Science

Bachelor of Science in Statistics

The Bachelor of Science in Statistics is a programme that provides comprehensive training in statistical theory, methods and applications. Offered at the University of Nairobi, Kenyatta University, JKUAT, Maseno University, Moi University, Egerton University and other institutions, the programme prepares graduates for careers in statistics, data analysis and quantitative research.

The curriculum covers probability theory, statistical inference, regression analysis, experimental design, sampling methods, biostatistics, econometrics, time series analysis, multivariate statistics, Bayesian statistics, statistical computing, operations research, programming, research methods and a research project. Students develop analytical and computational skills for data-driven decision making.

Kenya's data-driven economy, research needs and financial sector create strong demand for statistics graduates. The programme is offered through lectures, problem-solving sessions, computer laboratories and research projects.

Graduates are prepared for careers as statisticians, data analysts, biostatisticians, actuaries, quantitative analysts, research statisticians, data scientists, statistical consultants and risk analysts. They work in the Kenya National Bureau of Statistics, banks, insurance companies, research institutions, universities, government, consulting firms and tech companies.

The programme is accredited by the Commission for University Education (CUE). Admission requires KCSE mean grade C+ with B- in Mathematics and C+ in Physics or Chemistry. KUCCPS Cluster 4.

Prospective students should have strong analytical skills, a commitment to continuous learning, and an interest in contributing to Kenya's socio-economic development. The programme provides a solid foundation for further postgraduate study and professional advancement in related fields.

Duration: 4 years
Public, up to: Ksh 306,000
Private, up to: Ksh 191,700
Job market: High

The programme

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

Practicalities

Study mode: Full-time
Attachment: 3 months
Average class: 40 students
Award: Bachelor

What you study

10 subjects

Probability Theory
Statistical Inference
Regression Analysis
Experimental Design
Sampling Methods
Biostatistics
Econometrics
Time Series Analysis
Statistical Computing
Bayesian Statistics

Modules

10 in the programme

Probability Theory
Year 1Semester 13 creditsCore
Probability foundations. The module covers theoretical foundations, practical applications and industry-relevant skills, assessed through continuous assessment tests, coursework and end-of-semester...
Statistical Inference
Year 1Semester 23 creditsCore
Estimation and testing. The module covers theoretical foundations, practical applications and industry-relevant skills, assessed through continuous assessment tests, coursework and end-of-semester ...
Regression Analysis
Year 1Semester 33 creditsCore
Regression models. The module covers theoretical foundations, practical applications and industry-relevant skills, assessed through continuous assessment tests, coursework and end-of-semester exami...
Experimental Design
Year 2Semester 13 creditsCore
Design of experiments. The module covers theoretical foundations, practical applications and industry-relevant skills, assessed through continuous assessment tests, coursework and end-of-semester e...
Sampling Methods
Year 2Semester 23 creditsCore
Survey sampling. The module covers theoretical foundations, practical applications and industry-relevant skills, assessed through continuous assessment tests, coursework and end-of-semester examina...
Biostatistics
Year 2Semester 33 creditsCore
Statistics in health. The module covers theoretical foundations, practical applications and industry-relevant skills, assessed through continuous assessment tests, coursework and end-of-semester ex...
Econometrics
Year 3Semester 13 creditsCore
Economic statistics. The module covers theoretical foundations, practical applications and industry-relevant skills, assessed through continuous assessment tests, coursework and end-of-semester exa...
Time Series Analysis
Year 3Semester 23 creditsCore
Time series methods. The module covers theoretical foundations, practical applications and industry-relevant skills, assessed through continuous assessment tests, coursework and end-of-semester exa...
Statistical Computing
Year 3Semester 33 creditsCore
R, Python and SPSS. The module covers theoretical foundations, practical applications and industry-relevant skills, assessed through continuous assessment tests, coursework and end-of-semester exam...
Research Project
Year 4Semester 16 creditsCore
Final year research project. The module covers theoretical foundations, practical applications and industry-relevant skills, assessed through continuous assessment tests, coursework and end-of-seme...

