Science

Bachelor of Science in Applied Statistics

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

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

Kenya's data-driven economy, the growth of research institutions and the demand for evidence-based decision making create strong demand for statistics graduates. The programme is offered through lectures, computational sessions, data analysis projects and research.

Graduates are prepared for careers as statisticians, data analysts, biostatisticians, research analysts, market researchers, risk analysts, M&E officers, census officers and statistical consultants. They work in government, research institutions, banks, insurance companies, NGOs, international organisations and consulting firms.

The programme is accredited by the Commission for University Education (CUE). Admission requires KCSE mean grade C+ with B- in Mathematics and C+ in English or Kiswahili. 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 307,000
Private, up to: Ksh 188,700
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: 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
Multivariate Analysis
Statistical Computing

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 hypothesis testing. The module covers theoretical foundations, practical applications and industry-relevant skills, assessed through continuous assessment tests, coursework and end-o...
Regression Analysis
Year 1Semester 33 creditsCore
Linear and nonlinear regression. The module covers theoretical foundations, practical applications and industry-relevant skills, assessed through continuous assessment tests, coursework and end-of-...
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 modelling. The module covers theoretical foundations, practical applications and industry-relevant skills, assessed through continuous assessment tests, coursework and end-of-semester e...
Statistical Computing
Year 3Semester 33 creditsCore
R, Python and statistical software. The module covers theoretical foundations, practical applications and industry-relevant skills, assessed through continuous assessment tests, coursework and end-...
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

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.
Survey Statistics
Survey methods. 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.
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.

A day as a student

A typical day involves attending statistics lectures, analysing datasets using statistical software, studying probability theory, designing experiments, working on regression models, programming in R or Python, and researching statistical applications.

The trade offs

In its favour

High demand in data-driven economy
Versatile skills applicable across all sectors
Strong foundation for data science and analytics

Against it

Mathematically demanding programme
Requires proficiency in statistical software
Some roles may require postgraduate qualifications

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

Public306k to 307k

306k at Kenyatta University307k at University of Nairobi

Private110k to 189k

110k at Mount Kenya University189k 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

KU BSc Stats 306K/yr (mawese). UoN BSc ~307K/yr (uonbi.ac.ke). MKU 110K, Kabarak 188.7K/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

Higher Education Fund (HEF)
ScholarshipKsh 200,000Kenyan
KUCCPS-placed students
County Government Bursaries
BursaryKsh 30,000Kenyan
Kenyan students from participating counties
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 English or Kiswahili. Diploma in statistics, mathematics or related field from a recognised institution. KUCCPS Cluster 4.

Mathematics
B-
English or Kiswahili
C+

How you are assessed

4 components

End of Semester Examinations
Written examination50% of the mark
End of semester written exams
Research Project
Project10% of the mark
Final year research project
Computational Practicals
Practicum10% of the mark
Data analysis using statistical software
Continuous Assessment Tests
Cat30% of the mark
Regular tests and assignments

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 English or Kiswahili. 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

Data Analyst
High demandKsh 120,000 to Ksh 380,000
Data analysis and visualisation
M&E Officer
High demandKsh 100,000 to Ksh 280,000
Monitoring and evaluation
Research Analyst
High demandKsh 110,000 to Ksh 300,000
Research analysis
Biostatistician
Moderate demandKsh 130,000 to Ksh 350,000
Health statistics
Statistician
High demandKsh 120,000 to Ksh 350,000
Statistical analysis

Graduate outcomes

Graduates pursue careers as statisticians, data analysts and biostatisticians in government, research institutions, banks and NGOs.

Where these fields lead

8 careers

Tools you will learn

R
Statistical computingPrimary
Statistical computing and data analysis
Python
ProgrammingPrimary
Data analysis and statistical programming
SPSS
Statistical analysis
Statistical package for social sciences
STATA
Statistical analysis
Statistical software for data analysis
SAS
Statistical analysis
Advanced statistical analysis system
Microsoft Excel
Spreadsheet
Data entry and basic statistical analysis

Certifications

Industry links

Common misconceptions

Statistics is just about counting numbers.
The programme covers probability, inference, modelling, experimental design and statistical computing.
Statistics graduates only work at KNBS.
Graduates work in banks, research, NGOs, insurance, healthcare and consulting.
Statistics is boring.
Statistics is at the forefront of data science, machine learning and evidence-based decision making.
Statistics is the same as mathematics.
Statistics focuses on data analysis and uncertainty, while mathematics covers broader abstract concepts.