Science

Master of Science in Mathematical Statistics

The Master of Science in Mathematical Statistics is a postgraduate programme that prepares professionals for advanced practice in statistical theory, mathematical statistics, and data-driven decision-making. The programme combines mathematical statistics theory with computational methods, probability theory, and statistical inference.

Core areas include probability theory, statistical inference, mathematical statistics, stochastic processes, linear models, multivariate analysis, Bayesian statistics, time series analysis, research methods, and thesis. Students engage with both theoretical and practical work through coursework, computational projects, and research.

The programme is offered by the Technical University of Kenya through the School of Mathematics and Actuarial Science. TUK is a public university offering the programme over five semesters through full-time and part-time modes, including evening and regular sessions.

Students develop competencies in mathematical statistics, probability theory, statistical inference, stochastic processes, and computational statistics. The programme includes coursework, examinations, thesis, and an optional Postgraduate Diploma exit point after the first year, preparing graduates for analytical and research roles across multiple sectors.

TUK charges approximately KES 202,000/year for Group II (Pure and Applied Science) postgraduate programmes, including tuition of KES 170,000 and statutory fees of approximately KES 32,000. Entry requires at least a Second Class Honours (Upper Division) degree with at least 12 units in Mathematics from a recognised institution.

Graduates pursue careers as statisticians, data scientists, biostatisticians, risk analysts, quantitative analysts, and lecturers in statistics across research institutions, government agencies, financial institutions, healthcare organisations, and universities.

Skills Required

Mathematical Statistics and Probability Theory
Statistical Inference and Hypothesis Testing
Stochastic Processes and Modelling
Linear and Multivariate Statistical Models
Bayesian Statistics and Data Analysis
Time Series Analysis and Forecasting
Computational Statistics and Programming
Research Methods in Statistics
Data-Driven Decision Making
Academic Writing and Thesis Research

Key Subjects

Probability Theory
Statistical Inference
Mathematical Statistics
Stochastic Processes
Linear Models and Regression
Multivariate Statistical Analysis
Bayesian Statistics
Time Series Analysis and Forecasting
Research Methods in Statistics
Thesis Research

Certifications

ISI Professional Membership
IMS Professional Membership

Specializations

Mathematical Statistics

Focuses on mathematical statistics, covering theory, inference, probability, distributions, and managing mathematical statistics.

Biostatistics

Examines biostatistics, covering biology, medicine, health, trials, and applying statistics in biological sciences.

Stochastic Processes

Covers stochastic processes, covering processes, probability, randomness, modelling, and managing stochastic processes.

Bayesian Statistics

Focuses on Bayesian statistics, covering inference, priors, posteriors, computation, and managing Bayesian analysis.

Time Series Analysis

Examines time series analysis, covering forecasting, trends, seasonality, modelling, and managing time series.

Multivariate Analysis

Covers multivariate analysis, covering methods, models, regression, classification, and managing multivariate data.

Duration: 2 years
Public, up to: Ksh 201,000
Private, up to: Ksh 295,500
Job market: High

The programme

What you study, how long it takes, and how it is delivered.

Practicalities

Study mode: Full-time, Part-time, Evening
Attachment: 0 months
Average class: 15 students
Award: Masters

What you study

10 subjects

Probability Theory
Statistical Inference
Mathematical Statistics
Stochastic Processes
Linear Models and Regression
Multivariate Statistical Analysis
Bayesian Statistics
Time Series Analysis and Forecasting
Research Methods in Statistics
Thesis Research

Modules

12 in the programme

Probability Theory
Year 1Semester 13 creditsCore
Examines distributions, probability, theorems, limits, and understanding probability theory.
Statistical Inference
Year 1Semester 13 creditsCore
Covers estimation, hypothesis testing, confidence intervals, likelihood, and managing statistical inference.
Mathematical Statistics
Year 1Semester 13 creditsCore
Examines theory, methods, models, distributions, and managing mathematical statistics.
Research Methods in Statistics
Year 1Semester 13 creditsCore
Covers research design, data collection, analysis, methods, and conducting research, preparing students for their thesis.
Stochastic Processes
Year 1Semester 23 creditsCore
Examines processes, Markov chains, Poisson, randomness, and managing stochastic processes.
Linear Models and Regression
Year 1Semester 23 creditsCore
Covers regression, ANOVA, models, estimation, and managing linear models.
Multivariate Statistical Analysis
Year 1Semester 23 creditsCore
Examines methods, PCA, classification, clustering, and managing multivariate data.
Bayesian Statistics
Year 1Semester 23 creditsCore
Covers inference, priors, posteriors, computation, and managing Bayesian analysis.
Time Series Analysis and Forecasting
Year 2Semester 13 creditsCore
Examines forecasting, trends, seasonality, ARIMA, and managing time series.
Computational Statistics
Year 2Semester 13 creditsCore
Covers computing, programming, simulation, algorithms, and managing computational statistics using R, Python, and MATLAB.
Statistical Consulting and Applications
Year 2Semester 13 creditsCore
Examines consulting, applications, communication, projects, and applying statistics in real-world contexts.
Research Thesis
Year 2Semester 29 creditsCore
Original supervised research thesis on a mathematical statistics topic, demonstrating mastery of research methods and statistical knowledge, assessed through written submission and oral defence.

