Skip to content
Pathrel
Start free
All courses
Technology

Diploma in Data Science and Artificial Intelligence

The Diploma in Data Science and Artificial Intelligence is a programme that equips students with knowledge and practical skills in data analytics, machine learning, artificial intelligence and data-driven decision making. Offered at Africa International University, the programme prepares students for careers in the rapidly growing fields of data science and AI.

Duration
2 years
Next intake January 2027
Private tuition
Ksh 102,750
Ksh 205,500 total
Job market
High
Market demand for graduates

The short answer

What it costs
Up to Ksh 102,750 a year at a private one.The detail
Who can get in
A KCSE mean grade of C- or above. There is another way in without it.The detail
How long, and when
Takes 2 years, full-time, online, blended, with 3 months of attachment. Next intake January 2027, at 3 institutions.The detail
Where it leads
8 careers in the catalogue. The least exposed to AI is Cloud Security Engineer, at 14 out of 100.The detail

The programme

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

Overview

The curriculum covers programming fundamentals, statistics for data science, data visualisation, database systems, data mining, machine learning algorithms, deep learning, natural language processing, computer vision, big data technologies, AI ethics, cloud computing, data engineering, business analytics, communication skills and entrepreneurship. Students develop both analytical and programming skills.

Kenya's digital economy is experiencing rapid growth in data-driven services, creating demand for professionals who can analyse data, build AI models and support data-driven decision making. The programme responds to the need for skilled practitioners who can work with data, machine learning and AI technologies across sectors.

The programme is delivered through lectures, computer laboratory sessions, project-based learning, case studies and industrial attachment. Students gain hands-on experience in data analysis, model building and AI application development through laboratory sessions and attachment at IT departments, data teams or technology firms.

Graduates pursue careers as data analysts, data science assistants, machine learning assistants, business intelligence assistants, data engineers and AI application developers in banks, telecom firms, technology companies, government agencies and corporate analytics departments. They can also progress to a Bachelor of Science in Data Science or related degree programmes.

Prospective students should have analytical skills, interest in mathematics and programming, and problem-solving aptitude. The programme is suitable for students seeking practical data science and AI skills for employment or further study.

What you study

8 subjects
  • Programming and Statistics for Data Science
  • Data Visualisation and Database Systems
  • Data Mining and Machine Learning Algorithms
  • Deep Learning and Natural Language Processing
  • Big Data Technologies and Data Engineering
  • Computer Vision and AI Applications
  • AI Ethics and Cloud Computing
  • Business Analytics and Entrepreneurship

Modules

8 in the programme

  • Programming and Statistics for Data Science

    Year 1Semester 13 creditsCore

    Covers Python programming, data structures, statistics fundamentals, probability, hypothesis testing and data manipulation with pandas and numpy.

  • Data Visualisation and Database Systems

    Year 1Semester 13 creditsCore

    Covers data visualisation principles, charts and dashboards, SQL, relational databases, NoSQL databases and data warehousing fundamentals.

  • Data Mining and Machine Learning

    Year 1Semester 23 creditsCore

    Covers data mining techniques, supervised and unsupervised learning, regression, classification, clustering, model evaluation and scikit-learn.

  • Deep Learning and NLP

    Year 1Semester 23 creditsCore

    Covers neural networks, deep learning fundamentals, TensorFlow and PyTorch, natural language processing, text analysis and language models.

  • Big Data and Data Engineering

    Year 2Semester 13 creditsCore

    Covers big data technologies, Hadoop, Spark, data pipelines, ETL processes, data engineering and cloud computing platforms.

  • AI Applications and Ethics

    Year 2Semester 13 creditsCore

    Covers computer vision, AI application development, AI ethics, bias and fairness, responsible AI and business analytics.

  • Capstone Project and Entrepreneurship

    Year 2Semester 23 creditsCore

    A capstone project integrating data science and AI concepts to solve a real-world problem, plus entrepreneurship and communication skills.

  • Industrial Attachment

    Year 2Semester 26 creditsCore

    A three-month industrial attachment at an IT department, data team or technology firm, assessed through an attachment report.

Specialisations

  • Data Analytics

    Focuses on data analysis, visualisation and business intelligence for careers as data analysts.

  • Machine Learning

    Specialises in ML model development, training and deployment for careers as machine learning assistants.

