Science

Master of Science in Applied Statistics

The Master of Science in Applied Statistics is a postgraduate programme that prepares professionals for advanced practice in statistical analysis, data science, and quantitative research. The programme combines statistical theory with practical data analysis and modelling application.

Core areas include probability theory, statistical inference, regression analysis, experimental design, multivariate analysis, time series analysis, Bayesian statistics, statistical computing, research methods, and thesis. Students engage with both statistical theory and practical data analysis through coursework and research.

The programme is offered by JKUAT, Kenyatta University, Technical University of Kenya, University of Nairobi, and Maseno University. All are public universities offering the programme over two academic years through full-time and part-time modes of study.

Students develop competencies in statistical inference, regression modelling, experimental design, multivariate analysis, time series, Bayesian methods, statistical computing, and research methods. The programme includes coursework, examinations, and a research thesis or project.

TU-K charges approximately KES 201,000/year (KES 402,000 total). KU charges approximately KES 326,400/year. Entry requires a Bachelor's degree with Second Class Honours Upper Division in statistics, mathematics, or related fields from a recognised university.

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

Skills Required

Statistical Inference and Hypothesis Testing
Regression Analysis and Generalised Linear Models
Experimental Design and Analysis of Variance
Multivariate Analysis and Data Reduction
Time Series Analysis and Forecasting
Bayesian Statistics and Probabilistic Modelling
Statistical Computing with R and Python
Survey Sampling and Statistical Methods
Research Methods in Statistics
Academic Writing and Thesis Research

Key Subjects

Probability Theory and Statistical Inference
Regression Analysis and Generalised Linear Models
Experimental Design and Analysis of Variance
Multivariate Analysis and Data Reduction
Time Series Analysis and Forecasting
Bayesian Statistics and Probabilistic Modelling
Statistical Computing with R and Python
Survey Sampling and Statistical Methods
Research Methods in Statistics
Thesis Research

Certifications

KNBS Statistical Certification
ISI Professional Statistical Certification

Specializations

Biostatistics

Focuses on biostatistics, covering clinical trials, epidemiological methods, survival analysis, medical statistics, and managing biostatistics.

Financial Statistics

Examines financial statistics, covering risk modelling, financial time series, econometrics, actuarial statistics, and managing financial statistics.

Survey Statistics

Covers survey statistics, covering sampling design, survey methodology, questionnaire design, official statistics, and managing survey statistics.

Statistical Computing

Focuses on statistical computing, covering R programming, Python, data mining, machine learning, and managing statistical computing.

Industrial Statistics

Examines industrial statistics, covering quality control, reliability, design of experiments, process improvement, and managing industrial statistics.

Environmental Statistics

Covers environmental statistics, covering spatial statistics, environmental modelling, climate statistics, ecological statistics, and managing environmental statistics.

Duration: 2 years
Public, up to: Ksh 295,800
Job market: High

The programme

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

Practicalities

Study mode: Full-time, Part-time
Attachment: 0 months
Average class: 20 students
Award: Masters

What you study

10 subjects

Probability Theory and Statistical Inference
Regression Analysis and Generalised Linear Models
Experimental Design and Analysis of Variance
Multivariate Analysis and Data Reduction
Time Series Analysis and Forecasting
Bayesian Statistics and Probabilistic Modelling
Statistical Computing with R and Python
Survey Sampling and Statistical Methods
Research Methods in Statistics
Thesis Research

Modules

12 in the programme

Probability Theory and Statistical Inference
Year 1Semester 13 creditsCore
Examines probability distributions, random variables, limit theorems, estimation, hypothesis testing, likelihood theory, and managing statistical inference.
Regression Analysis and Generalised Linear Models
Year 1Semester 13 creditsCore
Covers linear regression, logistic regression, generalised linear models, mixed models, model selection, diagnostics, and managing regression analysis.
Research Methods in Statistics
Year 1Semester 13 creditsCore
Covers research design, data collection, analysis, ethical issues, and conducting statistical research, preparing students for their thesis.
Experimental Design and Analysis of Variance
Year 1Semester 23 creditsCore
Examines ANOVA, factorial designs, randomised blocks, response surface methodology, nested designs, and managing experimental design.
Multivariate Analysis and Data Reduction
Year 1Semester 23 creditsCore
Covers PCA, factor analysis, cluster analysis, discriminant analysis, MANOVA, canonical correlation, and managing multivariate analysis.
Time Series Analysis and Forecasting
Year 1Semester 23 creditsCore
Examines ARIMA models, seasonal adjustment, forecasting, spectral analysis, volatility models, state-space models, and managing time series analysis.
Bayesian Statistics and Probabilistic Modelling
Year 2Semester 13 creditsCore
Covers Bayesian inference, prior distributions, posterior distributions, MCMC, hierarchical models, Bayesian computation, and managing Bayesian statistics.
Statistical Computing with R and Python
Year 2Semester 13 creditsCore
Examines R programming, Python, data manipulation, simulation, visualisation, reproducible research, and managing statistical computing.
Survey Sampling and Statistical Methods
Year 2Semester 13 creditsCore
Covers sampling design, stratification, cluster sampling, weighting, non-response, estimation, and managing survey statistics.
Stochastic Processes and Applications
Year 2Semester 13 creditsCore
Examines Markov chains, Poisson processes, Brownian motion, martingales, queueing theory, and managing stochastic processes.
Statistical Quality Control and Reliability
Year 2Semester 13 creditsCore
Covers control charts, process capability, acceptance sampling, reliability theory, survival analysis, and managing industrial statistics.
Research Thesis or Project
Year 2Semester 26 creditsCore
Original research thesis or project on an applied statistics topic, demonstrating mastery of research methods and statistical knowledge, assessed through written submission and oral defence.

