Technology

Master of Science in Data Sciences and Analytics

The Master of Science in Data Sciences and Analytics is a postgraduate programme that trains graduates in data science, statistical analytics, and business intelligence. The programme prepares graduates for advanced careers in data science, analytics, and data-driven decision-making across industries.

Core areas include data science, statistical analysis, business analytics, computational statistics, data mining, machine learning, data visualisation, predictive modelling, big data analytics, and research methods. Students engage with theoretical foundations and practical data science applications with two specialisations: Business Analytics and Computational Statistics.

The programme is offered by University of Nairobi, Kenyatta University, and Moi University, with Taita Taveta University as a college. Strathmore University offers a related MSc in Data Science and Analytics at KES 1,207,000 total with Graduate Entrance Exam requirement. UoN offers through its School of Computing and Informatics and Department of Management Science.

Students develop competencies in data analysis, statistical modelling, business analytics, computational statistics, machine learning, data visualisation, predictive modelling, and research. The programme includes coursework, laboratory practicals, examinations, supervised research, and thesis over two academic years.

The programme is delivered over two years with full-time and part-time study modes. UoN requires Upper Second Class Honours in numerate subjects including mathematics, statistics, computer science, economics, engineering, or suitable science degree. Strathmore requires GEE written exam and oral interview.

Graduates pursue careers as data scientists, business analysts, statistical analysts, analytics managers, researchers, and lecturers in technology companies, financial institutions, consulting firms, government agencies, and universities.

Skills Required

Data Science and Statistical Analysis
Business Analytics and Intelligence
Computational Statistics and Modelling
Machine Learning and Predictive Modelling
Data Mining and Knowledge Discovery
Data Visualisation and Communication
Big Data Analytics and Processing
Statistical Programming (R/Python)
Data-Driven Decision Making
Data Science Research Methods

Key Subjects

Data Science
Statistical Analysis
Business Analytics
Computational Statistics
Data Mining
Machine Learning
Data Visualisation
Predictive Modelling
Big Data Analytics
Data Science Research Methods

Certifications

CSK Professional Membership
IASC Professional Membership

Specializations

Business Analytics

Focuses on business analytics, extracting insights from business data, improving business processes and outcomes, and using data for business intelligence and decision-making.

Computational Statistics

Examines computational statistics, improving data science algorithms, statistical computing, and transitioning into data science research and advanced analytics.

Data Mining

Covers data mining, pattern discovery, association rules, clustering, classification, and discovering patterns and knowledge in large datasets.

Machine Learning

Focuses on machine learning, supervised and unsupervised learning, model development, and building predictive models from data.

Big Data Analytics

Examines big data analytics, distributed processing, Hadoop, Spark, NoSQL, and processing and analysing large-scale datasets.

Statistical Modelling

Covers statistical modelling, regression analysis, Bayesian methods, time series, and applying statistical models to data analysis and prediction.

Duration: 2 years
Public, up to: Ksh 187,200
Private, up to: Ksh 603,500
Job market: Very 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: 25 students
Award: Masters

What you study

10 subjects

Data Science
Statistical Analysis
Business Analytics
Computational Statistics
Data Mining
Machine Learning
Data Visualisation
Predictive Modelling
Big Data Analytics
Data Science Research Methods

Modules

12 in the programme

Foundations of Data Science
Year 1Semester 13 creditsCore
Examines foundations of data science, data science lifecycle, data pipelines, data cleaning, feature engineering, and extracting insights from data.
Statistical Analysis and Inference
Year 1Semester 13 creditsCore
Covers statistical analysis, statistical methods, hypothesis testing, regression, Bayesian analysis, and applying statistics to data analysis.
Data Mining and Knowledge Discovery
Year 1Semester 13 creditsCore
Examines data mining, pattern discovery, association rules, clustering, classification, and discovering patterns and knowledge in large datasets.
Business Analytics and Intelligence
Year 1Semester 23 creditsCore
Covers business analytics, business intelligence, KPIs, dashboards, business metrics, and using data for business decision-making and intelligence.
Computational Statistics
Year 1Semester 23 creditsCore
Examines computational statistics, statistical computing, simulation, Monte Carlo methods, computational algorithms, and implementing statistical methods computationally.
Machine Learning for Analytics
Year 1Semester 23 creditsCore
Covers machine learning, supervised learning, unsupervised learning, model evaluation, and building predictive models from data.
Predictive Modelling and Forecasting
Year 2Semester 13 creditsCore
Examines predictive modelling, forecasting, time series analysis, predictive model building, and using data for prediction and forecasting.
Big Data Analytics
Year 2Semester 13 creditsCore
Covers big data analytics, Hadoop, Spark, distributed processing, NoSQL databases, and processing and analysing large-scale datasets.
Data Visualisation and Communication
Year 2Semester 13 creditsCore
Examines data visualisation, visual analytics, dashboards, interactive visualisations, and communicating insights through visual representations.
Statistical Programming
Year 2Semester 13 creditsCore
Covers statistical programming, R, Python, SQL, data manipulation, data wrangling, and programming for data analysis and statistical computing.
Data Science Research Methods
Year 2Semester 23 creditsCore
Examines research methods for data science, experimental design, evaluation methods, and conducting data science research, preparing students for their thesis.
Research Thesis
Year 2Semester 26 creditsCore
Original research thesis on a data sciences and analytics topic, demonstrating mastery of research methods and data science knowledge, assessed through written submission and oral defence.

