Technology

Master of Science in Data Science

The Master of Science in Data Science is a postgraduate programme that prepares professionals for advanced practice in data analytics, machine learning, and big data technologies. The programme combines statistical and computational theory with practical data science application.

Core areas include data mining, machine learning, statistical inference, big data analytics, data visualisation, time series analysis, text analytics, data storage and retrieval, research methods, and thesis. Students engage with both data science theory and practical programming and analytics work through coursework and research.

The programme is offered by the University of Nairobi, the Open University of Kenya, and Kenyatta University as public universities, and Strathmore University and KCA University as private institutions. Programmes are offered over two academic years through full-time and part-time modes of study.

Students develop competencies in data mining, machine learning, statistical inference, big data analytics, data visualisation, time series analysis, text analytics, data storage and retrieval, and research methods. The programme includes coursework, examinations, practical projects, and a supervised research thesis.

The Open University of Kenya charges KES 93,750/year (KES 187,500 total). UoN charges approximately KES 365,500/year for science-based masters. KCA University charges approximately KES 355,035/year. Strathmore University charges approximately KES 476,250/year (KES 952,500 total). Entry requires a Bachelor's degree with Second Class Honours Upper Division in mathematics, statistics, computer science, IT, economics, or related fields from a recognised university.

Graduates pursue careers as data scientists, data analysts, machine learning engineers, business intelligence analysts, data engineers, and lecturers in data science across financial institutions, technology companies, consulting firms, government agencies, and universities.

Skills Required

Data Mining and Pattern Discovery
Machine Learning and Predictive Modelling
Statistical Inference and Hypothesis Testing
Big Data Analytics and Distributed Computing
Data Visualisation and Dashboard Development
Time Series Analysis and Forecasting
Text Analytics and Natural Language Processing
Data Storage, Retrieval and Database Management
Research Methods in Data Science
Academic Writing and Thesis Research

Key Subjects

Data Mining and Pattern Discovery
Machine Learning and Predictive Modelling
Statistical Inference and Hypothesis Testing
Big Data Analytics and Distributed Computing
Data Visualisation and Dashboard Development
Time Series Analysis and Forecasting
Text Analytics and Natural Language Processing
Data Storage, Retrieval and Database Management
Research Methods in Data Science
Thesis Research

Certifications

Data Science Africa Certification
IEEE Computer Society Membership

Specializations

Business Analytics

Focuses on business analytics, covering business intelligence, data-driven decision making, customer analytics, financial analytics, and managing business analytics.

Machine Learning

Examines machine learning, covering supervised learning, unsupervised learning, deep learning, model evaluation, and managing machine learning.

Big Data Analytics

Covers big data analytics, covering distributed computing, Hadoop, Spark, NoSQL, stream processing, and managing big data.

Computational Statistics

Focuses on computational statistics, covering Bayesian statistics, optimisation, simulation, statistical computing, and managing computational statistics.

Data Visualisation

Examines data visualisation, covering visual analytics, dashboards, interactive visualisation, storytelling, and managing data visualisation.

Text Analytics and NLP

Covers text analytics and NLP, covering text mining, sentiment analysis, topic modelling, language processing, and managing text analytics.

Duration: 2 years
Public, up to: Ksh 362,500
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: 30 students
Award: Masters

What you study

10 subjects

Data Mining and Pattern Discovery
Machine Learning and Predictive Modelling
Statistical Inference and Hypothesis Testing
Big Data Analytics and Distributed Computing
Data Visualisation and Dashboard Development
Time Series Analysis and Forecasting
Text Analytics and Natural Language Processing
Data Storage, Retrieval and Database Management
Research Methods in Data Science
Thesis Research

