Position : Lecturer
Telephone : 035 902 6387
Email : SitholeMT@unizulu.ac.za
Office : Office SC315, Mathematical Sciences Department, Natural Sciences Building, KwaDlangezwa Campus
Biography:
Mr. Mthunzi Sithole is a dedicated Statistics Lecturer with teaching experience across undergraduate and postgraduate levels. He previously served at the University of Mpumalanga, Mbombela Campus, as an undergraduate lecturer. He is currently serving at the University of Zululand (UNIZULU – KwaDlangezwa Campus) as a permanent lecturer for both undergraduate students (from first to third level) and postgraduate students.
His work includes delivering lectures, supervising postgraduate students, designing and marking assessments, facilitating statistical support workshops, serving as an internal and external examiner, and coordinating SI learning activities. He is committed to academic leadership, student development, and enhancing learner support through technology-enhanced teaching and structured mentorship.
Lecture ‘s the following modules:
- MATHEMATICS AND STATISTICS FOR EARTH AND LIFE SCIENCES
- ELEMENTARY STATISTICS FOR SCIENCE AND COMMERCE STUDENTS
- RANDOM PROCESSES
- TIME SERIES ANALYSIS
- STOCHASTIC PROCESSES
Qualifications:
- PhD in Statistics (in progress) – University of KwaZulu-Natal
- MSc in Statistics (Highest qualification) – University of KwaZulu-Natal
BSc Honours in Statistics – University of KwaZulu-Natal
- BSc in Applied Mathematics and Statistics – University of KwaZulu-Natal
Honours / Awards
- SAS Academic Specialization in Statistics & Data Analytics (Certificate)
- University Teaching Assistant Programme Certificate – STASH Achievement
- Computer Literacy Certificate
- The Non-Technical Skills of Effective Data Scientists
Research interests:
His current doctoral research focuses on a Statistical Study on Anaemia, Malaria, and Malnutrition in sub-Saharan Africa. His study interrogates the statistical associations, co-occurrence patterns, and epidemiological determinants of these three major public health burdens affecting vulnerable populations. Through advanced biostatistical modelling, multivariate analysis, disease mapping, and inferential techniques, the project aims to quantify spatial inequalities, assess risk factors, and contribute predictive insights that can support health policy improvements, targeted interventions, and long-term disease-reduction strategies in the region. The PhD further integrates empirical health data from multiple African countries to explore how nutritional deprivation, parasitic infection, and haematological deficiencies interact and compound morbidity impacts.
Past Projects
His Master’s research focused on Time Series Analysis and Forecasting, applying multivariate and seasonal modelling techniques to real-world fuel usage temporal datasets to enhance forecasting accuracy and inform decision-support systems. The project included comparative analysis of forecasting models and diagnostic evaluation of stochastic trends.
His past engagements further include postgraduate tutoring, SI mentoring, and applied statistics academic support for undergraduate cohorts across various UKZN departments, where he facilitated statistical interpretation, classroom engagement, analytical skill-building, and revision support.
In addition, in October 2023, he served as an Assistant Teacher for Mathematics and Statistics for the Grade 12 Final Push at PMB Camp Schools in Pietermaritzburg, conducting intensive past-paper revision and examination preparation workshops to support students’ final assessment performance.