Specialisations

Data Analytics
Data analysis. This specialisation equips graduates with discipline-specific competencies for professional practice in Kenya and the East African region, with pathways to postgraduate study and professional certification.
Biostatistics
Health statistics. This specialisation equips graduates with discipline-specific competencies for professional practice in Kenya and the East African region, with pathways to postgraduate study and professional certification.
Econometrics
Economic statistics. This specialisation equips graduates with discipline-specific competencies for professional practice in Kenya and the East African region, with pathways to postgraduate study and professional certification.
Actuarial Science
Risk and insurance. This specialisation equips graduates with discipline-specific competencies for professional practice in Kenya and the East African region, with pathways to postgraduate study and professional certification.

A day as a student

A typical day involves attending statistics lectures, solving probability problems, working with statistical software (R, Python, SPSS), designing experiments, analysing datasets, studying econometric models, researching statistical methods, and working on quantitative research projects.

The trade offs

In its favour

High demand for statistical skills across sectors
Strong foundation for data science and analytics
Versatile degree with diverse career paths

Against it

Mathematically demanding programme
Some career paths require additional certification
Can be theoretical and abstract

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

Public288k to 306k

288k at University of Nairobi306k at Kenyatta University

Private110k to 192k

110k at Mount Kenya University192k at Kabarak 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 288K, KU 306K (uonbi, ku.ac.ke). Kabarak 191.7K, MKU 110K/yr.

HELB loans and government scholarships under the new funding model are available for eligible students.

Funding options

HELB Loan
County Bursaries
Government Scholarship (New Funding Model)

Scholarships

3 recorded

County Government Bursaries
BursaryKsh 30,000Kenyan
Kenyan students from participating counties
Higher Education Fund (HEF)
ScholarshipKsh 200,000Kenyan
KUCCPS-placed students
Higher Education Loans Board (HELB) Loan
LoanKsh 60,000Kenyan
Kenyan undergraduate students

Getting in

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

What you need

KCSE mean grade: C+
Alternative entry: KCSE mean grade C+ with B- in Mathematics and C+ in Physics or Chemistry. Diploma in statistics, mathematics, computer science or related field from a recognised institution. KUCCPS Cluster 4.

Mathematics
B-
Physics or Chemistry
C+
English or Kiswahili
C+

How you are assessed

4 components

Continuous Assessment Tests
Cat30% of the mark
Regular tests and problem sets
Research Project
Project10% of the mark
Final year research project
End of Semester Examinations
Written examination50% of the mark
End of semester written exams
Computer Practicals
Practicum10% of the mark
Statistical software sessions

Accreditation

Accredited by the Commission for University Education (CUE). Offered at multiple public and private universities. Admission requires KCSE mean grade C+ with B- in Mathematics and C+ in Physics or Chemistry. KUCCPS Cluster 4.

Accredited by

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

5 roles

Research Statistician
Moderate demandKsh 120,000 to Ksh 350,000
Research statistics
Actuary
High demandKsh 150,000 to Ksh 500,000
Actuarial science
Data Analyst
High demandKsh 120,000 to Ksh 380,000
Data analysis
Statistician
High demandKsh 110,000 to Ksh 320,000
Statistical analysis
Biostatistician
Moderate demandKsh 120,000 to Ksh 350,000
Health statistics

Graduate outcomes

Graduates pursue careers as statisticians, data analysts and biostatisticians in KNBS, banks, insurance companies and research institutions.

Where these fields lead

8 careers

Tools you will learn

R
Statistical computingPrimary
Statistical computing and graphics
SPSS
Statistical analysisPrimary
Statistical analysis package
Python
Programming
Statistical programming and data analysis
STATA
Statistical analysis
Statistical software for econometric analysis
SAS
Statistical analysis
Advanced statistical analysis system
Microsoft Excel
Spreadsheet
Data management and basic statistics

Certifications

Industry links

Common misconceptions

Statistics is just about counting numbers.
The programme covers probability, inference, modelling, econometrics and computational methods.
Statistics graduates only work for KNBS.
Graduates work in finance, research, healthcare, tech, government and consulting.
Statistics is too mathematical.
The programme is mathematically rigorous but highly practical and applied.
There are no statistics jobs in Kenya.
Data-driven industries create strong demand for statistical skills.