Science

Master of Science in Statistical Science

The Master of Science in Statistical Science is a postgraduate programme that trains graduates in statistical theory, statistical inference, computational statistics, and data analysis. The programme prepares graduates for advanced careers in biostatistics, financial analysis, market research, econometrics, epidemiology, and statistical programming.

Core areas include probability theory, statistical inference, linear models, Bayesian modelling, stochastic processes, predictive modelling, computational statistics, multivariate analysis, time series, machine learning, and research methods. Students engage with theoretical foundations and practical statistical applications using R, MATLAB, and C++.

The programme is offered by Strathmore University through its Institute of Mathematical Sciences. The programme covers 16 course units over 4 academic semesters plus a dissertation project. Strathmore also offers related MSc Data Science and Analytics and MSc Mathematical Finance and Risk Analytics.

Students develop competencies in statistical modelling, data analysis, Bayesian methods, time series forecasting, machine learning, survey design, and research. The programme includes coursework, computing practicals, examinations, dissertation, and oral defence.

The programme is delivered over two years with evening classes from 5:30pm to 8:30pm on weekdays. Total fee is KES 837,000 paid over 6 semesters. Intake is in May and September. Entry requires First Class or Upper Second in Mathematics or related discipline, or Lower Second with experience.

Graduates pursue careers as statisticians, biostatisticians, data analysts, quantitative analysts, and lecturers in research institutions, financial services, healthcare, government, and universities.

Skills Required

Statistical Inference and Hypothesis Testing
Bayesian Modelling and Data Analysis
Time Series Analysis and Forecasting
Multivariate Statistical Analysis
Predictive Modelling and Statistical Learning
Computational Statistics and Simulation
Machine Learning and Pattern Recognition
Survey Design and Analysis
Statistical Programming in R, MATLAB, and C++
Statistical Science Research Methods

Key Subjects

Probability Theory
Statistical Inference
Linear Models
Bayesian Modelling
Stochastic Processes
Predictive Modelling
Computational Statistics
Multivariate Analysis
Time Series Analysis
Machine Learning

Certifications

RSS Chartered Statistician
ASA Professional Statistician

Specializations

Biostatistics

Focuses on biostatistics, clinical trials, epidemiological methods, survival analysis, longitudinal data, and applying statistics in health and life sciences.

Financial Statistics

Examines financial statistics, risk modelling, econometrics, time series, stochastic processes, and applying statistics in finance and banking.

Computational Statistics

Covers computational statistics, simulation, Monte Carlo methods, optimisation, algorithms, statistical computing, and using computational methods in statistics.

Bayesian Statistics

Focuses on Bayesian modelling, Bayesian inference, MCMC, hierarchical models, prior distributions, and applying Bayesian methods in data analysis.

Machine Learning

Examines machine learning, pattern recognition, statistical learning, classification, regression, clustering, and applying machine learning in data analysis.

Survey Statistics

Covers survey design, sampling methods, survey analysis, complex surveys, weighting, and designing and analysing statistical surveys.

Duration: 2 years
Public, up to: Ksh 352,000
Private, up to: Ksh 305,000
Job market: High

The programme

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

Practicalities

Study mode: Evening
Attachment: 0 months
Average class: 20 students
Award: Masters

What you study

10 subjects

Probability Theory
Statistical Inference
Linear Models
Bayesian Modelling
Stochastic Processes
Predictive Modelling
Computational Statistics
Multivariate Analysis
Time Series Analysis
Machine Learning

Modules

12 in the programme

Probability Theory
Year 1Semester 13 creditsCore
Examines probability axioms, random variables, distributions, limit theorems, characteristic functions, moment generating functions, and understanding probability theory.
Statistical Inference
Year 1Semester 13 creditsCore
Covers point estimation, interval estimation, hypothesis testing, likelihood, sufficiency, asymptotic theory, UMVUE, and making statistical inferences.
Linear Models and Regression Analysis
Year 1Semester 13 creditsCore
Examines linear regression, ANOVA, GLM, model diagnostics, variable selection, leverage, influence, and building and analysing linear models.
Design and Analysis of Surveys
Year 1Semester 23 creditsCore
Covers sampling methods, questionnaire design, weighting, complex surveys, stratification, cluster sampling, and designing and analysing statistical surveys.
Bayesian Modelling and Data Analysis
Year 1Semester 23 creditsCore
Examines Bayesian inference, prior distributions, posterior distributions, MCMC, hierarchical models, Bayesian computation, and applying Bayesian methods in data analysis.
Stochastic Processes
Year 1Semester 23 creditsCore
Covers Markov chains, Poisson processes, Brownian motion, martingales, renewal processes, queueing theory, and understanding stochastic processes.
Predictive Modelling and Statistical Learning
Year 2Semester 33 creditsCore
Examines statistical learning, classification, regression, cross-validation, regularisation, bias-variance trade-off, and building predictive models.
Computational Statistics
Year 2Semester 33 creditsCore
Covers simulation, Monte Carlo, bootstrap, permutation tests, optimisation, numerical methods, R programming, and using computational methods in statistics.
Multivariate Statistical Analysis
Year 2Semester 33 creditsCore
Examines PCA, factor analysis, cluster analysis, discriminant analysis, MANOVA, canonical correlation, and analysing multivariate data.
Time Series Analysis and Forecasting
Year 2Semester 43 creditsCore
Covers ARIMA, GARCH, spectral analysis, forecasting, state-space models, volatility models, and analysing and forecasting time series.
Machine Learning and Pattern Recognition
Year 2Semester 43 creditsCore
Examines pattern recognition, neural networks, SVM, random forests, deep learning, clustering, and applying machine learning in data analysis.
Dissertation Project
Year 2Semester 46 creditsCore
Original dissertation project on a statistical science topic, demonstrating mastery of statistical methods and knowledge, assessed through written submission and oral defence.

