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Short Learning Programme / Data Science in Practice
Short Learning Programme

Data Science in Practice

Instructor
Teacher

Prof C Harley

Last updated

18 May, 2026

About Course

Description:

 The Data Science Across Disciplines Research Group from the Faculty of Engineering and the Built Environment is pleased to offer a short learning programme on Data Science in Practice. Tools associated with the Fourth Industrial Revolution – such as Data Analysis, Machine Learning Modelling and Data Visualisation – are becoming increasingly critical in industry, making it imperative that staff upskill in the area of Data Science as a whole. The purpose of this programme is to provide professionals, from diverse backgrounds, with a solid knowledge base in the field of Data Science as required when tackling problems across social, economic and technical fields. This programme is ideal for early career professionals in need of upskilling to become Data Scientists as it offers the necessary theoretical knowledge and practical experience.

Admission Requirements:

This programme is at a South African National Qualification Framework (NQF) Level 8. Applicants require at least a Bachelor of Engineering (BEng) or Bachelor of Science (BSc) in the Mathematical Sciences; other relevant NQF level 7 qualifications will be considered on a case by case basis.

Course Curriculum

The SLP is offered through a combination of online lectures and practical sessions. Supporting study materials will be provided and the following topics will be considered: 

Basics in Python: Packages, Fundamental In-Built Tools and Programming Practises

Data Wrangling: Exploratory Data Analysis Framework, Structuring Data, Cleaning Data, Enriching Data/ Transformations and Validating Data

Supervised Machine Learning: Methods for Regression (Linear, Logistic, K-nearest Neighbours, SVM), Methods of Multi-Class Classification, Ensemble Learning and Neural Networks & Deep Learning Architectures

Model Evaluation and Improvement: Data Leakage, Cross-Validation, Grid-Search Techniques and Model Evaluation Metrics

Unsupervised Machine Learning: Dimensionality Reductions, Clustering and Unsupervised Deep Learning

Privacy Preserving Models: Sensitive Data, Potential Threats and Privacy Preserving Models

Your Instructors

Prof C Harley

Prof C Harley

Instructor

Course Dates

  • Enrollment Opens: Wednesday, 01 Apr 2026
  • Enrollment Closes: Tuesday, 30 Jun 2026
  • Course Starts: Monday, 27 Jul 2026
  • Course Ends: Friday, 30 Oct 2026
Data Science in Practice - Default Course Cover
R 13470.00*

*2026 pricing.

Application Token
FEBESLP
You'll need this token during application
This course includes:
Duration 6 months
Focus Area Digital revolution
Accredited No
NQF Level n/a
Mode of Delivery Scheduled delivery — asynchronous only
Skill Level Beginner
Start Date 7/27/2026