Science

Master of Science in Biometry

The Master of Science in Biometry is a postgraduate programme that prepares professionals for advanced practice in biological statistics, experimental design, and biometric research. The programme combines statistical theory with biological and agricultural data analysis application.

Core areas include biostatistics, experimental design, regression analysis, multivariate analysis, statistical computing, survey sampling, biological data analysis, research methods, and thesis. Students engage with both statistical theory and practical biological data analysis through coursework and research.

The programme is offered by the University of Nairobi through the Department of Plant Science and Crop Protection at Chiromo Campus. It is a public university offering the programme over two academic years through full-time and part-time modes of study.

Students develop competencies in biostatistics, experimental design, regression modelling, multivariate analysis, statistical computing, survey sampling, biological data analysis, and research methods. The programme includes coursework, examinations, and a research thesis.

UoN charges approximately KES 354,000/year for science-based master's programmes. KU charges approximately KES 326,400/year for related statistics programmes. Entry requires a Bachelor's degree with Second Class Honours Upper Division in statistics, mathematics, biological sciences, agriculture, or related fields from a recognised university.

Graduates pursue careers as biometricians, statisticians, data analysts, research methodologists, quantitative analysts, and lecturers in biometry across agricultural research institutions, government agencies, pharmaceutical companies, and universities.

Skills Required

Biostatistics and Biological Data Analysis
Experimental Design and Analysis of Variance
Regression Analysis and Generalised Linear Models
Multivariate Analysis and Data Reduction
Statistical Computing with R and SAS
Survey Sampling and Statistical Methods
Biological Modelling and Simulation
Research Methods in Biometry
Data Management and Statistical Quality Control
Academic Writing and Thesis Research

Key Subjects

Biostatistics and Biological Data Analysis
Experimental Design and Analysis of Variance
Regression Analysis and Generalised Linear Models
Multivariate Analysis and Data Reduction
Statistical Computing with R and SAS
Survey Sampling and Statistical Methods
Biological Modelling and Simulation
Research Methods in Biometry
Data Management and Statistical Quality Control
Thesis Research

Certifications

KNBS Statistical Certification
IBS Biometric Certification

Specializations

Agricultural Statistics

Focuses on agricultural statistics, covering experimental design, field trial analysis, crop yield modelling, agricultural surveys, and managing agricultural statistics.

Biostatistics

Examines biostatistics, covering clinical trials, epidemiological methods, survival analysis, medical statistics, and managing biostatistics.

Environmental Statistics

Covers environmental statistics, covering spatial statistics, environmental modelling, ecological data analysis, climate statistics, and managing environmental statistics.

Statistical Computing

Focuses on statistical computing, covering R programming, SAS, data mining, statistical software, simulation, and managing statistical computing.

Survey Statistics

Examines survey statistics, covering sampling design, survey methodology, questionnaire design, official statistics, and managing survey statistics.

Quantitative Genetics

Covers quantitative genetics, covering genetic data analysis, breeding values, heritability estimation, QTL analysis, and managing quantitative genetics statistics.

Duration: 2 years
Public, up to: Ksh 388,000
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: 15 students
Award: Masters

What you study

10 subjects

Biostatistics and Biological Data Analysis
Experimental Design and Analysis of Variance
Regression Analysis and Generalised Linear Models
Multivariate Analysis and Data Reduction
Statistical Computing with R and SAS
Survey Sampling and Statistical Methods
Biological Modelling and Simulation
Research Methods in Biometry
Data Management and Statistical Quality Control
Thesis Research

Modules

12 in the programme

Biostatistics and Biological Data Analysis
Year 1Semester 13 creditsCore
Examines biological data analysis, clinical trials, epidemiological methods, survival analysis, medical statistics, and managing biostatistics.
Experimental Design and Analysis of Variance
Year 1Semester 13 creditsCore
Covers completely randomised designs, randomised complete blocks, factorial designs, split-plot designs, response surface methodology, and managing experimental design.
Research Methods in Biometry
Year 1Semester 13 creditsCore
Covers research design, data collection, analysis, ethical issues, and conducting biometric research, preparing students for their thesis.
Regression Analysis and Generalised Linear Models
Year 1Semester 23 creditsCore
Examines linear regression, logistic regression, generalised linear models, mixed models, nonlinear regression, and managing regression analysis.
Multivariate Analysis and Data Reduction
Year 1Semester 23 creditsCore
Covers PCA, factor analysis, cluster analysis, discriminant analysis, MANOVA, canonical correlation, and managing multivariate analysis.
Statistical Computing with R and SAS
Year 1Semester 23 creditsCore
Examines R programming, SAS, data manipulation, simulation, visualisation, reproducible research, and managing statistical computing.
Survey Sampling and Statistical Methods
Year 1Semester 23 creditsCore
Covers sampling design, stratification, cluster sampling, weighting, non-response, estimation, and managing survey statistics.
Biological Modelling and Simulation
Year 2Semester 13 creditsCore
Examines population modelling, growth curves, ecological modelling, simulation, dynamical systems, and managing biological modelling.
Quantitative Genetics and Breeding Analysis
Year 2Semester 13 creditsCore
Covers genetic data analysis, breeding values, heritability estimation, QTL analysis, selection indices, and managing quantitative genetics.
Statistical Quality Control and Data Management
Year 2Semester 13 creditsCore
Examines control charts, process capability, acceptance sampling, data management, database systems, and managing statistical quality control.
Environmental and Spatial Statistics
Year 2Semester 13 creditsCore
Covers spatial statistics, geostatistics, environmental modelling, climate data analysis, ecological statistics, and managing environmental statistics.
Research Thesis
Year 2Semester 29 creditsCore
Original research thesis on a biometry topic, demonstrating mastery of research methods and biometric knowledge, assessed through written submission and oral defence.