Specialisations

Biostatistics
Focuses on biostatistics, clinical trials, epidemiological methods, survival analysis, longitudinal data, and applying statistics in health and life sciences.
Financial Statistics
Examines financial statistics, risk modelling, econometrics, time series, stochastic processes, and applying statistics in finance and banking.
Computational Statistics
Covers computational statistics, simulation, Monte Carlo methods, optimisation, algorithms, statistical computing, and using computational methods in statistics.
Bayesian Statistics
Focuses on Bayesian modelling, Bayesian inference, MCMC, hierarchical models, prior distributions, and applying Bayesian methods in data analysis.
Machine Learning
Examines machine learning, pattern recognition, statistical learning, classification, regression, clustering, and applying machine learning in data analysis.
Survey Statistics
Covers survey design, sampling methods, survey analysis, complex surveys, weighting, and designing and analysing statistical surveys.

A day as a student

A typical day during the MSc Statistical Science programme at Strathmore begins with evening classes from 5:30pm to 8:30pm on weekdays. Students engage with theoretical foundations and practical statistical applications. Probability theory sessions cover probability axioms, random variables, distributions, limit theorems, characteristic functions, and understanding probability. Statistical inference sessions examine point estimation, interval estimation, hypothesis testing, likelihood, sufficiency, asymptotic theory, and making statistical inferences. Linear models sessions cover linear regression, ANOVA, GLM, model diagnostics, variable selection, and building and analysing linear models. Bayesian modelling sessions examine Bayesian inference, prior distributions, posterior distributions, MCMC, hierarchical models, and applying Bayesian methods. Stochastic processes sessions cover Markov chains, Poisson processes, Brownian motion, martingales, and understanding stochastic processes. Predictive modelling sessions examine statistical learning, classification, regression, cross-validation, regularisation, and building predictive models. Computational statistics sessions cover simulation, Monte Carlo, bootstrap, permutation tests, optimisation, and using computational methods in statistics. Multivariate analysis sessions examine PCA, factor analysis, cluster analysis, discriminant analysis, MANOVA, and analysing multivariate data. Time series sessions cover ARIMA, GARCH, spectral analysis, forecasting, state-space models, and analysing and forecasting time series. Machine learning sessions examine pattern recognition, neural networks, SVM, random forests, deep learning, and applying machine learning. Survey design sessions cover sampling, questionnaire design, weighting, complex surveys, and designing and analysing surveys. Computing practicals provide hands-on experience with R, MATLAB, and C++ for statistical modelling, data analysis, simulation, and machine learning. The programme includes an international course module providing one week of international exposure at Academic Centres of Excellence in Europe. Dissertation work involves original research on a statistical science topic. Guest lectures from Strathmore SIMS faculty, KNBS statisticians, industry quantitative analysts, and international statistics experts provide real-world insights. The two-year programme culminates in a dissertation project.

The trade offs

In its favour

Kenya's data revolution, financial services, healthcare research, government statistics, big data, and evidence-based policy create high demand for qualified statistical science professionals.
Strathmore's Institute of Mathematical Sciences offers excellent balance between theory and application, with 16 course units and dissertation using R, MATLAB, and C++.
Career opportunities in biostatistics, financial analysis, market research, econometrics, epidemiology, bioinformatics, demography, and statistical programming.
Programme includes international course module with one week exposure at Academic Centres of Excellence in Europe.

Against it

Programme requires strong mathematics background (First Class or Upper Second in Mathematics or related), which excludes many graduates.
Total fee of KES 837,000 is relatively expensive, limiting accessibility for some students.
Only offered at Strathmore University (private), with no public university option for this exact programme.
Evening programme (5:30-8:30pm) may be challenging for students with other evening commitments.

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

Public296k to 352k

296k at Kenyatta University352k at University of Nairobi

Private305k to 305k

305k at Strathmore University305k at Strathmore University

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

KU 296K/yr ku.ac.ke; UoN ~352K/yr; Strathmore 305K/yr sims.strathmore.edu

HELB postgraduate loans are available for Kenyan students. Strathmore University offers scholarships and financial aid. ISI supports statistical science capacity building. The programme's statistical science focus attracts research, industry, and international funding.

