Postgraduate Diploma in Data Science
Overview
This is a two-year program that results in a Postgraduate Diploma (NQF Exit Level 8). The modules were designed to complement one another to form a coherent program with a focus on producing well-rounded professionals in analytical Data Science. The modules cover a range of statistical and machine learning concepts as well as statistical software. Focus is on the application of the methodology rather than the theory. 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.
SAQA
117040
NQF LEVEL
08
DURATION
2 Years
CLOSING DATE
31 October
Why it's was developed?
In our current data era, more and more jobs are involving working with data, where graduates from different academic backgrounds are challenged to turn overwhelming amounts of data into actionable insights for industries. Consequently, it was identified that industries are in need of an analytical skill enhancement program for such employees. Similarly, the employees themselves may also be interested in enhancing their analytical skills for their own career development, either within their current industry or beyond.
The Postgraduate Diploma (PGDip) in Data Science was thus developed to allow graduates with industry experience and a strong passion for data management and analytics to enhance their skills in the field of Data Science. This PGDip also serves as a “bridge” to the Masters in Data Science for graduates from Engineering, Commerce and Science who are interested in pursing a career in Data Science or who wish to upskill in this field.
Entry Requirements
- Applicants are eligible to apply to register for the qualification of Postgraduate Diploma in Data Science if they have previously been awarded a Bachelor degree in Commerce, Engineering or Science with at least two years industry data management experience, displayed in a “Portfolio of Evidence” assessed as satisfactory by the Discipline.
Programme Structure
A hybrid approach has been adopted where online lectures take place after hours (from 6pm) on weekdays, with at least one on-campus sessions each semester. 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 Structure
Fees are charged per module. Click on the following for the fees for year 1 and year 2 in 2026. 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 R7300 (amount subject to change each year) must be paid by the deadline (typically by the first week of 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.
Portfolio of Evidence and Selection Process
In November, applicants are emailed requesting them to submit their CV, Proof of Qualification and Portfolio of Evidence. They are supplied with a template to outline their industry experience in data management/analytics (this forms the Portfolio of Evidence).
The selection committee reviews the applications during the course of December. Spaces are very limited and a careful selection process is followed. Priority is given to applicants who are able to demonstrate sufficient data management/analytics experience in their job (at least two years of experience is required). Online courses and certificates will not count towards this required industry experience.
Applicants are informed of the outcome in early January. If you applied for the program by the cut-off in September but do not receive the email requesting your documentation by the beginning of December, please contact us. Lectures usually start in the second week of February.
Modules
Two modules are taken per semester for the first 3 semesters, followed by an industry project in the last semester. The curriculum is as follows:
Semester 1
- Data Mining: Descriptive Analytics – STAT603 (16 Credits)
- Applied Binary Classification and Matching – STAT606 (16 Credits)
Total Credits: 32 Credits
Semester 2
- Applied Generalized Linear Model Analysis
STAT601 (16 Credits) - Machine Learning and Predictive Modeling Techniques for Business – STAT605 (16 Credits)
Total Credits: 32 Credits
Semester 1
- Time Series and Forecasting Econometrics – STAT602 (16 Credits)
- Applied Longitudinal and Geospatial Analysis – STAT604 (16 Credits)
Total Credits: 32 Credits
Semester 2
- Industry Project in Data Science – STAT6RP (32 Credits)
Total Credits: 32 Credits
- Total Credits for Qualification: 128
Apply today and take the next step toward your future.