Modules

12 in the programme

Data Mining and Pattern Discovery
Year 1Semester 13 creditsCore
Examines pattern discovery, association rules, clustering, classification, anomaly detection, and managing data mining.
Machine Learning and Predictive Modelling
Year 1Semester 13 creditsCore
Covers supervised learning, unsupervised learning, deep learning, model evaluation, feature engineering, and managing machine learning.
Research Methods in Data Science
Year 1Semester 13 creditsCore
Covers research design, data collection, analysis, ethical issues, and conducting data science research, preparing students for their thesis.
Statistical Inference and Hypothesis Testing
Year 1Semester 23 creditsCore
Examines hypothesis testing, estimation, Bayesian methods, regression, correlation, and managing statistical inference.
Big Data Analytics and Distributed Computing
Year 1Semester 23 creditsCore
Covers distributed computing, Hadoop, Spark, NoSQL, stream processing, and managing big data.
Data Visualisation and Dashboard Development
Year 1Semester 23 creditsCore
Examines visual analytics, dashboards, interactive visualisation, storytelling, design, and managing data visualisation.
Time Series Analysis and Forecasting
Year 1Semester 23 creditsCore
Covers forecasting, ARIMA, seasonal models, volatility, spectral analysis, and managing time series.
Text Analytics and Natural Language Processing
Year 2Semester 13 creditsCore
Examines text mining, sentiment analysis, topic modelling, NLP, language models, and managing text analytics.
Data Storage, Retrieval and Database Management
Year 2Semester 13 creditsCore
Covers databases, data warehouses, ETL, data lakes, data governance, and managing data storage.
Deep Learning and Neural Networks
Year 2Semester 13 creditsCore
Examines neural networks, CNN, RNN, transformers, transfer learning, and managing deep learning.
Ethics, Privacy and Data Governance
Year 2Semester 13 creditsCore
Covers data ethics, privacy, GDPR, data protection, bias, fairness, and managing data governance.
Research Thesis
Year 2Semester 29 creditsCore
Original supervised research thesis on a data science topic, demonstrating mastery of research methods and data science knowledge, assessed through written submission and oral defence.

Specialisations

Business Analytics
Focuses on business analytics, covering business intelligence, data-driven decision making, customer analytics, financial analytics, and managing business analytics.
Machine Learning
Examines machine learning, covering supervised learning, unsupervised learning, deep learning, model evaluation, and managing machine learning.
Big Data Analytics
Covers big data analytics, covering distributed computing, Hadoop, Spark, NoSQL, stream processing, and managing big data.
Computational Statistics
Focuses on computational statistics, covering Bayesian statistics, optimisation, simulation, statistical computing, and managing computational statistics.
Data Visualisation
Examines data visualisation, covering visual analytics, dashboards, interactive visualisation, storytelling, and managing data visualisation.
Text Analytics and NLP
Covers text analytics and NLP, covering text mining, sentiment analysis, topic modelling, language processing, and managing text analytics.

A day as a student

A typical day during the MSc in Data Science programme combines lectures, laboratory sessions, practical coding workshops, project work, seminars, and independent study. Sessions cover data mining, machine learning, statistical inference, big data, and data visualisation. Data mining sessions examine pattern discovery, association rules, clustering, classification, and managing data mining. Machine learning sessions cover supervised learning, unsupervised learning, deep learning, model evaluation, and managing machine learning. Statistical inference sessions cover hypothesis testing, estimation, Bayesian methods, regression, and managing statistical inference. Big data sessions cover distributed computing, Hadoop, Spark, NoSQL, stream processing, and managing big data. Data visualisation sessions cover visual analytics, dashboards, interactive visualisation, storytelling, and managing data visualisation. Time series sessions cover forecasting, ARIMA, seasonal models, volatility, and managing time series. Text analytics sessions cover text mining, sentiment analysis, topic modelling, NLP, and managing text analytics. Data storage sessions cover databases, data warehouses, ETL, data lakes, and managing data storage. Research methods sessions prepare students for their thesis, covering research design, data collection, and analysis. Laboratory sessions provide hands-on experience with Python, R, SQL, Hadoop, Spark, and visualisation tools. Practical coding workshops provide hands-on experience with machine learning, data mining, and big data processing. Project work provides hands-on experience with real datasets, business problems, and end-to-end data science pipelines. Seminars and discussion groups provide opportunities for debating current issues in data science. Guest lectures from experienced data scientists, industry professionals, and researchers provide practical insights. The programme culminates in a supervised research thesis on a data science topic.

The trade offs

In its favour

Very high demand for data science professionals with growing fintech, digital transformation, AI adoption, and data-driven decision making in Kenya.
Programme offered by five universities (3 public, 2 private), providing wide institutional choice.
Open University of Kenya offers very competitive fees at KES 93,750/year for data science.
Programme covers emerging fields including deep learning, NLP, and big data, ensuring market relevance.

Against it

Strathmore University (private) charges higher fees at approximately KES 476,250/year (KES 952,500 total).
UoN fees estimated at approximately KES 365,500/year, not independently verified for data science specifically.
Programme requires mathematics, statistics, computer science, or related background, which limits access for non-related graduates.
Rapidly evolving technology landscape requires continuous self-learning beyond the curriculum.

