Science

Doctor of Philosophy in Mathematical Statistics

The Doctor of Philosophy in Mathematical Statistics is a postgraduate research programme offered at the University of Nairobi and Kenyatta University through their respective departments of mathematics and statistics. The programme trains scholars to conduct advanced research on statistical theory, probability, and statistical methodology, contributing to evidence-based decision-making in science, industry, and government in Kenya and globally.

Students engage with advanced probability theory, statistical inference, stochastic processes, Bayesian statistics, multivariate analysis, and research methodology. The programme combines coursework with thesis research, requiring candidates to develop a research proposal, conduct independent theoretical and applied research, and produce a doctoral dissertation that makes an original contribution to mathematical statistics.

Kenya's growing data-driven economy requires advanced statistical expertise for national statistics, financial modelling, health research, agriculture, and quality control. The programme addresses national priorities under Vision 2030 by training researchers who can develop statistical methods and apply them to national development challenges.

Teaching methods include lectures, statistical computing, theoretical proofs, applied data analysis, and supervised thesis work. Students have access to university computing facilities and collaborate with the Kenya National Bureau of Statistics, research institutions, and industry.

Graduates pursue careers as university lecturers, statisticians, biostatisticians, data scientists, and quantitative analysts in universities, the KNBS, research institutions, financial institutions, and international organisations.

This programme is ideal for holders of a Master's degree in mathematical statistics, statistics, mathematics, or related quantitative fields who are committed to advancing knowledge in statistical theory and methodology.

Duration: 3-5 years
Public, up to: Ksh 428,000
Private, up to: Ksh 150,000
Job market: Moderate

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: 5 students
Award: PhD

What you study

10 subjects

Probability Theory
Statistical Inference
Stochastic Processes
Bayesian Statistics
Multivariate Analysis
Research Methodology
Statistical Computing
Thesis Research
Asymptotic Theory
Regression Analysis

Modules

12 in the programme

Advanced Probability Theory
Year 1Semester 13 creditsCore
Advanced probability including measure theory, probability spaces, random variables, limit theorems, and probability distributions
Statistical Inference and Estimation
Year 1Semester 23 creditsCore
Advanced statistical inference including point estimation, hypothesis testing, confidence intervals, and maximum likelihood theory
Statistical Inference and Decision Theory
Year 1Semester 23 creditsCore
Statistical inference, including probability theory, hypothesis testing, regression analysis, and statistical software applications for doctoral research.
Stochastic Processes and Applications
Year 1Semester 33 creditsCore
Stochastic processes including Markov chains, Poisson processes, Brownian motion, and stochastic differential equations
Bayesian Statistics and Computing
Year 1Semester 33 creditsCore
Bayesian statistics, including probability theory, hypothesis testing, regression analysis, and statistical software applications for doctoral research.
Bayesian Statistics and Computational Methods
Year 2Semester 13 creditsCore
Bayesian statistics including prior distributions, posterior inference, MCMC methods, and Bayesian computation
Stochastic Processes and Modelling
Year 2Semester 13 creditsCore
Stochastic processes, covering mathematical and computational modelling techniques, simulation methods, and their application in research and analysis.
Multivariate Analysis
Year 2Semester 23 creditsCore
Multivariate statistical methods including PCA, factor analysis, discriminant analysis, and multivariate regression
Data Science and Machine Learning
Year 2Semester 23 creditsCore
Data science, examining pedagogical theories, educational frameworks, and their application in teaching, learning, and curriculum development.
Asymptotic Theory and Large Sample Methods
Year 2Semester 33 creditsCore
Asymptotic statistics including consistency, asymptotic normality, efficiency, and large sample inference
Research Methods in Statistics
Year 2Semester 33 creditsCore
Research design and methodology, covering quantitative and qualitative approaches, data collection techniques, and analytical frameworks relevant to doctoral-level research in the field.
Research Methodology and Statistical Computing
Year 3Semester 13 creditsCore
Research design, statistical computing in R and Python, simulation methods, and computational statistics

Specialisations

Economic Data Science
Specialization in Economic Data Science, preparing graduates for careers such as economic-data-scientist.
Public Health Data Analysis (Junior)
Specialization in Public Health Data Analysis (Junior), preparing graduates for careers such as public-health-data-analyst-junior.
Academic Research in Science
Specialization in Academic Research in Science, preparing graduates for careers such as university-lecturer.
Public Health Data Analysis
Specialization in Public Health Data Analysis, preparing graduates for careers such as data-analyst-in-public-health.
Biostatistics Research
Specialization in Biostatistics Research, preparing graduates for careers such as biostatistician.
Operations Research Analysis
Specialization in Operations Research Analysis, preparing graduates for careers such as operations-research-analyst.
Time Series Analysis
Research on time series and forecasting
Bayesian Statistics
Research on Bayesian methods
Data Science
Research on data science and ML
Biostatistics
Research on biostatistics

A day as a student

A typical day combines theoretical statistical research with computational analysis, mathematical proofs, data analysis, literature review, and thesis writing. Students attend mathematics and statistics research seminars, present findings at departmental colloquia, and engage with KNBS and research institutions for applied statistical research.

