response models—continuous latent variable outcome; latent class models- discrete latent variable outcome; mimic models; correspondence analysis and biplots; canonical correlations.

Semester 2:

IoT Analytics (STAT801)

Aim: The module will enable student to leverage their IoT analytical insights that will drive valuable and timely business actions.

Content: The module covers the full IoT analytics life cycle: data capture, integration, visualization, understanding the signals, analytics and deployment.

Research Methods for Data Science (STAT806)

Aim: To equip students with the competence to execute the overall process of designing a research study from its inception to its report.

Content: This module will cover the nature and characteristics of business problems; business analytics research design; scientific communication skills; understand the strategies of disseminating scientific findings in oral and written formats such as presentations, research reports and papers, advanced concepts and techniques in, data warehousing, text mining, cloud analytics.

Year 2

Semester 1:

Survival analysis and Retention Modelling (STAT803)

Aim: To acquaint students with the knowledge and skills for the analysis of survival data.

Content: Data mining approach for survival analysis and retention modelling; Time to first event based survival models-Cox PH and Accelerated failure time models; Discrete time/ interval censored models; Frailty models; Time dependent variables and non proportional hazard rate models; Competing risk models; Recurrent event models; Multistate models; Time-dependent ROC analysis; Application to credit scoring.

Contemporary topics in Business analytics (STAT805)

Aim: To provide students with knowledge and skills of advanced and recent topics of business analytics.

Content: Selected material in advanced and recent topics in business analytics.

Year-long:

Research Project in Data Science (STAT8RP)

Aim: To give students the opportunity to conduct a real-world data science project using data from business, industry and government organizations.

Content: Students work independently to understand the business problem, and then clean and analyze the data to achieve the necessary balance given the needs of the project.

Entrance Requirements:

Honours degree in Statistics, or
Honours degree in Data Science (which covers sufficient statistical methodology at Honours level), or
Postgraduate Diploma in Data Science

Please note that as this is an analytical Data Science Masters program (as opposed to computational Data Science), sufficient statistical methodological knowledge is required. Thus, an Honours in purely Computer Science (or similar) will not be considered. If you are interested in pursuing this program but do not yet meet the entrance requirement, then please consider our Postgraduate Diploma in Data Science.

How to Apply:

Apply online here. Applications usually open around mid-year and close in September. Please remember to upload all documentation requested and pay the required application fee. International students can view which documents are required here.

Course Code: MSC-DS

PLEASE NOTE: For your application to be considered, the application fee must be paid upon applying. See here for more information on the application fee and banking details. Please take note of the payment reference to be used. Please submit proof of payment of the application fee by submitting a ticket here.

Selection Process:

In December, applicants external to UKZN are emailed requesting them to submit their academic record and proof of qualifications.

Spaces are limited and selection is purely based on academic performance. The applicant's credit weighted average (CWA) for their qualifying Honours degree or Postgraduate diploma (as per the entrance requirements above) is determined. Applicants are then ranked according to their CWA and the top candidates are selected. Applicants are informed of the outcome in early January. Lectures usually start in the second week of February.

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