Specialisations

Mathematical Statistics
Focuses on mathematical statistics, covering theory, inference, probability, distributions, and managing mathematical statistics.
Biostatistics
Examines biostatistics, covering biology, medicine, health, trials, and applying statistics in biological sciences.
Stochastic Processes
Covers stochastic processes, covering processes, probability, randomness, modelling, and managing stochastic processes.
Bayesian Statistics
Focuses on Bayesian statistics, covering inference, priors, posteriors, computation, and managing Bayesian analysis.
Time Series Analysis
Examines time series analysis, covering forecasting, trends, seasonality, modelling, and managing time series.
Multivariate Analysis
Covers multivariate analysis, covering methods, models, regression, classification, and managing multivariate data.

A day as a student

A typical day during the MSc in Mathematical Statistics programme combines lectures, computational labs, project work, seminars, and independent study. Sessions cover probability theory, statistical inference, mathematical statistics, stochastic processes, and Bayesian statistics. Probability theory sessions examine distributions, probability, theorems, limits, and understanding probability. Statistical inference sessions cover estimation, hypothesis testing, confidence intervals, likelihood, and managing inference. Mathematical statistics sessions cover theory, methods, models, distributions, and managing mathematical statistics. Stochastic processes sessions cover processes, Markov chains, Poisson, randomness, and managing stochastic processes. Linear models sessions cover regression, ANOVA, models, estimation, and managing linear models. Multivariate analysis sessions cover methods, PCA, classification, clustering, and managing multivariate data. Bayesian statistics sessions cover inference, priors, posteriors, computation, and managing Bayesian analysis. Time series sessions cover forecasting, trends, seasonality, ARIMA, and managing time series. Research methods sessions prepare students for their thesis, covering research design, data collection, and analysis. Computational labs provide hands-on experience with R, Python, and MATLAB for statistical computing. Project work provides hands-on experience with data analysis, statistical modelling, and computational methods. Seminars provide opportunities for presenting research findings and debating current issues in mathematical statistics. Guest lectures from experienced statisticians, data scientists, and researchers provide practical insights. The programme culminates in a supervised research thesis on a mathematical statistics topic.

The trade offs

In its favour

High demand for statistics professionals with Kenya's growing data economy and financial sector.
TUK offers competitive fees at approximately KES 202,000/year for Group II programmes.
Programme offers both full-time and part-time modes with evening and regular sessions.
Students can exit with a Postgraduate Diploma after the first year if desired.

Against it

Only one university (TUK, public) confirmed offering this specific programme name.
No private university confirmed offering this specific programme.
Programme runs over 5 semesters with possible additional charges for the extra semester.
Programme requires strong mathematics background with at least 12 units in Mathematics.

What it costs

Tuition at both ends of the market, and how to pay for it.

What it costs, and where

Against 243 science courses

Public201k to 201k

201k at Technical University of Kenya201k at Technical University of Kenya

Private150k to 296k

150k at Kabarak University296k at USIU-Africa

Annual tuition in Kenyan shillings, rounded. The upright tick is the median for this field, so a bar sitting entirely to its right is an expensive programme by the standards of its own subject.

The fine print

TUK Group II ~201K/yr (eafinder.com). USIU ~295K/yr (usiu.ac.ke). Kabarak ~150K/yr.

HELB postgraduate loans are available for Kenyan students. TUK may offer scholarships for eligible students. ISI may offer grants for statistics research. Some research institutions, government agencies, and industry bodies may sponsor staff for postgraduate study.