Specialisations

Biostatistics
Focuses on biostatistics, covering clinical trials, epidemiological methods, survival analysis, medical statistics, and managing biostatistics.
Financial Statistics
Examines financial statistics, covering risk modelling, financial time series, econometrics, actuarial statistics, and managing financial statistics.
Survey Statistics
Covers survey statistics, covering sampling design, survey methodology, questionnaire design, official statistics, and managing survey statistics.
Statistical Computing
Focuses on statistical computing, covering R programming, Python, data mining, machine learning, and managing statistical computing.
Industrial Statistics
Examines industrial statistics, covering quality control, reliability, design of experiments, process improvement, and managing industrial statistics.
Environmental Statistics
Covers environmental statistics, covering spatial statistics, environmental modelling, climate statistics, ecological statistics, and managing environmental statistics.

A day as a student

A typical day during the MSc in Applied Statistics programme combines lectures, computer laboratory sessions, practical workshops, seminars, and independent study. Sessions cover probability theory, statistical inference, regression analysis, experimental design, and multivariate analysis. Probability theory sessions examine probability distributions, random variables, limit theorems, stochastic processes, and managing probability theory. Statistical inference sessions cover estimation, hypothesis testing, confidence intervals, likelihood theory, non-parametric methods, and managing statistical inference. Regression analysis sessions cover linear regression, logistic regression, generalised linear models, mixed models, model selection, and managing regression analysis. Experimental design sessions cover ANOVA, factorial designs, randomised blocks, response surface methodology, and managing experimental design. Multivariate analysis sessions cover PCA, factor analysis, cluster analysis, discriminant analysis, MANOVA, and managing multivariate analysis. Time series sessions cover ARIMA models, seasonal adjustment, forecasting, spectral analysis, volatility models, and managing time series analysis. Bayesian statistics sessions cover Bayesian inference, prior distributions, posterior distributions, MCMC, hierarchical models, and managing Bayesian statistics. Statistical computing sessions cover R programming, Python, data manipulation, simulation, visualisation, and managing statistical computing. Survey sampling sessions cover sampling design, stratification, cluster sampling, weighting, estimation, and managing survey statistics. Research methods sessions prepare students for their thesis, covering research design, data collection, and analysis. Computer laboratory sessions provide hands-on experience with R, Python, statistical software, data analysis, and simulation. Seminars and discussion groups provide opportunities for debating current issues in statistics. Guest lectures from experienced statisticians, data scientists, and industry professionals provide practical insights. The programme culminates in a research thesis or project on an applied statistics topic.

The trade offs

In its favour

Very high demand for statistics professionals with growing data-driven decision-making, big data, evidence-based policy, and research in Kenya.
Programme is offered by five public universities (JKUAT, KU, TU-K, UoN, Maseno), providing wide institutional choice.
TU-K offers competitive fees at approximately KES 201,000/year (KES 402,000 total).
Programme combines statistical theory with computing, providing versatile skills for data science, research, and industry.

Against it

KU fees are higher at approximately KES 326,400/year.
No private university confirmed offering this programme, limiting options.
Programme requires mathematics or statistics background, which may limit access for non-related graduates.
Rapidly evolving field requires continuous self-learning beyond the programme curriculum.

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

Public232k to 296k

232k at Technical University of Kenya296k at Kenyatta 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

TUK ~231.6K/yr (intake.tukenya.ac.ke). KU 295.8K/yr (mawese.com). Private null.

HELB postgraduate loans are available for Kenyan students. KU may offer postgraduate bursaries for eligible students. KNBS and ISI may provide training support for statistics professionals. Some organisations may sponsor staff for postgraduate study.

