Bachelor of Science (Statistics major)
Overview
This major is offered from the Pietermaritzburg and Westville campuses. The Statistics major equips students with the skills to collect, analyse, interpret, and present data to make informed decisions and solve problems in various fields. It’s about understanding how to draw meaningful conclusions from data, whether it’s for research, business, or other applications.
SAQA
84728
NQF LEVEL
07
DURATION
3 Years
CAO CODE
KN-W-BG4 (Westville Campus)
KN-P-BG4 (PMB Campus)
CLOSING DATE
30 September
DISCIPLINE
Entry Requirements
NSC degree pass with Maths Level 4, English and Life Orientation Level 4 and either Agricultural Science or Life Sciences or Physical Science Level 4. In addition, an Application Performance Score (APS) of at least 30 is required.
Learn how to calculate your APS here.
How to apply?
All first-time South African applicants to UKZN for entry into degree/ diploma study (Grade 12s and undergraduate transfer students from other institutions) must apply via the Central Applications Office (CAO).
Students applying to study in this program should apply for a BSc (General). Students can choose to do this either at the Westville or Pietermaritzburg campuses.
Modules
The advantage of UKZN’s undergraduate BSc general program is that it is versatile. The BSc starts off general in first year with 6 core modules and 2 electives.
Students therefore have the option of taking two modules from a range of electives such as chemistry, physics, geography, and economics. This allows them to re-assess their interests and strengths in second year, where they have the option of changing their majors from Statistics if they do not enjoy these modules.
Semester 1
- Introduction to Calculus – MATH130 (16 Credits)
- Introduction to Statistics – STAT130 (16 Credits)
64 credits may be selected from second major or recommended electives given below:
- Principles of Microeconomics – ECON101 (16 Credits)
- Mechanics, Optics and Thermal Physics – PHYS110 (16 Credits)
Total Credits: 64 Credits
Semester 2
- Calculus and Linear Algebra – MATH140 (16 Credits)
- Statistical Methods – STAT140 (16 Credits)
64 credits may be selected from second major or recommended electives given below:
- Principles of Macroeconomics – ECON102 (16 Credits)
- Electromagnetism, Waves and Modern Physics – PHYS120 (16 Credits)
- Development and Applications Fundamentals – ISTN103 (16 Credits)
- Foundations of Computer Science – COMP107 (16 Credits)
Total Credits: 64 Credits
Semester 1
- Advanced Calculus and Linear Algebra – MATH212 (16 Credits)
- Probability Distributions – STAT230 (16 Credits)
No fewer than 32 credits from Level 2 may be from the second major. Recommended level 2 electives are below:
- Mechanics – MATH235 (16 Credits)
- Discrete Mathematics with Applications – MATH236 (16 Credits)
- Data Mining using Machine Learning – STAT243 (16 Credits)
Total Credits: 64 Credits
Semester 2
- Statistical Inference – STAT240 (16 Credits)
- Further Calculus and Introductory Analysis – MATH251 (16 Credits)
No fewer than 32 credits from Level 2 may be from the second major. Recommended level 2 electives are below:
- Intro to Numerical Mathematics – MATH243 (16 Credits)
- Mathematical Modelling – MATH246 (16 Credits)
- Experimental Design and Analysis – STAT222 (16 Credits)
Total Credits: 64 Credits.
Semester 1
- Linear Models – STAT301 (16 Credits)
- Random Processes – STAT350 (16 Credits)
No fewer than 64 credits from Level 3 modules from second major.
Total Credits: 64 Credits
Semester 2
- Biostatistics Methods – STAT305 (16 Credits)
- Applied Probability Models – STAT395 (16 Credits)
No fewer than 64 credits from Level 3 modules from second major.
Total Credits: 64 Credits.
- Total Credits for Qualification: 384
What do they actually do?
Graduates from this degree are equipped with both the theoretical and computational expertise needed to extract value from data across Banking, Business, Government and Industry. They combine statistical reasoning, mathematical modelling and advanced programming skills to analyse complex datasets, build predictive models, and develop intelligent systems that support strategic decision-making.
They play a critical role in transforming raw data into actionable insights, helping organisations identify trends, optimise processes, manage risk, and stay competitive in data-driven environments.
Typical responsibilities of the job include:
- Designing and managing data acquisition processes
- Developing algorithms and computational models
- Analysing structured and unstructured data
- Building predictive and machine learning models
- Applying statistical and mathematical techniques to complex real-world problems
- Designing and implementing data management systems and software solutions
- Visualising and communicating insights to stakeholders
- Acting in a technical or strategic consultancy capacity
Areas of Employment
This multidisciplinary skill set prepares graduates for careers in data science, artificial intelligence, quantitative analytics, software development, financial modelling, and research. Here are some examples of employers and types of companies in South Africa and abroad that hire these graduates:
- Financial Services & Banking (FNB, Standard Bank, ABSA, Sanlam)
- Technology & Digital Companies (Takealot, Media24, Microsoft)
- Retail, FMCG & Consumer Analytics (Mr Price, Woolworths, Spar, Checkers)
- Telecommunications (MTN, Vodacom)
- Insurance Companies (Discovery)
Types of Roles
Graduates with this multidisciplinary degree could be employed in roles such as:
- Data Scientist/Data Analyst
- Machine Learning Engineer/AI Specialist
- Business Intelligence Analyst
- Quantitative Analyst
- Software Developer or Data Engineer
- Analytics Consultant
Apply today and take the next step toward your future.