Funding options

HELB Postgraduate Loan
Strathmore University Scholarships
ISI Statistical Science Funding

Scholarships

3 recorded

HELB Postgraduate Loan
LoanKsh 200,000Kenyan
Kenyan students pursuing postgraduate statistical science studies at recognised universities.
Strathmore University Scholarships
ScholarshipKsh 400,000Kenyan
Strathmore University offers scholarships and financial aid for postgraduate students based on academic merit and financial need.
ISI Statistical Science Funding
GrantKsh 200,000
ISI supports statistical science capacity building globally, providing funding for postgraduate study and research in statistics.

Getting in

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

What you need

KCSE mean grade: C+ (KCSE or equivalent)
Alternative entry: The programme accepts graduates from mathematics, statistics, actuarial science, physics, economics, computer science, and related quantitative disciplines. Strathmore requires First Class or Upper Second, or Lower Second with PGD/Certificate or 2 years experience. KCSE C+ minimum. Evening programme with intakes in May and September.

How you are assessed

4 components

Coursework and Continuous Assessment
Coursework30% of the mark
Continuous assessment through coursework assignments, computing practicals, problem sets, data analysis projects, and class participation.
Written Examinations
Examination50% of the mark
Written examinations covering probability, inference, Bayesian modelling, stochastic processes, and computational statistics, conducted at end of each semester.
Computing Practical Assessment
Practical20% of the mark
Assessment of computing practicals, R programming, MATLAB modelling, C++ implementation, data analysis, and statistical computing.
Dissertation Project
Research100% of the mark
Original dissertation project on a statistical science topic, demonstrating mastery of statistical methods and knowledge, assessed through written submission and oral defence.

Accreditation

The programme is accredited by the Commission for University Education (CUE). Strathmore University offers MSc Statistical Science through its Institute of Mathematical Sciences with 16 course units and a dissertation. The programme meets CUE standards for postgraduate statistical science training. KNBS produces official statistics in Kenya, and ISI, ASA, and RSS support statistical science internationally.

Accredited by

Commission for University Education (CUE)
Academic accreditationRequired
Programme accredited by CUE. Strathmore University offers MSc Statistical Science through its Institute of Mathematical Sciences with 16 course units and a dissertation. The programme meets CUE standards for postgraduate statistical science training.

Where it leads

The roles it opens, and what you leave with.

Where graduates go

6 roles

Statistician
High demandKsh 150,000 to Ksh 600,000
Designs and conducts statistical analysis, overseeing data analysis, modelling, inference, and supporting research and decision-making with statistical evidence.
Biostatistician
High demandKsh 150,000 to Ksh 600,000
Applies statistics in health sciences, overseeing clinical trials, epidemiological studies, survival analysis, and supporting health research.
Data Analyst
High demandKsh 130,000 to Ksh 550,000
Analyses data for insights, overseeing data cleaning, analysis, visualisation, reporting, and supporting data-driven decision-making.
Quantitative Analyst
Moderate demandKsh 180,000 to Ksh 700,000
Applies quantitative methods in finance, overseeing risk modelling, pricing, trading strategies, and supporting financial decision-making.
Epidemiologist
Moderate demandKsh 140,000 to Ksh 550,000
Studies disease patterns, overseeing epidemiological studies, outbreak investigation, surveillance, and supporting public health.
University Lecturer
Moderate demandKsh 130,000 to Ksh 500,000
Teaches statistical science in universities, conducting research and training future statistics professionals.

Graduate outcomes

Graduates pursue careers as statisticians, biostatisticians, data analysts, and quantitative analysts in research institutions, financial services, healthcare, and government.

Where these fields lead

8 careers

Tools you will learn

R
SoftwarePrimary
Open-source statistical computing environment for statistical modelling, data analysis, visualisation, simulation, and statistical computing in R.
MATLAB
SoftwarePrimary
Numerical computing software for mathematical modelling, statistical computation, matrix operations, simulation, and mathematical and statistical computing.
C++
SoftwarePrimary
Programming language for high-performance statistical computing, algorithms, numerical methods, simulation, and computational statistics.
Python
Software
Programming language for data analysis, machine learning, statistical computing, pandas, scikit-learn, and data science applications.

Industry links

Common misconceptions

Statistical science is just about calculating averages.
The programme involves probability theory, Bayesian modelling, stochastic processes, machine learning, computational statistics, and research, covering comprehensive statistical science.
This programme is only for mathematics graduates.
The programme accepts graduates from mathematics, statistics, actuarial science, physics, economics, computer science, and related quantitative disciplines.
There is limited demand for statisticians in Kenya.
Kenya's data revolution, financial services, healthcare research, government statistics, big data, and evidence-based policy create high demand for qualified statistical science professionals.
Statistical science is the same as data science.
While overlapping, statistical science emphasises theoretical foundations, inference, and mathematical rigour, while data science emphasises computational methods and applications.
Bayesian statistics is too complex for practical use.
Bayesian methods are widely used in practice, with MCMC and computational tools making Bayesian modelling accessible and powerful for real-world problems.
A master's in statistical science is redundant after a mathematics degree.
The master's provides specialised statistical skills, computational expertise, research capability, and career progression to senior statistical and analytical positions.