Funding options

HELB Postgraduate Loan
KU Postgraduate Bursary
KNBS Training Support

Scholarships

3 recorded

HELB Postgraduate Loan
LoanKsh 200,000Kenyan
Kenyan students pursuing postgraduate studies at recognised universities.
KU Postgraduate Bursary
ScholarshipKsh 100,000Kenyan
Kenyatta University offers postgraduate bursaries for eligible students.
KNBS Training Support
GrantKsh 200,000Kenyan
Kenya National Bureau of Statistics may provide training support for statistics professionals.

Getting in

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

What you need

KCSE mean grade: N/A (Postgraduate)
Alternative entry: Most universities require Upper Second Class Honours in statistics, mathematics, or related field. Lower Second Division holders with relevant experience or postgraduate diploma are considered. Contact respective universities for specific admission requirements.

How you are assessed

4 components

Coursework and Continuous Assessment
Coursework30% of the mark
Continuous assessment through coursework assignments, problem sets, computer laboratory reports, seminar presentations, and class participation.
Written Examinations
Examination40% of the mark
Written examinations covering probability, statistical inference, regression, experimental design, multivariate analysis, and time series.
Computer Laboratory and Project Assessment
Practical30% of the mark
Practical assessment through computer laboratory exercises, data analysis projects, statistical computing, simulation, and demonstrating statistical skills.
Research Thesis or Project
Research100% of the mark
Original research thesis or project on an applied 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). JKUAT, Kenyatta University, Technical University of Kenya, University of Nairobi, and Maseno University (all public) offer MSc in Applied Statistics. All programmes meet CUE standards for postgraduate training in statistics. Graduates are eligible for KNBS statistical certification and ISI professional statistical certification.

Accredited by

Commission for University Education (CUE)
Academic accreditationRequired
Programme accredited by CUE. JKUAT, Kenyatta University, Technical University of Kenya, University of Nairobi, and Maseno University (all public) offer MSc in Applied Statistics. All programmes meet CUE standards for postgraduate training in statistics. Graduates are eligible for KNBS statistical certification and ISI professional statistical certification.

Where it leads

The roles it opens, and what you leave with.

Where graduates go

6 roles

Statistician
High demandKsh 130,000 to Ksh 550,000
Conducts statistical analysis, overseeing data collection, analysis, modelling, interpretation, and managing statistical work.
Data Analyst
High demandKsh 130,000 to Ksh 550,000
Analyses data, overseeing data cleaning, analysis, visualisation, reporting, and managing data analysis.
Biostatistician
High demandKsh 140,000 to Ksh 600,000
Applies statistics in health research, overseeing clinical trials, epidemiological analysis, survival analysis, and managing biostatistics.
Quantitative Analyst
High demandKsh 140,000 to Ksh 600,000
Applies statistics in finance, overseeing risk modelling, financial analysis, quantitative research, and managing quantitative analysis.
Research Statistician
Moderate demandKsh 130,000 to Ksh 550,000
Conducts statistical research, overseeing research design, data analysis, publication, and managing statistical research.
Statistics Lecturer
Moderate demandKsh 120,000 to Ksh 500,000
Teaches statistics at university or college level, overseeing instruction, research, laboratory supervision, and academic supervision.

Graduate outcomes

Where these fields lead

8 careers

Tools you will learn

R
SoftwarePrimary
R statistical software for data analysis, covering statistical modelling, data visualisation, hypothesis testing, and managing statistical computing.
Python
Software
Python for data science, covering pandas, numpy, scipy, scikit-learn, data manipulation, and managing statistical computing.
SPSS
Software
SPSS for statistical analysis, covering data analysis, hypothesis testing, regression, survey analysis, and managing statistical analysis.
SAS
Software
SAS for advanced analytics, covering data analysis, statistical modelling, business analytics, and managing statistical computing.

Industry links

Common misconceptions

Applied statistics is just about crunching numbers.
Applied statistics covers comprehensive statistical theory, modelling, experimental design, inference, and computing beyond just number crunching.
This programme is only for those who are good at maths.
Statistics skills are valuable for anyone interested in data analysis, research, decision-making, evidence-based policy, and quantitative problem-solving.
Statistics has limited career prospects in Kenya.
With growing data-driven decision-making, big data, evidence-based policy, and research, demand for statistics professionals is very high in Kenya.
Statistical computing is just about using Excel.
Statistical computing covers comprehensive R programming, Python, data mining, machine learning, simulation, and advanced statistical software.
Bayesian statistics is just a different way of calculating.
Bayesian statistics covers comprehensive probabilistic modelling, prior information, posterior inference, MCMC, and hierarchical modelling.
Survey statistics is just about questionnaires.
Survey statistics covers comprehensive sampling design, estimation, weighting, non-response adjustment, and official statistics production.