Technology

Bachelor of Science in Statistical Modelling with Data Science

This Bachelor's programme prepares students for professional careers in statistical modelling and data science. It develops expertise in statistical analysis, data modelling, machine learning, and predictive analytics while building technical skills for designing, implementing, and managing statistical and data science infrastructure.

Students design statistical models, develop data science solutions, implement predictive analytics platforms, analyse statistical data, evaluate model performance, assess data quality, troubleshoot modelling issues, and optimise statistical analysis. They also strengthen statistical modelling skills, problem-solving, data analysis, technical communication, and systems management.

Kenya's data-driven economy requires professionals who can develop statistical models, manage data science, and support data analytics across financial institutions, government agencies, research institutions, and business organisations.

The programme combines coursework, statistical modelling laboratories, data science projects, industrial attachment, and statistical design projects to build comprehensive statistical modelling and data science expertise.

Graduates pursue careers as data scientists, statistical analysts, data analysts, machine learning engineers, and predictive modelling specialists across financial institutions, government agencies, research institutions, and business organisations.

The programme is ideal for students interested in statistical modelling, data science, machine learning, predictive analytics, and applying statistical technology to solve data and analytics challenges. The programme emphasises practical skills through industrial attachments, research projects, and community engagement that prepare graduates for real-world challenges.

Duration: 4 years
Public, up to: Ksh 306,000
Private, up to: Ksh 441,500
Job market: High

The programme

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

Practicalities

Study mode: Full-time
Attachment: 6 months
Average class: 50 students
Award: Bachelor

What you study

10 subjects

Statistical Modelling and Statistical Analysis
Data Science and Data Analytics
Machine Learning and Predictive Modelling
Statistical Computing and Computational Statistics
Data Mining and Data Exploration
Probability Theory and Statistical Theory
Regression Analysis and Multivariate Analysis
Time Series Analysis and Forecasting
Experimental Design and Survey Methods
Research Methods and Statistical Research

Modules

10 in the programme

Statistical Modelling and Statistical Analysis
Year 1Semester 13 creditsCore
Statistical modelling, statistical analysis, regression analysis, and supporting statistical modelling.
Data Science and Data Analytics
Year 1Semester 23 creditsCore
Data Science and Data Analytics: Data science, data analytics, data processing, and supporting data science. Covers data modelling, query optimisation, storage structures, and information retrieval.
Machine Learning and Predictive Modelling
Year 1Semester 33 creditsCore
Machine learning, predictive modelling, algorithm development, and supporting machine learning.
Statistical Computing and Computational Statistics
Year 2Semester 13 creditsCore
Statistical computing, computational statistics, programming for statistics, and supporting statistical computing.
Data Mining and Data Exploration
Year 2Semester 23 creditsCore
Data Mining and Data Exploration: Data mining, data exploration, pattern recognition, and supporting data mining. Covers data modelling, query optimisation, storage structures, and information retr...
Probability Theory and Statistical Theory
Year 2Semester 33 creditsCore
Probability theory, statistical theory, mathematical statistics, and supporting probability theory.
Regression Analysis and Multivariate Analysis
Year 3Semester 13 creditsCore
Regression analysis, multivariate analysis, advanced regression, and supporting regression analysis.
Time Series Analysis and Forecasting
Year 3Semester 23 creditsCore
Time series analysis, forecasting, temporal analysis, and supporting time series analysis.
Experimental Design and Survey Methods
Year 3Semester 33 creditsCore
Experimental design, survey methods, sampling theory, and supporting experimental design.
Research Methods and Statistical Research
Year 4Semester 16 creditsCore
Research methods, statistical research, data collection, and supporting research methods and project work.

Specialisations

Data Science and Big Data Analytics
Concentrates on data science, big data analytics, data processing, and data engineering. 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.
Computational Statistics and Statistical Computing
Focuses on computational statistics, statistical computing, programming for statistics, and algorithm development.
Statistical Analysis and Applied Statistics
Specialises in statistical analysis, applied statistics, regression analysis, and statistical modelling.
Time Series Analysis and Forecasting
Focuses on time series analysis, forecasting, temporal modelling, and predictive analytics. 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.
Machine Learning and Predictive Modelling
Focuses on machine learning, predictive modelling, algorithm development, and predictive analytics. 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 starts with morning lectures on statistical modelling, data science, or machine learning, followed by statistical laboratories, data science projects, or industry visits. Students design statistical models, develop data science solutions, work with statistical tools, analyse data, and work on their statistical modelling projects. Industrial attachments at financial institutions, government agencies, and research institutions provide hands-on experience.

The trade offs

In its favour

High demand — data analytics needs, digital transformation, and data-driven decision making create strong demand for statistical modelling and data science professionals.
Diverse applications — graduates work in finance, government, research, healthcare, retail, and various industries.
Technology — statistical modelling and data science involves cutting-edge technology including machine learning, predictive analytics, and statistical computing.

Against it

Continuous learning — statistical modelling and data science faces rapid technology changes, continuous learning requirements, and skills obsolescence.
Technical complexity — statistical modelling and data science requires strong technical skills in statistics, machine learning, and data analysis.
Data sensitivity — working with data involves handling sensitive information, data privacy concerns, and sometimes working with confidential data.