Funding options

HELB Postgraduate Loan
TUK Scholarship
ISI Grant

Scholarships

3 recorded

HELB Postgraduate Loan
LoanKsh 200,000Kenyan
Kenyan students pursuing postgraduate studies at recognised universities.
TUK Scholarship
ScholarshipKsh 202,000Kenyan
Technical University of Kenya offers scholarships for eligible postgraduate students.
ISI Grant
ScholarshipKsh 100,000
ISI may offer grants for statistics research and education.

Getting in

The grades, the alternatives, and who accredits the award.

What you need

KCSE mean grade: N/A (Postgraduate)
Alternative entry: TUK requires at least a Second Class Honours (Upper Division) degree with strong mathematics background (at least 12 units in Mathematics). Lower Second Class may be considered with a Postgraduate certificate and two years of relevant work experience. Contact TUK for specific admission requirements.

How you are assessed

4 components

Coursework and Continuous Assessment
Coursework30% of the mark
Continuous assessment through coursework assignments, computational projects, seminar presentations, and class participation.
Written Examinations
Examination70% of the mark
Written examinations covering probability theory, statistical inference, mathematical statistics, and stochastic processes.
Computational Project Assessment
Project40% of the mark
Practical assessment through computational projects, data analysis, statistical modelling, and demonstrating statistical skills.
Research Thesis
Research100% of the mark
Original supervised research thesis on a mathematical statistics topic, demonstrating mastery of research methods and statistical knowledge, assessed through written submission and oral defence.

Accreditation

The programme is accredited by the Commission for University Education (CUE). Technical University of Kenya (public) offers MSc in Mathematical Statistics through the School of Mathematics and Actuarial Science. The programme meets CUE standards for postgraduate training in mathematical statistics. Graduates are eligible for ISI and IMS professional memberships.

Accredited by

Commission for University Education (CUE)
Academic accreditationRequired
Programme accredited by CUE. Technical University of Kenya (public) offers MSc in Mathematical Statistics through the School of Mathematics and Actuarial Science. The programme meets CUE standards for postgraduate training in mathematical statistics. Graduates are eligible for ISI and IMS professional memberships.

Where it leads

The roles it opens, and what you leave with.

Where graduates go

6 roles

Statistician
High demandKsh 100,000 to Ksh 450,000
Conducts statistical analysis, overseeing data, modelling, inference, and managing statistics.
Data Scientist
High demandKsh 120,000 to Ksh 500,000
Applies data science methods, overseeing analytics, ML, modelling, and managing data science.
Biostatistician
Moderate demandKsh 100,000 to Ksh 450,000
Applies statistics in biology and health, overseeing trials, analysis, modelling, and managing biostatistics.
Risk Analyst
High demandKsh 100,000 to Ksh 450,000
Conducts risk analysis, overseeing assessment, modelling, quantification, and managing risk.
Quantitative Analyst
High demandKsh 120,000 to Ksh 500,000
Develops quantitative models, overseeing modelling, analysis, analytics, and managing quantitative analysis.
Statistics Lecturer
Moderate demandKsh 100,000 to Ksh 450,000
Teaches statistics at university or college level, overseeing instruction, research, and academic supervision.

Graduate outcomes

Where these fields lead

8 careers

Tools you will learn

R
SoftwarePrimary
R for statistical computing and analysis, covering data, statistics, modelling, and managing statistical analysis.
Python
Software
Python for data analysis and scientific computing, covering data, modelling, analysis, and managing computational statistics.
MATLAB
Software
MATLAB for mathematical computing and modelling, covering modelling, simulation, analysis, and managing computational statistics.
SPSS
Software
SPSS for statistical analysis, covering data, statistics, analysis, and managing research data.

Industry links

Common misconceptions

Mathematical statistics is just about calculating averages.
Mathematical statistics covers comprehensive probability theory, inference, stochastic processes, and Bayesian methods beyond just calculating averages.
This programme is only for mathematicians.
Statistical skills are valuable for biologists, economists, engineers, and data scientists beyond just mathematicians.
Statistics is just about spreadsheets and charts.
Mathematical statistics covers comprehensive theory, inference, modelling, and computational methods beyond just spreadsheets and charts.
Bayesian statistics is just about guessing.
Bayesian statistics covers rigorous inference, priors, posteriors, computation, and analysis beyond just guessing.
Statistics careers are limited in Kenya.
Kenya's growing data economy, financial sector, and healthcare research create high demand for statistics professionals.
Stochastic processes are just about randomness.
Stochastic processes cover comprehensive Markov chains, Poisson processes, modelling, and applications beyond just randomness.