The trade offs

In its favour

Two public universities offer the programme providing choice and fee range
Access to KNBS, Central Bank and research institutes
Diverse career paths in academia, government, finance, health, and research
Opportunities for international collaboration through ISI and RSS

Against it

UoN PhD is expensive (KSh 1,324,000 total)
Requires strong mathematical background and theoretical rigour
Requires strong mathematical and programming skills
Statistical research requires access to quality data which can be limited in Kenya

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

Public367k to 428k

367k at Kenyatta University428k at University of Nairobi

Private150k to 150k

150k at Mount Kenya University150k at Mount Kenya 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

UoN Math Stats PhD ~428K/yr (uonbi.ac.ke). KU ~367K/yr (ku.ac.ke). MKU ~150K/yr.

Students may access HELB postgraduate loans, DAAD scholarships, NRF research grants, and ISI research grants for mathematical statistics research.

Funding options

HELB Postgraduate Loan
LoanKsh 80,000
DAAD Scholarship
Scholarship
NRF Research Grant
Grant
ISI Research Grant
Grant

Scholarships

5 recorded

NRF Research Grant
GrantKenyan
Researchers at Kenyan institutions conducting research relevant to national development
HELB Postgraduate Loan
LoanKsh 80,000Kenyan
Kenyan postgraduate students enrolled in recognised universities
HELB Postgraduate Scholarship
LoanKsh 200,000Kenyan
Kenyan postgraduate students
DAAD Kenya Scholarship
ScholarshipKenyan
Postgraduate students in Kenya
NACOSTI Research Grant
GrantKenyan
Science and technology researchers in Kenya

Getting in

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

What you need

KCSE mean grade: C+
Alternative entry: Relevant diploma or equivalent qualification

Master's Degree
MSc in Mathematical Statistics, Statistics, Mathematics, or related quantitative field from a recognised university
Undergraduate qualification
Bachelor's degree in Mathematics, Statistics, or related quantitative field
Research capability
Evidence of research ability through publications, reports, or academic work

How you are assessed

5 components

Thesis Proposal Defence
Oral defence20% of the mark
Defence of research proposal before faculty panel
Thesis
Thesis50% of the mark
Doctoral thesis research, submission and oral defence
Oral Viva Voce
Coursework100% of the mark
Thesis Examination
Coursework100% of the mark
Computing and Analysis Projects
Coursework20% of the mark

Accreditation

Accredited by the Commission for University Education (CUE).

Accredited by

Commission for University Education (CUE)
Academic accreditationRequired
Mandatory accreditation for all university programmes in Kenya
Commission for University Education
AcademicRequired
Statutory body responsible for accreditation of university programmes in Kenya

Where it leads

The roles it opens, and what you leave with.

Where graduates go

7 roles

Operations Research Analyst
Moderate demandKsh 80,000 to Ksh 180,000
Applies statistical and mathematical methods to optimise operations in industry and government
Economic Data Scientist
High demandKsh 90,000 to Ksh 200,000
Analyses economic and financial data using statistical methods at banks, CBK, or consulting firms
Biostatistician
High demandKsh 80,000 to Ksh 180,000
Applies statistical methods to health research at KEMRI, hospitals, or research institutions
University Lecturer (Statistics)
High demandKsh 80,000 to Ksh 180,000
Teaches statistics and conducts research in university mathematics and statistics departments
Statistics Researcher
High demandKsh 200,000 to Ksh 500,000
Research at KNBS and research institutes
University Professor
High demandKsh 180,000 to Ksh 450,000
Teaching and research in mathematics departments
Data Scientist
High demandKsh 250,000 to Ksh 600,000
Data science at tech companies and banks

Graduate outcomes

Graduates work as university lecturers, statisticians, biostatisticians, and data scientists in universities, the KNBS, research institutions, financial institutions, and international organisations.

Where these fields lead

8 careers

Certifications

Industry links

Common misconceptions

Mathematical statistics is just about calculating averages
Mathematical statistics encompasses probability theory, statistical inference, stochastic processes, Bayesian methods, and asymptotic theory, requiring advanced mathematical training and rigorous proofs.
A PhD in mathematical statistics has limited career prospects
Growing demand for data-driven decision-making creates opportunities for statisticians at universities, KNBS, financial institutions, research organisations, and international bodies.
Mathematical statistics research is not relevant to Kenya
Kenya's development planning, census, health research, agricultural surveys, and financial modelling all depend on robust statistical methods, making this research critical for national development.
Statistics is the same as data science
Mathematical statistics provides the theoretical foundation for data science, focusing on statistical theory, inference, and methodology, while data science applies these methods to large-scale data problems.