What it costs

Tuition at both ends of the market, and how to pay for it.

What it costs, and where

Against 191 technology courses

Public288k to 306k

288k at University of Nairobi306k at Kenyatta University

Private111k to 442k

111k at Mount Kenya University442k at USIU-Africa

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). USIU 441.5K, MKU 111K/yr.

Students can access HELB undergraduate loans and KUCCPS placement. County bursaries, TVET sponsorships, and international data science scholarships are available.

Funding options

HELB Undergraduate Loan
KUCCPS Placement
County Government Bursaries

Scholarships

5 recorded

Huawei Seeds for the Future Kenya
Scholarship
Talented ICT/STEM university students with a GPA of 3.0+
Generation Google Scholarship
Scholarship
Women in computer science and related technical fields
Higher Education Loans Board (HELB) Loan
LoanKsh 60,000Kenyan
Kenyan citizens pursuing undergraduate studies at accredited institutions.
County Government Bursaries
BursaryKsh 50,000Kenyan
Students from specific counties. Varies by county.
Higher Education Fund (HEF)
PlacementKsh 0Kenyan
Kenyan students placed to TVET institutions through KUCCPS receive government-subsidised tuition.

Getting in

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

What you need

KCSE mean grade: C+
Alternative entry: Diploma in Statistics, Data Science, Computer Science, or related fields from recognised TVET institutions may be considered for upgrading. Relevant statistical or data analysis experience is advantageous.

Mathematics
C+
English or Kiswahili
C
Physics or Chemistry
C
Biology or Geography
C

How you are assessed

5 components

Statistical Modelling and Data Science Practical Examinations
Practicum30% of the mark
Assessment of statistical modelling and data science competencies including statistical analysis, machine learning, data science, and statistical computing.
End of Semester Written Examinations
Exam30% of the mark
Written examinations covering statistical modelling, data science, machine learning, and statistical research.
Research Project
Research10% of the mark
Independent statistical modelling and data science research project, assessed through written report and presentation.
Industry Attachment Assessment
Clinical10% of the mark
Assessment of industry attachments including statistical modelling work, data science work, documentation, and supervision evaluation.
Continuous Assessment Tests and Assignments
Cat20% of the mark
Assignments, tests, presentations, case studies, and statistical modelling projects throughout each semester.

Accreditation

Accredited by the Technical and Vocational Education and Training Authority (TVETA). Examined by Kenya National Examinations Council (KNEC) or National Industrial Training Authority (NITA). The TVET Act governs TVET institutions. The Data Protection Act governs data handling. The programme is accredited by the Commission for University Education (CUE), the statutory body responsible for quality assurance and regulation of university education in Kenya.

Accredited by

Technical and Vocational Education and Training Authority
NationalRequired
TVETA accredits TVET institutions and programmes in Kenya.

Where it leads

The roles it opens, and what you leave with.

Where graduates go

5 roles

Data Scientist / Data Science Specialist
High demandKsh 60,000 to Ksh 150,000
Develops data science solutions, statistical models, machine learning algorithms, and supports data science at financial institutions, government agencies, or research institutions.
Predictive Modelling Specialist / Forecasting Analyst
High demandKsh 60,000 to Ksh 150,000
Develops predictive models, forecasting systems, time series models, and supports predictive modelling at financial institutions, government agencies, or business organisations.
Statistical Analyst / Statistician
High demandKsh 60,000 to Ksh 150,000
Analyses statistical data, develops statistical models, conducts statistical analysis, and supports statistical analysis at government agencies, research institutions, or financial institutions.
Data Analyst / Data Analytics Specialist
High demandKsh 55,000 to Ksh 140,000
Analyses data, develops data analytics solutions, creates data reports, and supports data analytics at financial institutions, government agencies, or business organisations.
Machine Learning Engineer / ML Engineer
High demandKsh 70,000 to Ksh 170,000
Develops machine learning models, predictive algorithms, ML systems, and supports machine learning at technology companies, financial institutions, or research institutions.

Graduate outcomes

Graduates work as data scientists and statistical analysts. Statistical modelling and data science is in high demand driven by data analytics needs. Salaries range from KES 60,000 to 150,000 per month. TVETA, KNEC, KNBS, and financial institutions offer the main opportunities.

Where these fields lead

8 careers

Tools you will learn

Figma
DesignPrimary
Adobe Creative Suite
DesignPrimary
Unity
Game engine
Visual Studio Code
Ide
Microsoft Excel
SoftwarePrimary
Spreadsheet for analytical data management, quality control charts and statistical analysis.

Certifications

Industry links

Common misconceptions

Statistical modelling and data science is just about calculating averages.
Statistical modelling and data science includes machine learning, predictive analytics, statistical computing, data mining, and advanced statistical analysis — calculating averages is one component.
Statistical modelling and data science is only for research institutions.
Statistical modelling and data science applies to financial institutions, government agencies, healthcare, retail, and many other industries beyond research institutions.
There are limited career opportunities for statistical modelling graduates in Kenya.
Kenya's data analytics needs, digital transformation, statistical analysis requirements, and data-driven decision making create demand for statistical modelling and data science professionals at financial institutions, government agencies, and research institutions.
This programme has limited career opportunities.
Graduates pursue diverse career paths across multiple sectors in Kenya and internationally.