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

Public94k to 363k

94k at Open University of Kenya363k at University of Nairobi

Private312k to 604k

312k at KCA 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

OUK 93,750/yr, UoN ~362,500/yr (uonbi.ac.ke). KCA 312,190/yr (kcau.ac.ke). Strathmore 603,500/yr (kenyaplex).

HELB postgraduate loans are available for Kenyan students. Strathmore University may offer scholarships for eligible students. Data Science Africa and technology organisations may provide grants and research support. Some technology companies may sponsor staff for postgraduate study.

Funding options

HELB Postgraduate Loan
Strathmore Scholarships
Data Science Africa Grants

Scholarships

3 recorded

HELB Postgraduate Loan
LoanKsh 200,000Kenyan
Kenyan students pursuing postgraduate studies at recognised universities.
Strathmore Scholarships
ScholarshipKsh 200,000Kenyan
Strathmore University offers scholarships for eligible postgraduate students.
Data Science Africa Grants
GrantKsh 250,000Kenyan
Data Science Africa and partner organisations may provide grants for data science professionals and researchers.

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 mathematics, statistics, computer science, IT, economics, or related fields. 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, coding projects, laboratory reports, seminar presentations, and class participation.
Written Examinations
Examination70% of the mark
Written examinations covering data mining, machine learning, statistical inference, big data, and data visualisation.
Practical Project Assessment
Practical30% of the mark
Practical assessment through coding projects, data analysis, machine learning implementation, and demonstrating data science skills.
Research Thesis
Research100% of the mark
Original supervised research thesis on a data science 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). University of Nairobi, Open University of Kenya, and Kenyatta University (public) and Strathmore University and KCA University (private) offer MSc in Data Science or related programmes. All programmes meet CUE standards for postgraduate training in data science. Graduates are eligible for Data Science Africa certification and IEEE Computer Society membership.

Accredited by

Commission for University Education (CUE)
Academic accreditationRequired
Programme accredited by CUE. University of Nairobi, Open University of Kenya, and Kenyatta University (public) and Strathmore University and KCA University (private) offer MSc in Data Science or related programmes. All programmes meet CUE standards for postgraduate training in data science. Graduates are eligible for Data Science Africa certification and IEEE Computer Society membership.

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 800,000
Conducts data science work, overseeing data analysis, machine learning, modelling, and managing data science.
Data Analyst
Very high demandKsh 150,000 to Ksh 650,000
Analyses data, overseeing data cleaning, analysis, visualisation, reporting, and managing data analysis.
Machine Learning Engineer
Very high demandKsh 180,000 to Ksh 800,000
Develops ML systems, overseeing model development, deployment, monitoring, and managing machine learning engineering.
Business Intelligence Analyst
High demandKsh 150,000 to Ksh 650,000
Develops BI solutions, overseeing dashboards, reporting, analytics, and managing business intelligence.
Data Engineer
High demandKsh 160,000 to Ksh 700,000
Builds data infrastructure, overseeing pipelines, ETL, databases, architecture, and managing data engineering.
Data Science Lecturer
Moderate demandKsh 120,000 to Ksh 500,000
Teaches data science at university or college level, overseeing instruction, research, and academic supervision.

Graduate outcomes

Where these fields lead

8 careers

Tools you will learn

Python
SoftwarePrimary
Python for data science, covering pandas, numpy, scikit-learn, TensorFlow, PyTorch, and managing data science projects.
R
Software
R for statistical analysis, covering statistical modelling, visualisation, time series, and managing statistical analysis.
SQL
Software
SQL for database management, covering queries, joins, optimisation, and managing database systems.
Tableau
Software
Tableau for data visualisation, covering dashboards, interactive visualisation, reporting, and managing data visualisation.

Industry links

Common misconceptions

Data science is just about making charts and graphs.
Data science covers comprehensive data mining, machine learning, statistical inference, big data, NLP, and predictive modelling beyond just visualisation.
This programme is only for computer scientists.
Data science skills are valuable for mathematicians, statisticians, economists, business analysts, scientists, and researchers beyond just computer scientists.
Data science has limited career prospects in Kenya.
With growing fintech, digital transformation, AI adoption, and data-driven decision making, demand for data science professionals is very high in Kenya.
Machine learning is just about using algorithms.
Machine learning covers comprehensive supervised learning, unsupervised learning, deep learning, model evaluation, feature engineering, and deployment.
Big data is just about large datasets.
Big data covers comprehensive distributed computing, Hadoop, Spark, NoSQL, stream processing, and data architecture.
Data visualisation is just about making dashboards.
Data visualisation covers comprehensive visual analytics, interactive visualisation, storytelling, perception, and design principles.