Technology

Master of Science in Statistical Computing

The Master of Science in Statistical Computing is a postgraduate programme that prepares professionals for advanced practice in computational statistics, statistical programming, and data science. The programme provides specialised training in statistical computing, data analysis, statistical modelling, machine learning, and computational methods for large datasets.

Core areas include statistical computing, computational statistics, statistical programming, data analysis, machine learning, Bayesian methods, stochastic processes, research methods, and thesis. Students engage with both theoretical and practical work through coursework, computing sessions, and research.

The programme is offered by Jomo Kenyatta University of Agriculture and Technology and the University of Nairobi as public institutions, with Strathmore University offering a closely related MSc in Statistical Science as a private institution. The programme is available in full-time and part-time modes over two years, combining coursework with research.

Students develop competencies in statistical computing, programming, data analysis, machine learning, Bayesian methods, and research. The programme includes coursework, examinations, computing projects, and a supervised research thesis, preparing graduates for data science, statistical programming, and analytical roles.

JKUAT charges approximately KES 140,000 per year while UoN charges approximately KES 362,500 per year. Strathmore University (private) charges approximately KES 305,000 per year for a closely related programme. Entry requires at least an Upper Second Class Honours degree in Statistics, Mathematics, or a related field from a recognised institution.

Graduates pursue careers as statistical programmers, data scientists, computational statisticians, quantitative analysts, research analysts, and lecturers across technology companies, financial institutions, research organisations, government, and universities.

Skills Required

Statistical Computing and Programming
Computational Statistics
Statistical Modelling and Inference
Machine Learning and Predictive Analytics
Bayesian Methods and Data Analysis
Data Visualisation and Reporting
Statistical Software Development
Big Data Analytics
Research Methods in Statistical Computing
Academic Writing and Thesis Research

Key Subjects

Statistical Computing
Computational Statistics
Statistical Programming
Machine Learning and Predictive Analytics
Bayesian Methods and Data Analysis
Stochastic Processes
Multivariate Statistical Analysis
Time Series Analysis and Forecasting
Research Methods in Statistical Computing
Thesis Research

Certifications

KNBS Professional Membership
ISI Professional Membership

Specializations

Computational Statistics

Focuses on computational statistics, covering computing, statistics, algorithms, methods, and managing computational statistics and data processing.

Statistical Programming

Examines statistical programming, covering programming, statistics, software, R, Python, and managing statistical programming and software development.

Machine Learning

Covers machine learning, covering learning, algorithms, data, prediction, and managing machine learning and predictive analytics.

Bayesian Methods

Focuses on Bayesian methods, covering Bayesian, statistics, inference, probability, and managing Bayesian methods and data analysis.

Big Data Analytics

Examines big data analytics, covering big, data, analytics, computing, and managing big data analytics and processing.

Data Science

Covers data science, covering data, science, analysis, statistics, and managing data science and analytics.

Duration: 2 years
Public, up to: Ksh 158,600
Private, up to: Ksh 418,500
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

Statistical Computing
Computational Statistics
Statistical Programming
Machine Learning and Predictive Analytics
Bayesian Methods and Data Analysis
Stochastic Processes
Multivariate Statistical Analysis
Time Series Analysis and Forecasting
Research Methods in Statistical Computing
Thesis Research

