Technology

Master of Science in Data Science and Artificial Intelligence

This Master's programme prepares IT and computer science professionals for advanced data science and artificial intelligence practice. It focuses on data science, AI algorithms, deep learning, and intelligent systems — equipping graduates for senior roles in AI engineering, data science, and intelligent systems development.

Students learn to apply data science methods, develop AI algorithms, implement deep learning networks, build intelligent systems, and conduct data science and AI research. They develop skills in data science, AI algorithm development, deep learning, natural language processing, and intelligent systems design.

Throughout the programme, students examine how Kenya's technology landscape — with AI adoption, data-driven innovation, demand for intelligent systems, and need for combined data science and AI expertise — requires professionals who can develop AI-powered data solutions. They learn how data science and AI expertise serves technology companies, banks, government, research institutions, and startups in leveraging AI and data science for innovation.

The programme combines coursework, AI projects, data science labs, and a thesis or research project over two years. Students study data science, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, big data analytics, research methods, and AI ethics.

Graduates pursue careers as AI engineers, data scientists, machine learning engineers, deep learning specialists, AI research scientists, and university lecturers across technology companies, banks, government, research institutions, startups, and universities in Kenya and the region.

The programme is ideal for computer science and IT graduates who want to build data science and AI expertise for senior AI engineering and intelligent systems development roles.

Duration: 4 years
Public, up to: Ksh 254,100
Job market: Very high

The programme

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

Practicalities

Study mode: Full-time
Attachment: 0 months
Average class: 25 students
Award: Masters

What you study

8 subjects

Machine Learning and Deep Learning
Big Data Analytics
Natural Language Processing
Computer Vision
Statistical Modeling
Data Engineering
AI Ethics and Governance
Data Visualization and Storytelling

Modules

12 in the programme

Machine Learning Algorithms and Theory
Year 1Semester 13 creditsCore
Supervised learning (regression, classification, ensemble methods), unsupervised learning (clustering, dimensionality reduction), and probabilistic models.
Deep Learning and Neural Networks
Year 1Semester 23 creditsCore
Convolutional neural networks, recurrent networks, attention mechanisms, and transfer learning for image and sequence data.
Big Data Technologies and Engineering
Year 1Semester 33 creditsCore
Spark, Hadoop, distributed databases, data pipelines, and ETL processes for large-scale data processing.
Natural Language Processing
Year 2Semester 13 creditsCore
Text preprocessing, word embeddings, language models, sentiment analysis, and LLM applications.
Computer Vision and Image Analysis
Year 2Semester 23 creditsCore
Image processing, object detection, segmentation, and deep learning for visual data.
Statistical Modelling and Inference
Year 2Semester 33 creditsCore
Bayesian methods, hypothesis testing, causal inference, and experiment design.
Data Visualization and Communication
Year 3Semester 13 creditsCore
Storytelling with data, visualization design, dashboards, and presenting findings to non-technical audiences.
AI Ethics, Fairness, and Governance
Year 3Semester 23 creditsCore
Bias in ML, fairness metrics, explainability, regulatory compliance, and responsible AI.
Elective: Time Series Analysis and Forecasting
Year 3Semester 33 creditsCore
ARIMA, SARIMA, Prophet, and deep learning for forecasting applications in finance and agriculture.
Elective: Recommendation Systems
Year 4Semester 13 creditsCore
Collaborative filtering, content-based systems, and matrix factorization for personalization.
Research Methods and Seminar
Year 4Semester 23 creditsCore
Research design, literature review, academic writing, and presentation skills.
Master's Thesis
Year 4Semester 36 creditsCore
Original research project in data science or AI with publication-ready results and defended thesis.

Specialisations

Business Analytics and AI Strategy
Focus on using AI to drive business value and data strategy.
Big Data Analytics
Focus on large-scale data processing, analytics, and business intelligence.
Natural Language Processing
Focus on language models, text analysis, and NLP applications.
Machine Learning Engineering
Focus on building scalable ML systems and production deployments.
Deep Learning and Computer Vision
Focus on neural networks and image/video analysis applications.

A day as a student

Students work with real-world datasets from Safaricom, Equity Bank, and Kenya's agriculture sector. Morning lectures on advanced ML algorithms, NLP, or computer vision are followed by afternoon labs where students build and deploy models using Python (TensorFlow, PyTorch, scikit-learn), SQL, and cloud platforms (GCP, AWS). Group projects involve end-to-end data science workflows — data collection, cleaning, feature engineering, model training, evaluation, and deployment. Access to GPUs and cloud credits for computationally intensive tasks. Weekly data science seminars featuring industry practitioners. Students publish findings and present at conferences.

The trade offs

In its favour

Exceptional demand and high salaries across industries — Kenya's fintech and telecom sectors are data-hungry.
Versatile skills applicable across sectors — healthcare, agriculture, finance, and e-commerce all need data science.
Remote work is standard in data science — access to international opportunities and salaries.

