Technology

Master of Science in Data Science and Computational Intelligence

The Master of Science in Data Science and Computational Intelligence is a postgraduate programme that trains graduates in data science, computational intelligence, and artificial intelligence. The programme prepares graduates for advanced careers in data science, AI development, and intelligent systems engineering.

Core areas include data science, computational intelligence, machine learning, artificial intelligence, neural networks, deep learning, natural language processing, computer vision, optimisation algorithms, and research methods. Students engage with theoretical foundations and practical applications in data science and intelligent systems.

The programme is offered by Riara University through its School of Computing Sciences, with USIU-Africa as a college. Riara University is a private university in Nairobi with established computing sciences programmes. USIU-Africa offers related data science and analytics programmes through its School of Science and Technology.

Students develop competencies in data analysis, machine learning, AI development, neural networks, deep learning, NLP, computer vision, optimisation, and research. The programme includes coursework, laboratory practicals, examinations, supervised research, and thesis over six semesters.

The programme is delivered over two years (six semesters) with full-time and part-time study modes. Riara University fees are KES 95,000 per semester, totalling KES 570,000 over six semesters. Applicants need a recognised Bachelor's degree in computer science, IT, or related field.

Graduates pursue careers as data scientists, AI engineers, machine learning engineers, computational intelligence specialists, researchers, and lecturers in technology companies, AI startups, financial institutions, research institutions, and universities.

Skills Required

Data Science and Analytics
Machine Learning and Model Development
Artificial Intelligence Engineering
Neural Networks and Deep Learning
Natural Language Processing
Computer Vision and Image Processing
Computational Intelligence and Optimisation
Data Mining and Knowledge Discovery
Intelligent Systems Design
Data Science Research Methods

Key Subjects

Data Science
Computational Intelligence
Machine Learning
Artificial Intelligence
Neural Networks
Deep Learning
Natural Language Processing
Computer Vision
Optimisation Algorithms
Data Science Research Methods

Certifications

IEEE CIS Professional Membership
AAAI Professional Membership

Specializations

Machine Learning

Focuses on machine learning, supervised and unsupervised learning, reinforcement learning, model development, and building intelligent systems that learn from data.

Artificial Intelligence

Examines artificial intelligence, AI systems, knowledge representation, reasoning, expert systems, and designing and building intelligent systems.

Deep Learning

Covers deep learning, neural networks, CNNs, RNNs, transformers, and building deep learning models for complex data analysis.

Natural Language Processing

Focuses on natural language processing, text analytics, language models, chatbots, and processing and analysing human language data.

Computer Vision

Examines computer vision, image processing, object detection, image classification, and building systems that understand visual data.

Computational Intelligence

Covers computational intelligence, evolutionary computation, fuzzy systems, swarm intelligence, optimisation algorithms, and intelligent problem-solving.

Duration: 2 years
Public, up to: Ksh 256,200
Private, up to: Ksh 285,000
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: 20 students
Award: Masters

What you study

10 subjects

Data Science
Computational Intelligence
Machine Learning
Artificial Intelligence
Neural Networks
Deep Learning
Natural Language Processing
Computer Vision
Optimisation Algorithms
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.
Machine Learning
Year 1Semester 13 creditsCore
Covers machine learning, supervised learning, unsupervised learning, reinforcement learning, model evaluation, and building ML models from data.
Artificial Intelligence
Year 1Semester 13 creditsCore
Examines artificial intelligence, AI fundamentals, knowledge representation, reasoning, search algorithms, and designing and building intelligent systems.
Neural Networks and Deep Learning
Year 1Semester 23 creditsCore
Covers neural networks, deep learning, CNNs, RNNs, LSTMs, transformers, and building deep learning models for complex data analysis.
Natural Language Processing
Year 1Semester 23 creditsCore
Examines natural language processing, text processing, language models, word embeddings, sentiment analysis, and processing and analysing human language data.
Computer Vision
Year 1Semester 23 creditsCore
Covers computer vision, image processing, object detection, image classification, facial recognition, and building systems that understand visual data.
Computational Intelligence
Year 2Semester 13 creditsCore
Examines computational intelligence, evolutionary computation, genetic algorithms, fuzzy systems, swarm intelligence, and intelligent optimisation algorithms.
Optimisation Algorithms
Year 2Semester 13 creditsCore
Covers optimisation algorithms, gradient descent, convex optimisation, hyperparameter tuning, and optimising models and algorithms for performance.
Big Data Analytics
Year 2Semester 13 creditsCore
Examines big data analytics, distributed computing, Hadoop, Spark, NoSQL databases, and processing and analysing large-scale datasets.
Data Science Research Methods
Year 2Semester 13 creditsCore
Covers research methods for data science, experimental design, evaluation methods, and conducting data science research, preparing students for their thesis.
AI Ethics and Governance
Year 2Semester 23 creditsCore
Examines AI ethics, responsible AI, bias in AI, AI governance, privacy, and ethical and governance considerations in AI and data science.
Research Thesis
Year 2Semester 26 creditsCore
Original research thesis on a data science and computational intelligence topic, demonstrating mastery of research methods and AI knowledge, assessed through written submission and oral defence.