  • Data Engineering

    Focuses on data pipelines, ETL and big data technologies for careers as data engineers.

  • AI Applications

    Specialises in AI application development including NLP and computer vision for careers as AI developers.

A day as a student

A typical day begins with a morning lecture on statistics or machine learning algorithms, followed by a laboratory session on data analysis using Python. Afternoon sessions may include a deep learning practical, a data visualisation exercise, or a group project on building AI models. Students also work on data science projects and participate in coding challenges.

The trade-offs

In its favourAgainst it
Data science and AI are among the fastest-growing fields globally with high demand across all sectors.The field evolves rapidly, requiring continuous learning and upskilling beyond the diploma.
The programme combines theoretical knowledge with practical laboratory sessions and industry attachment.The programme is offered at only one university, limiting study location options.
Graduates can progress to a Bachelor of Science in Data Science or related degree programmes.—

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
Private76k to 103k
76k at Techsavanna Software Institute (TSI)103k at Zetech 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

TSI 76K, Zetech 102.75K/yr private (tsi.ac.ke, scribd). Public: unverified.

Students can access HELB loans. AIU offers scholarships and work-study opportunities for qualifying students.

Funding options

  • HELB Loan

Scholarships

1 recorded

  • HELB Loan

    LoanKsh 40,000Kenyan

    Kenyan students enrolled in accredited programmes at recognised institutions

Scholarships you could apply for

3 usable where it is taught
Save one and be reminded before it closes

Getting in

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

What you need

KCSE mean grade
C-minimum entry
Alternative entry

Certificate in IT or related field from a recognised institution, or Recognition of Prior Learning (RPL) certification through KNQA.

When it next starts

Taught at 3 institutions
January 20273 institutions

Including Africa International University, Zetech University, Tangaza University.

May 20271 institution

Including Zetech University.

September 20273 institutions

Including Africa International University, Zetech University, Tangaza University.

How you are assessed

4 components

  • Continuous Assessment Tests

    Written examination30% of the mark

    Mid-semester and end-of-semester written tests covering programming, statistics and machine learning.

  • Laboratory Practicals

    Practicum40% of the mark

    Assessment of laboratory sessions including data analysis, model building and AI application exercises.

  • Capstone Project

    Project50% of the mark

    A capstone project integrating data science and AI concepts, assessed through a presentation and report.

  • Industrial Attachment Report

    Practicum100% of the mark

    Assessment of performance during industrial attachment at an IT department, data team or technology firm.

Accreditation

  • Commission for University Education

    AcademicRequired

    Accredited by the Commission for University Education (CUE). The programme is offered at Africa International University.

Where it leads

The roles it opens, and what you leave with.

Where graduates go

4 roles

  • Data Analyst

    High demandKsh 45,000 to Ksh 180,000

    Analyses data, creates visualisations, builds dashboards, generates insights and supports data-driven decision making in organisations.

  • Data Science Assistant

    Moderate demandKsh 42,000 to Ksh 160,000

    Supports data science projects, prepares data, builds models, conducts experiments and assists senior data scientists.

  • Machine Learning Assistant

    Moderate demandKsh 44,000 to Ksh 170,000

    Supports ML model development, data preprocessing, model training, testing and deployment under senior ML engineers.

  • Business Intelligence Assistant

    Moderate demandKsh 40,000 to Ksh 150,000

    Supports BI operations, creates reports, maintains dashboards, analyses business data and assists with BI tool administration.

Graduate outcomes

Graduates work as data analysts, data science assistants and machine learning assistants in banks, telecom firms, technology companies and government agencies.

Where these fields lead

8 careers

Certifications

Common misconceptions

  • Data science is only for mathematics experts.

    The programme teaches statistics and programming from foundational levels, making it accessible to students with basic mathematics skills.

  • AI will replace jobs, making this field pointless.

    AI creates new roles requiring professionals who can build, deploy and maintain AI systems, increasing demand for skilled practitioners.

  • You need expensive equipment to study data science.

    The programme uses standard computers and cloud-based tools, with university laboratories providing necessary computing resources.

  • Data science is the same as computer science.

    Data science focuses on data analysis, statistics and AI, while computer science covers broader computing including software engineering and systems.

Further reading

Read next