Specialisations

Agricultural Statistics
Focuses on agricultural statistics, covering experimental design, field trial analysis, crop yield modelling, agricultural surveys, and managing agricultural statistics.
Biostatistics
Examines biostatistics, covering clinical trials, epidemiological methods, survival analysis, medical statistics, and managing biostatistics.
Environmental Statistics
Covers environmental statistics, covering spatial statistics, environmental modelling, ecological data analysis, climate statistics, and managing environmental statistics.
Statistical Computing
Focuses on statistical computing, covering R programming, SAS, data mining, statistical software, simulation, and managing statistical computing.
Survey Statistics
Examines survey statistics, covering sampling design, survey methodology, questionnaire design, official statistics, and managing survey statistics.
Quantitative Genetics
Covers quantitative genetics, covering genetic data analysis, breeding values, heritability estimation, QTL analysis, and managing quantitative genetics statistics.

A day as a student

A typical day during the MSc in Biometry programme combines lectures, computer laboratory sessions, practical workshops, seminars, and independent study. Sessions cover biostatistics, experimental design, regression analysis, multivariate analysis, and statistical computing. Biostatistics sessions examine biological data analysis, clinical trials, epidemiological methods, survival analysis, medical statistics, and managing biostatistics. Experimental design sessions cover completely randomised designs, randomised complete blocks, factorial designs, split-plot designs, response surface methodology, and managing experimental design. Regression analysis sessions cover linear regression, logistic regression, generalised linear models, mixed models, nonlinear regression, and managing regression analysis. Multivariate analysis sessions cover PCA, factor analysis, cluster analysis, discriminant analysis, MANOVA, and managing multivariate analysis. Statistical computing sessions cover R programming, SAS, data manipulation, simulation, visualisation, reproducible research, and managing statistical computing. Survey sampling sessions cover sampling design, stratification, cluster sampling, weighting, estimation, and managing survey statistics. Biological modelling sessions cover population modelling, growth curves, ecological modelling, simulation, and managing biological modelling. Quantitative genetics sessions cover genetic data analysis, breeding values, heritability estimation, QTL analysis, and managing quantitative genetics. Research methods sessions prepare students for their thesis, covering research design, data collection, and analysis. Computer laboratory sessions provide hands-on experience with R, SAS, data analysis, simulation, and statistical modelling. Seminars and discussion groups provide opportunities for debating current issues in biometry. Guest lectures from experienced biometricians, statisticians, and researchers provide practical insights. The programme culminates in a research thesis on a biometry topic.

The trade offs

In its favour

Steady demand for biometry professionals with growing data-driven research, evidence-based policy, agricultural research, and clinical trials in Kenya.
Programme combines statistics with biology, agriculture, and computing, providing versatile interdisciplinary skills.
KU offers competitive fees at approximately KES 326,400/year for related statistics programmes.
Strong industry links with KALRO, KNBS, and agricultural research institutions providing collaboration and career opportunities.

Against it

UoN fees are higher at approximately KES 354,000/year for science-based master's programmes.
Only UoN confirmed offering this specific programme name, limiting institutional choice.
No private university confirmed offering this programme, limiting options.
Programme requires statistics, mathematics, or biological sciences background, which limits access for non-related graduates.

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

Public148k to 388k

148k at Kenyatta University388k at University of Nairobi

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 Maths MSc ~388K/yr (uonbi.ac.ke). KU ~148K/yr (international.ku.ac.ke). Private: unverified.

HELB postgraduate loans are available for Kenyan students. UoN may offer postgraduate bursaries for eligible students. KALRO and agricultural research organisations may provide research support for biometry professionals. Some organisations may sponsor staff for postgraduate study.