Against it

Rapidly evolving field — requires continuous learning and staying current with new tools and techniques.
Success depends on quality of datasets — can be frustrating when working with poor or insufficient data.
Models can fail in production — debugging and maintaining ML systems can be challenging.

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 254k

94k at Open University of Kenya254k at JKUAT

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, JKUAT 254,100/yr MSc AI (jkuat.ac.ke). Private unverified.

HELB postgraduate loans available. Google Cloud, Facebook, and Safaricom offer scholarships. Companies often sponsor employees pursuing the degree to build internal data science capacity.

Funding options

HELB Postgraduate Loan
Google Cloud Scholarship
Safaricom Skills Academy
Facebook Developer Circle Scholarships

Scholarships

5 recorded

Safaricom Skills Academy Scholarship
ScholarshipKsh 500,000
Tech professionals pursuing advanced data and AI skills.
Facebook Developer Circle Scholars
ScholarshipKsh 400,000
Developers pursuing AI and machine learning education.
HELB Postgraduate Loan
LoanKsh 200,000Kenyan
Kenyan citizens pursuing postgraduate studies.
MasterCard Foundation Scholars Program
ScholarshipKsh 1,200,000
Economically disadvantaged students with exceptional potential.
Google Cloud Scholarship
ScholarshipKsh 600,000
Students pursuing data science and cloud certifications.

Getting in

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

What you need

KCSE mean grade: Second Upper Division (2.1) or equivalent
Alternative entry: Bachelor's degree in Engineering, Physics, or Economics with strong quantitative background and programming experience. Professional experience (2+ years) in data analysis or IT can compensate for slightly lower grades.

Bachelor's degree in Computer Science, Statistics, Mathematics, or related field
2.1 or higher
Strong programming skills in Python, R, or Java
demonstrated in bachelor's
Mathematics (calculus, linear algebra, statistics)
background required
English language proficiency
IELTS 6.5 or TOEFL 90+

How you are assessed

4 components

Practical Projects and Assignments
Cat30% of the mark
Hands-on projects — data analysis, model building, and deployment.
Coursework and Examinations
Exam35% of the mark
End-of-semester exams and assignments on ML algorithms, statistics, and tools.
Thesis Defence and Presentation
Research5% of the mark
Oral defence of thesis before examination panel.
Research Thesis
Research30% of the mark
Original research thesis on a data science or AI topic with implementation and validation.

Accreditation

Accredited by CUE and recognised by the Computer Society of Kenya. Curriculum aligns with international data science standards and industry best practices. Research outputs typically include conference papers, published models, and open-source contributions.

Accredited by

Computer Society of Kenya
Professional accreditation
Peer review and recognition by professional computing body.
Commission for University Education
Academic accreditationRequired
Mandatory accreditation for postgraduate programmes in Kenya.

Where it leads

The roles it opens, and what you leave with.

Where graduates go

5 roles

Data Scientist
Very high demandKsh 250,000 to Ksh 1,200,000
Build ML models and analyse data at tech companies, fintechs, and enterprises — drive data-driven decisions.
Data Engineer
High demandKsh 220,000 to Ksh 900,000
Design and build data infrastructure and pipelines for large-scale data processing.
AI Research Scientist
High demandKsh 200,000 to Ksh 1,000,000
Conduct cutting-edge AI research at tech labs, universities, and research institutes.
Analytics Manager/Lead
High demandKsh 300,000 to Ksh 1,500,000
Lead analytics teams and drive data strategy at enterprises — combine technical and leadership skills.
Machine Learning Engineer
Very high demandKsh 280,000 to Ksh 1,500,000
Design, build, and deploy ML systems in production — focus on scalability and reliability.

Graduate outcomes

Graduates work as Data Scientists, ML Engineers, Data Engineers, and AI Specialists at tech companies (Google Kenya, Microsoft, Amazon Web Services), fintechs (Safaricom, Equity Bank, Flutterwave, PesaPal), telecommunications (Airtel, Vodafone), and insurance companies (AIG Kenya, Madison). Starting salaries range from KES 250,000–450,000, with experienced data scientists earning KES 500,000–1,500,000+. Many pursue PhD research, launch AI startups, or work as international consultants earning USD 5,000–20,000+ monthly.

Where these fields lead

8 careers

Certifications

Industry links

Common misconceptions

Data science is just machine learning.
Data science encompasses statistics, programming, domain expertise, and storytelling — ML is just one tool.
You need a PhD in statistics or math to study data science.
Strong programming skills and mathematical intuition matter more than advanced pure mathematics.
Data scientists spend all their time building models.
Most time goes to data collection, cleaning, and preparation (70%) — modelling is only 20% of the work.
This programme has limited career opportunities.
Graduates pursue diverse career paths across multiple sectors in Kenya and internationally.