Modules

12 in the programme

Statistical Computing
Year 1Semester 13 creditsCore
Examines statistical computing, covering computing, statistics, algorithms, software, and managing computational statistics and data processing.
Statistical Programming
Year 1Semester 13 creditsCore
Covers statistical programming, covering programming, statistics, software, R, Python, and managing statistical programming and software development.
Probability Theory and Statistical Inference
Year 1Semester 13 creditsCore
Examines probability and inference, covering probability, statistics, inference, theory, and managing statistical theory and inference.
Linear Models and Regression
Year 1Semester 13 creditsCore
Covers linear models, covering models, regression, linear, analysis, and managing linear models and regression analysis.
Machine Learning and Predictive Analytics
Year 1Semester 23 creditsCore
Examines machine learning, covering learning, algorithms, data, prediction, and managing machine learning and predictive analytics.
Bayesian Modelling and Data Analysis
Year 1Semester 23 creditsCore
Covers Bayesian methods, covering Bayesian, statistics, inference, probability, and managing Bayesian methods and data analysis.
Stochastic Processes
Year 1Semester 23 creditsCore
Examines stochastic processes, covering stochastic, processes, probability, modelling, and managing stochastic processes and modelling.
Research Methods in Statistical Computing
Year 1Semester 23 creditsCore
Covers research, methods, statistics, computing, and conducting research in statistical computing, preparing students for their thesis.
Multivariate Statistical Analysis
Year 1Semester 23 creditsCore
Examines multivariate analysis, covering multivariate, statistics, analysis, methods, and managing multivariate statistical analysis.
Time Series Analysis and Forecasting
Year 2Semester 13 creditsCore
Covers time series analysis, covering time, series, analysis, forecasting, and managing time series analysis and forecasting.
Big Data Analytics
Year 2Semester 13 creditsCore
Examines big data analytics, covering big, data, analytics, computing, and managing big data analytics and processing.
Research Thesis
Year 2Semester 212 creditsCore
Original supervised research thesis on a statistical computing topic, demonstrating mastery of research methods and statistical computing knowledge, assessed through written submission and oral defence.

Specialisations

Computational Statistics
Focuses on computational statistics, covering computing, statistics, algorithms, methods, and managing computational statistics and data processing.
Statistical Programming
Examines statistical programming, covering programming, statistics, software, R, Python, and managing statistical programming and software development.
Machine Learning
Covers machine learning, covering learning, algorithms, data, prediction, and managing machine learning and predictive analytics.
Bayesian Methods
Focuses on Bayesian methods, covering Bayesian, statistics, inference, probability, and managing Bayesian methods and data analysis.
Big Data Analytics
Examines big data analytics, covering big, data, analytics, computing, and managing big data analytics and processing.
Data Science
Covers data science, covering data, science, analysis, statistics, and managing data science and analytics.

A day as a student

A typical day during the MSc in Statistical Computing programme combines lectures, computing sessions, seminars, and independent study. Sessions cover statistical computing, computational methods, programming, machine learning, and Bayesian analysis. Statistical computing sessions examine computing, statistics, algorithms, software, and managing computational statistics. Programming sessions cover programming, statistics, software, R, Python, and managing statistical programming. Machine learning sessions cover learning, algorithms, data, prediction, and managing machine learning and predictive analytics. Bayesian methods sessions cover Bayesian, statistics, inference, probability, and managing Bayesian methods and data analysis. Stochastic processes sessions cover stochastic, processes, probability, modelling, and managing stochastic processes and modelling. Multivariate analysis sessions cover multivariate, statistics, analysis, methods, and managing multivariate statistical analysis. Time series sessions cover time, series, analysis, forecasting, and managing time series analysis and forecasting. Computing sessions provide practical experience in R, Python, MATLAB, and C++ for statistical computing and data analysis. Seminars provide opportunities for presenting research findings and discussing current developments in statistical computing. The programme culminates in a supervised research thesis on a statistical computing topic.

The trade offs

In its favour

Programme provides specialised training in statistical computing, a high-demand skill in Kenya and globally.
Programme combines statistical theory with practical computing skills using R, Python, and MATLAB.
Programme offered at both public and private universities with different fee options.
Graduates are in high demand in technology, finance, research, and data-driven sectors.

Against it

UoN fee data is estimated from MSc Mathematics, not confirmed for Statistical Computing specifically.
Strathmore offers Statistical Science, not Statistical Computing specifically.
Significant fee difference between JKUAT (KES 140,000/year) and UoN (KES 362,500/year).
Programme requires strong background in statistics, mathematics, or computing.

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

Public159k to 159k

159k at Co-operative University of Kenya159k at Co-operative University of Kenya

Private419k to 419k

419k at Strathmore University419k at Strathmore 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

Strathmore MSc SS 837K/2yr=418.5K/yr (keguide). CUK MSc Stat Comp 158.6K/yr (keguide).

HELB postgraduate loans are available for Kenyan students. Universities may offer scholarships for eligible students. ISI and international statistics organisations may offer scholarships and fellowships for statistical computing research and education.