Specialisations

Machine Learning
Focuses on machine learning, supervised and unsupervised learning, reinforcement learning, model development, and building intelligent systems that learn from data.
Artificial Intelligence
Examines artificial intelligence, AI systems, knowledge representation, reasoning, expert systems, and designing and building intelligent systems.
Deep Learning
Covers deep learning, neural networks, CNNs, RNNs, transformers, and building deep learning models for complex data analysis.
Natural Language Processing
Focuses on natural language processing, text analytics, language models, chatbots, and processing and analysing human language data.
Computer Vision
Examines computer vision, image processing, object detection, image classification, and building systems that understand visual data.
Computational Intelligence
Covers computational intelligence, evolutionary computation, fuzzy systems, swarm intelligence, optimisation algorithms, and intelligent problem-solving.

A day as a student

A typical day during the MSc Data Science and Computational Intelligence programme begins with morning lectures on machine learning, AI, or neural networks. Students engage in theoretical discussions on algorithms, computational intelligence, and intelligent systems. Data science sessions cover the data science lifecycle, data pipelines, data cleaning, feature engineering, and extracting insights from data. Machine learning sessions examine supervised learning, unsupervised learning, reinforcement learning, model evaluation, and building ML models. Artificial intelligence sessions cover AI fundamentals, knowledge representation, reasoning, search algorithms, and designing intelligent systems. Neural networks sessions involve neural network architecture, backpropagation, activation functions, and building and training neural networks. Deep learning sessions cover CNNs, RNNs, LSTMs, transformers, and building deep learning models for complex data. Natural language processing sessions examine text processing, language models, word embeddings, sentiment analysis, and processing human language data. Computer vision sessions involve image processing, object detection, image classification, facial recognition, and building systems that understand visual data. Computational intelligence sessions cover evolutionary computation, genetic algorithms, fuzzy systems, swarm intelligence, and intelligent optimisation. Optimisation sessions examine gradient descent, convex optimisation, hyperparameter tuning, and optimising models and algorithms. Laboratory sessions provide hands-on experience with ML frameworks, deep learning tools, NLP libraries, and computer vision tools. The programme is delivered over six semesters with coursework and research. Guest lectures from AI industry professionals, data scientists, and researchers provide real-world insights. The two-year programme culminates in a research thesis.

The trade offs

In its favour

Kenya's fintech growth, AI adoption, digital transformation, and technology startup ecosystem create very high demand for qualified data science and AI professionals.
Riara University offers the programme at KES 95,000 per semester, which is relatively affordable for a private university AI programme.
Programme uniquely combines data science with computational intelligence, providing both data analytics and AI engineering skills.
USIU-Africa as a college is dual-accredited by CUE and WASC, providing international recognition and standards.

Against it

Programme is offered at limited institutions, primarily Riara University, restricting geographic access.
Programme requires strong quantitative background in mathematics, statistics, or computing, which may exclude some graduates.
Rapidly evolving AI field may require frequent curriculum updates to remain current with industry tools and techniques.
Programme requires access to computing resources and GPU infrastructure for deep learning, which may vary across institutions.

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

Public254k to 256k

254k at JKUAT256k at Technical University of Kenya

Private285k to 285k

285k at Riara University285k at Riara 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

JKUAT 254,100/yr (jkuat.ac.ke). TUK 256,200/yr (eafinder.com). Riara 285,000/yr (businessradar.co.ke).

HELB postgraduate loans are available for Kenyan students. Riara University offers financial aid for qualifying students. IEEE CIS provides grants for computational intelligence research. The programme's AI and data science focus attracts industry and technology funding.

Funding options

HELB Postgraduate Loan
Riara University Financial Aid
IEEE CIS Grants

Scholarships

3 recorded

HELB Postgraduate Loan
LoanKsh 200,000Kenyan
Kenyan students pursuing postgraduate data science and AI studies at recognised universities.
Riara University Financial Aid
ScholarshipKsh 200,000Kenyan
Riara University offers financial aid for qualifying students based on academic merit and financial need.
IEEE CIS Grants
GrantKsh 300,000
IEEE Computational Intelligence Society provides grants for research, conference attendance, and professional development in computational intelligence and AI.