Specialisations

Business Analytics
Focuses on business analytics, extracting insights from business data, improving business processes and outcomes, and using data for business intelligence and decision-making.
Computational Statistics
Examines computational statistics, improving data science algorithms, statistical computing, and transitioning into data science research and advanced analytics.
Data Mining
Covers data mining, pattern discovery, association rules, clustering, classification, and discovering patterns and knowledge in large datasets.
Machine Learning
Focuses on machine learning, supervised and unsupervised learning, model development, and building predictive models from data.
Big Data Analytics
Examines big data analytics, distributed processing, Hadoop, Spark, NoSQL, and processing and analysing large-scale datasets.
Statistical Modelling
Covers statistical modelling, regression analysis, Bayesian methods, time series, and applying statistical models to data analysis and prediction.

A day as a student

A typical day during the MSc Data Sciences and Analytics programme begins with morning lectures on statistical analysis, data mining, or machine learning. Students engage in theoretical discussions on data science methods, statistical theory, and analytics techniques. Data science sessions cover the data science lifecycle, data pipelines, data cleaning, feature engineering, and extracting insights from data. Statistical analysis sessions examine statistical methods, hypothesis testing, regression, Bayesian analysis, and applying statistics to data analysis. Business analytics sessions cover business intelligence, KPIs, dashboards, business metrics, and using data for business decision-making. Computational statistics sessions involve statistical computing, simulation, Monte Carlo methods, computational algorithms, and implementing statistical methods computationally. Data mining sessions cover pattern discovery, association rules, clustering, classification, and discovering patterns in large datasets. Machine learning sessions examine supervised learning, unsupervised learning, model evaluation, and building predictive models. Data visualisation sessions cover visual analytics, dashboards, interactive visualisations, and communicating insights visually. Predictive modelling sessions involve forecasting, time series, predictive model building, and using data for prediction. Big data sessions cover Hadoop, Spark, distributed processing, NoSQL, and processing large-scale datasets. Programming sessions involve R, Python, SQL, data manipulation, and programming for analytics. Laboratory sessions provide hands-on experience with analytics tools, programming, data analysis projects, and real datasets. Research methodology sessions prepare students for their thesis, covering data science research methods, experimental design, and data analysis. Guest lectures from data science industry professionals, KNBS statisticians, and analytics consultants provide real-world insights. The two-year programme culminates in a research thesis.

The trade offs

In its favour

Kenya's fintech growth, digital transformation, data-driven governance, and KNBS data initiatives create very high demand for qualified data science and analytics professionals.
Programme offers two specialisations — Business Analytics and Computational Statistics — catering to both business-focused and research-focused career paths.
UoN accepts graduates from diverse numerate backgrounds including mathematics, statistics, economics, engineering, and computer science.
Strong career opportunities with technology companies, financial institutions, consulting firms, KNBS, government agencies, and international organisations.

Against it

Programme requires strong quantitative background in mathematics or statistics, which may exclude some graduates.
Strathmore's related programme at KES 1,207,000 total may be expensive, though public university options are more affordable.
Strathmore requires Graduate Entrance Exam (GEE) with written exam and oral interview, adding complexity to admission.
Rapidly evolving field may require frequent curriculum updates to remain current with industry tools and technologies.

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

Public187k to 187k

187k at Moi University187k at Moi University

Private195k to 604k

195k at Kabarak University604k 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

Moi ~187,200/yr (worldscholarshipforum.com). Kabarak 195,200/yr (kabarak.ac.ke). Strathmore 603,500/yr.

HELB postgraduate loans are available for Kenyan students. KNBS supports statistical training and capacity building. IEEE provides big data research grants. The programme's data science focus attracts industry and government funding.

Funding options

HELB Postgraduate Loan
KNBS Statistical Training
IEEE Big Data Grants

Scholarships

3 recorded

HELB Postgraduate Loan
LoanKsh 200,000Kenyan
Kenyan students pursuing postgraduate data science and analytics studies at recognised universities.
KNBS Statistical Training
GrantKsh 300,000Kenyan
KNBS supports statistical training and capacity building, providing funding for postgraduate training in statistics and data science.
IEEE Big Data Grants
GrantKsh 300,000
IEEE provides big data research grants for research, conference attendance, and professional development in big data and analytics.