Funding options

HELB Postgraduate Loan
University Scholarship
ISI Scholarship

Scholarships

3 recorded

HELB Postgraduate Loan
LoanKsh 200,000Kenyan
Kenyan students pursuing postgraduate studies at recognised universities.
University Scholarship
ScholarshipKsh 140,000Kenyan
Universities may offer scholarships for eligible postgraduate statistical computing students.
ISI Scholarship
ScholarshipKsh 200,000
ISI and international statistics organisations may offer scholarships for statistical computing research and education.

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 at least an Upper Second Class Honours degree in a relevant field. Lower Second Class may be considered with relevant work experience or postgraduate diplomas. 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, computing projects, seminar presentations, and class participation.
Written Examinations
Examination70% of the mark
Written examinations covering statistical computing, programming, machine learning, Bayesian methods, and stochastic processes.
Computing Project
Project40% of the mark
Practical assessment through computing projects, demonstrating statistical programming, data analysis, and software development skills.
Research Thesis
Research100% of the mark
Original supervised research thesis on a statistical computing topic, demonstrating mastery of research methods and statistical computing knowledge, assessed through written submission and oral defence.

Accreditation

The programme is accredited by the Commission for University Education (CUE). JKUAT, University of Nairobi (public), and Strathmore University (private) offer MSc in Statistical Computing or closely related programmes. The programme meets CUE standards for postgraduate training in statistical computing. Graduates may interact with KNBS for official statistics standards and ISI for international statistics standards. The programme aligns with ISI global statistics and computational frameworks.

Accredited by

Commission for University Education (CUE)
Academic accreditationRequired
Programme accredited by CUE. JKUAT, University of Nairobi (public), and Strathmore University (private) offer MSc in Statistical Computing or closely related programmes. The programme meets CUE standards for postgraduate training in statistical computing. Graduates may interact with KNBS for official statistics standards and ISI for international statistics standards. The programme aligns with ISI global statistics and computational frameworks.

Where it leads

The roles it opens, and what you leave with.

Where graduates go

6 roles

Statistical Programmer
High demandKsh 130,000 to Ksh 450,000
Develops statistical software and programmes, covering programming, statistics, software, and managing statistical programming and development.
Data Scientist
High demandKsh 150,000 to Ksh 500,000
Manages data science projects, covering data, science, analysis, statistics, and managing data science and analytics.
Computational Statistician
Moderate demandKsh 130,000 to Ksh 450,000
Conducts computational statistics, covering computing, statistics, algorithms, and managing computational statistics and analysis.
Quantitative Analyst
High demandKsh 150,000 to Ksh 500,000
Conducts quantitative analysis, covering quantitative, analysis, finance, statistics, and managing quantitative analysis and modelling.
Research Analyst
High demandKsh 100,000 to Ksh 350,000
Conducts research analysis, covering research, analysis, data, statistics, and managing research analysis and reporting.
Statistics Lecturer
Moderate demandKsh 100,000 to Ksh 400,000
Teaches statistical computing at university or college level, overseeing instruction, research, and academic supervision.

Graduate outcomes

Where these fields lead

8 careers

Tools you will learn

R Statistical Software
SoftwarePrimary
R for statistical computing and data analysis, covering computing, statistics, analysis, and managing statistical computing and data processing.
Python
Software
Python for data analysis and statistical computing, covering computing, data, analysis, and managing data analysis and computing.
MATLAB
Software
MATLAB for statistical modelling and computing, covering modelling, computing, statistics, and managing statistical modelling and computing.
C++
Software
C++ for statistical software development and high-performance computing, covering programming, computing, statistics, and managing statistical software development.

Industry links

Common misconceptions

Statistical computing is just about using statistical software.
Statistical computing covers algorithm development, computational methods, programming, machine learning, and statistical software development beyond just using existing software.
This programme is only for computer scientists.
Statistical computing is for professionals from statistics, mathematics, economics, and science backgrounds, not just computer scientists.
Statistical computing has limited career prospects in Kenya.
Statistical computing graduates are in high demand in technology companies, financial institutions, research organisations, and data-driven businesses.
This programme is the same as general statistics.
Statistical computing focuses specifically on computational methods, programming, and algorithms for statistical analysis, distinct from general statistics.
Statistical computing is only about data analysis.
The programme covers algorithm development, software engineering, machine learning, Bayesian methods, and stochastic processes alongside data analysis.
You need to be a programmer to study statistical computing.
While programming skills are developed during the programme, a background in statistics or mathematics is sufficient for entry.