Funding options

HELB Postgraduate Loan
UoN Postgraduate Bursary
KALRO Research Support

Scholarships

3 recorded

HELB Postgraduate Loan
LoanKsh 200,000Kenyan
Kenyan students pursuing postgraduate studies at recognised universities.
UoN Postgraduate Bursary
ScholarshipKsh 100,000Kenyan
University of Nairobi offers postgraduate bursaries for eligible students.
KALRO Research Support
GrantKsh 200,000Kenyan
KALRO and partner organisations may provide research support for biometry and agricultural research professionals.

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 Upper Second Class Honours in statistics, mathematics, biological sciences, or agriculture. Lower Second Division holders with relevant experience are considered. 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, problem sets, computer laboratory reports, seminar presentations, and class participation.
Written Examinations
Examination40% of the mark
Written examinations covering biostatistics, experimental design, regression, multivariate analysis, and statistical computing.
Computer Laboratory and Project Assessment
Practical30% of the mark
Practical assessment through computer laboratory exercises, data analysis projects, statistical computing, simulation, and demonstrating biometric skills.
Research Thesis
Research100% of the mark
Original research thesis on a biometry topic, demonstrating mastery of research methods and biometric knowledge, assessed through written submission and oral defence.

Accreditation

The programme is accredited by the Commission for University Education (CUE). University of Nairobi (public) offers MSc in Biometry through the Department of Plant Science and Crop Protection at Chiromo Campus. The programme meets CUE standards for postgraduate training in biometry and biological statistics. Graduates are eligible for KNBS statistical certification and IBS biometric certification.

Accredited by

Commission for University Education (CUE)
Academic accreditationRequired
Programme accredited by CUE. University of Nairobi (public) offers MSc in Biometry through the Department of Plant Science and Crop Protection at Chiromo Campus. The programme meets CUE standards for postgraduate training in biometry and biological statistics. Graduates are eligible for KNBS statistical certification and IBS biometric certification.

Where it leads

The roles it opens, and what you leave with.

Where graduates go

6 roles

Biometrician
Moderate demandKsh 130,000 to Ksh 550,000
Conducts biometric analysis, overseeing experimental design, data analysis, statistical modelling, and managing biometric research.
Statistician
Moderate demandKsh 130,000 to Ksh 550,000
Conducts statistical analysis, overseeing data collection, analysis, modelling, interpretation, and managing statistical work.
Data Analyst
High demandKsh 130,000 to Ksh 550,000
Analyses biological and agricultural data, overseeing data cleaning, analysis, visualisation, reporting, and managing data analysis.
Research Methodologist
Moderate demandKsh 130,000 to Ksh 550,000
Designs research methodologies, overseeing experimental design, sampling, analysis, and managing research methodology.
Quantitative Analyst
Moderate demandKsh 140,000 to Ksh 600,000
Applies quantitative methods, overseeing statistical modelling, data analysis, quantitative research, and managing quantitative analysis.
Biometry Lecturer
Moderate demandKsh 120,000 to Ksh 500,000
Teaches biometry and statistics at university or college level, overseeing instruction, research, laboratory supervision, and academic supervision.

Graduate outcomes

Where these fields lead

8 careers

Tools you will learn

R
SoftwarePrimary
R statistical software for data analysis, covering statistical modelling, data visualisation, experimental design analysis, and managing statistical computing.
SAS
Software
SAS for advanced analytics, covering data analysis, statistical modelling, biostatistics, and managing statistical computing.
Python
Software
Python for data science, covering pandas, numpy, scipy, data manipulation, and managing statistical computing.
GenStat
Software
GenStat for biological data analysis, covering experimental design, REML, spatial analysis, and managing biological statistics.

Industry links

Common misconceptions

Biometry is just about calculating averages.
Biometry covers comprehensive statistical theory, experimental design, multivariate analysis, statistical computing, and biological modelling beyond just calculating averages.
This programme is only for those who are good at maths.
Biometry skills are valuable for anyone interested in biological research, agricultural science, data analysis, evidence-based decision-making, and quantitative problem-solving.
Biometry has limited career prospects in Kenya.
With growing data-driven research, evidence-based policy, agricultural research, and clinical trials, demand for biometry professionals is steady in Kenya.
Statistical computing is just about using Excel.
Statistical computing covers comprehensive R programming, SAS, data mining, simulation, reproducible research, and advanced statistical software.
Experimental design is just about setting up experiments.
Experimental design covers comprehensive design principles, randomisation, replication, blocking, power analysis, and efficiency optimisation.
Biometry is the same as biostatistics.
While related, biometry focuses more on biological and agricultural data analysis, experimental design, and quantitative genetics, while biostatistics focuses more on health and medical data.