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Master of Science in Statistical Science

<p>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.

Duration
2 years
Next intake January 2027
Public tuition
Ksh 352,000
Ksh 704,000 total
Private tuition
Ksh 305,000
Ksh 610,000 total
Job market
High
Market demand for graduates

The short answer

What it costs
Up to Ksh 352,000 a year at a public institution, Ksh 704,000 for the whole course. Up to Ksh 305,000 a year at a private one.The detail
Who can get in
A KCSE mean grade of C+ (KCSE or equivalent) or above. There is another way in without it.The detail
How long, and when
Takes 2 years, evening, with 0 months of attachment. Next intake January 2027, at 1 institution.The detail
Where it leads
8 careers in the catalogue. The least exposed to AI is Early Childhood Strategist, at 10 out of 100.The detail

The programme

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

Overview

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.

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 favourAgainst it
Kenya's data revolution, financial services, healthcare research, government statistics, big data, and evidence-based policy create high demand for qualified statistical science professionals.Programme requires strong mathematics background (First Class or Upper Second in Mathematics or related), which excludes many graduates.
Strathmore's Institute of Mathematical Sciences offers excellent balance between theory and application, with 16 course units and dissertation using R, MATLAB, and C++.Total fee of KES 837,000 is relatively expensive, limiting accessibility for some students.
Career opportunities in biostatistics, financial analysis, market research, econometrics, epidemiology, bioinformatics, demography, and statistical programming.Only offered at Strathmore University (private), with no public university option for this exact programme.
Programme includes international course module with one week exposure at Academic Centres of Excellence in Europe.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.

Scholarships you could apply for

2 usable where it is taught
Save one and be reminded before it closes

Getting in

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

What you need

KCSE mean grade
C+ (KCSE or equivalent)minimum entry
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.

When it next starts

Taught at 1 institution
January 20271 institution

Including Strathmore University.

May 20271 institution

Including Strathmore University.

September 20271 institution

Including Strathmore University.

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

  • 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.

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.

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.

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.

Further reading

Read next