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 computer science, IT, mathematics, statistics, engineering, physics, and related quantitative graduates. Two-year programme (six semesters) at Riara University. USIU requires GPA 2.5 or better, personal narrative, and two letters of recommendation. Application fee KES 3,000 at USIU. Foreign qualifications require KNQA equation certificate.

How you are assessed

4 components

Coursework and Assignments
Coursework30% of the mark
Continuous assessment through coursework assignments, ML projects, programming exercises, model development, and class participation.
Written Examinations
Examination30% of the mark
Written examinations covering machine learning, AI, neural networks, and computational intelligence, conducted at end of each semester.
Practical and Laboratory Assessment
Practical40% of the mark
Assessment of practical and laboratory skills including model development, deep learning implementation, NLP projects, computer vision, and project reports.
Research Thesis
Research100% of the mark
Original research thesis on a data science and computational intelligence topic, demonstrating mastery of research methods and AI knowledge, assessed through written submission and oral defence.

Accreditation

The programme is accredited by the Commission for University Education (CUE). Riara University offers MSc in Data Science and Computational Intelligence through its School of Computing Sciences. USIU-Africa is dual-accredited by CUE and WASC (United States). The programme meets CUE standards for postgraduate data science and computational intelligence training.

Accredited by

Commission for University Education (CUE)
Academic accreditationRequired
Programme accredited by CUE. Riara University offers MSc in Data Science and Computational Intelligence through its School of Computing Sciences. USIU-Africa is dual-accredited by CUE and WASC. The programme meets CUE standards for postgraduate data science training.
Kenya ICT Authority
Professional accreditation
Kenya ICT Authority promotes ICT, AI, and data science standards in Kenya. The programme aligns with national digital transformation and AI capacity building goals.

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.
AI Engineer
Very high demandKsh 180,000 to Ksh 750,000
Designs and builds AI systems, developing intelligent applications, implementing machine learning models, and creating AI solutions for organisations.
Machine Learning Engineer
Very high demandKsh 170,000 to Ksh 700,000
Develops and deploys ML models, building ML pipelines, optimising model performance, and implementing machine learning solutions in production.
Computational Intelligence Specialist
Moderate demandKsh 160,000 to Ksh 650,000
Develops computational intelligence solutions, applying evolutionary algorithms, fuzzy systems, and optimisation techniques to complex problems.
NLP Engineer
High demandKsh 170,000 to Ksh 700,000
Develops natural language processing systems, building chatbots, language models, text analytics, and processing human language data.
University Lecturer
High demandKsh 130,000 to Ksh 500,000
Teaches data science and AI in universities, conducting research and training future data science and AI professionals.

Graduate outcomes

Graduates pursue careers as data scientists, AI engineers, and machine learning engineers in technology companies, AI startups, and research institutions.

Where these fields lead

8 careers

Tools you will learn

Python (TensorFlow/PyTorch)
SoftwarePrimary
Python with TensorFlow and PyTorch for deep learning, neural networks, model building, and AI development with modern ML frameworks.
Scikit-learn
SoftwarePrimary
Python machine learning library for classification, regression, clustering, model selection, and implementing machine learning algorithms.
Jupyter Notebook
Software
Interactive computing environment for data exploration, model development, visualisation, and collaborative data science work.
Google Colab
Software
Cloud-based Jupyter environment with GPU access for deep learning, model training, and AI development without local GPU requirements.

Industry links

Common misconceptions

Data science and computational intelligence is just about programming.
The programme involves mathematical foundations, statistical theory, algorithm design, AI principles, and research, far beyond programming.
Computational intelligence is the same as computer science.
Computational intelligence focuses on AI, neural networks, and intelligent systems, while computer science covers broader computing theory, software, and systems.
There is limited demand for AI and data science professionals in Kenya.
Kenya's fintech growth, AI adoption, digital transformation, and technology startup ecosystem create very high demand for qualified data science and AI professionals.
This programme is only for computer science graduates.
The programme accepts graduates from computer science, IT, mathematics, statistics, engineering, physics, and related quantitative fields.
AI and data science are overhyped and will fade.
AI and data science are transformative technologies with growing applications across industries, and demand for qualified professionals continues to increase globally.
A master's in data science is redundant after a bachelor's in computer science.
The master's provides specialised AI and data science knowledge, research capability, and career progression to senior data science and AI engineering positions.