Getting in

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

What you need

KCSE mean grade: N/A (Postgraduate)
Alternative entry: The programme accepts graduates from mathematics, statistics, computer science, economics, engineering, actuarial science, management science, and suitable science degrees. Two-year programme with full-time and part-time options. Strathmore requires Graduate Entrance Exam (GEE) — written exam (English comprehension, arithmetic, essay) and oral interview, every Wednesday and Friday at 2pm. KES 2,500 interview fee. Application requires two reference forms, certified transcripts, KCSE certificate, CV, passport photos, and ID copy.

How you are assessed

4 components

Coursework and Assignments
Coursework30% of the mark
Continuous assessment through coursework assignments, data analysis projects, programming exercises, case studies, and class participation.
Written Examinations
Examination30% of the mark
Written examinations covering statistical analysis, data mining, machine learning, and computational statistics, conducted at end of each semester.
Practical and Laboratory Assessment
Practical40% of the mark
Assessment of practical and laboratory skills including data analysis projects, programming, visualisation, model building, and project reports.
Research Thesis
Research100% of the mark
Original research thesis on a data sciences and analytics topic, demonstrating mastery of research methods and data science knowledge, assessed through written submission and oral defence.

Accreditation

The programme is accredited by the Commission for University Education (CUE). UoN offers through its School of Computing and Informatics. TTU also offers the programme. The programme meets CUE standards for postgraduate data science and analytics training. KNBS sets standards for statistical practice in Kenya.

Accredited by

Commission for University Education (CUE)
Academic accreditationRequired
Programme accredited by CUE. Offered by University of Nairobi, Kenyatta University, Moi University, and Taita Taveta University. The programme meets CUE standards for postgraduate data science and analytics training.
Kenya National Bureau of Statistics (KNBS)
Professional accreditation
KNBS sets standards for statistical practice in Kenya. Graduates working in official statistics may collaborate with KNBS on data and statistics initiatives.

Where it leads

The roles it opens, and what you leave with.

Where graduates go

6 roles

Data Scientist
Very high demandKsh 180,000 to Ksh 700,000
Builds predictive models, applies machine learning, develops data pipelines, and extracts advanced insights from data for organisations.
Business Analyst
High demandKsh 130,000 to Ksh 550,000
Analyses business data, creating insights, building dashboards, and supporting data-driven business decision-making and process improvement.
Statistical Analyst
High demandKsh 120,000 to Ksh 500,000
Conducts statistical analysis, building statistical models, designing experiments, and applying statistical methods to data analysis in organisations.
Analytics Manager
High demandKsh 200,000 to Ksh 800,000
Manages analytics teams, overseeing analytics projects, data initiatives, and driving data-driven culture in organisations.
Data Science Researcher
Moderate demandKsh 150,000 to Ksh 600,000
Conducts data science research, developing new algorithms, improving analytics methods, and contributing to data science knowledge.
University Lecturer
High demandKsh 130,000 to Ksh 500,000
Teaches data science and analytics in universities, conducting research and training future data science professionals.

Graduate outcomes

Graduates pursue careers as data scientists, business analysts, and statistical analysts in technology companies, financial institutions, and government agencies.

Where these fields lead

8 careers

Tools you will learn

R
SoftwarePrimary
R statistical programming language for statistical analysis, data visualisation, ggplot, statistical modelling, and computational statistics.
Python
SoftwarePrimary
Python programming language for data analysis, machine learning, data manipulation with pandas, NumPy, scikit-learn, and data science libraries.
Tableau
Software
Data visualisation and business intelligence tool for creating interactive dashboards, reports, and visual analytics for business intelligence.
Apache Spark
Software
Big data processing framework for large-scale data processing, distributed analytics, and handling big data workloads.

Industry links

Common misconceptions

Data sciences and analytics is just about using Excel.
The programme involves statistical theory, machine learning, big data technologies, computational statistics, and programming, far beyond Excel.
This programme is the same as computer science.
While related, data sciences and analytics focuses on statistical analysis, data mining, and business intelligence, while computer science covers broader computing theory and software.
There is limited demand for data science professionals in Kenya.
Kenya's fintech growth, digital transformation, data-driven governance, and KNBS data initiatives create very high demand for qualified data science and analytics professionals.
This programme is only for mathematics or statistics graduates.
UoN accepts graduates from mathematics, statistics, computer science, economics, engineering, actuarial science, management science, and suitable science degrees.
Data science is just a trend that will fade.
Data science is a fundamental discipline driving digital transformation, AI, and evidence-based decision-making across all sectors, with growing demand globally.
A master's in data sciences is redundant after a bachelor's in mathematics or statistics.
The master's provides advanced computational skills, machine learning, big data technologies, and career progression to senior data science and analytics positions.