B.Sc. · 146 units · Computing
To grow data scientists who turn Nigerian data into decisions — statistically rigorous and engineering-capable.
At a glance
Blend statistics, machine learning and data engineering.
Emphasise reproducible, ethical, well-communicated analysis.
Bridge from notebook to production data products.
Every credit unit in the Data Science programme, grouped by level and semester — Core, GST, Institutional and Elective courses as approved by Senate.
Communication in English
Introduction to Computing Sciences
Problem-Solving and Algorithmic Thinking
Elementary Mathematics I (Algebra & Trigonometry)
Descriptive Statistics
Introduction to Data Science
Running total through 100L S1: 16 / 146 units
Nigerian Peoples and Culture
Computer Programming I (Python for Data Science)
Elementary Mathematics II (Calculus with Applications)
Probability Theory I
Digital Logic and Computer Hardware
Data Science Toolchain (Notebooks, Git & Cloud Sandboxes)
Running total through 100L S2: 31 / 146 units
Entrepreneurship and Innovation
Data Structures and Algorithms
Linear Algebra I
Programming for Data Science II (R & the Tidyverse)
Probability Theory II
Computer Organisation and Architecture
Running total through 200L S1: 47 / 146 units
Philosophy, Logic and Human Existence
Operating Systems
Data Wrangling and Preprocessing
Statistical Inference
Database Systems and SQL for Data Science
Discrete Mathematics
Data Visualization
Cloud Computing and the Digital Data Economy
Running total through 200L S2: 68 / 146 units
Venture Creation
Peace and Conflict Resolution
Deep Learning Fundamentals
Time Series Analysis and Forecasting
Software Engineering Principles
SIWES: Industrial Training (24 weeks)
MLOps: Principles of Reproducible Machine Learning
Data Engineering Foundations (Pipelines & Warehousing)
Running total through 300L S2: 93 / 146 units
Design and Analysis of Algorithms
Regression Analysis and Statistical Modelling
Big Data Technologies
Machine Learning ⭐ flagship exemplar course
Data Ethics, Privacy and Governance
Cloud Data Platforms and Engineering
Running total through 300L S1: 110 / 146 units
Applied Machine Learning in FinTech
Applied Machine Learning in Healthcare
MLOps and Model Deployment at Scale
Natural Language Processing
Advanced Statistical Learning
Data Science Capstone Project I
Industry 4.0, IoT and Edge Data Analytics
Running total through 400L S1: 131 / 146 units
Explainable AI and Responsible Data Science
Seminar in Data Science
Data Science Capstone Project II
Data Science Elective (choose 1 of 3 — see bank below)
Data Product Management and Entrepreneurship
Professional Ethics, ICT Law and Society
Running total through 400L S2: 146 / 146 units
Distance-Learning applicants are UTME-exempt (JAMB 2024 policy): placed on a separate DL matriculation list, screened via O’Level verification + a Senate-approved aptitude screening, and not mobilised for NYSC.
Curious, evidence-driven, loves a clean chart.
Kesiena
Your Data Science tutor
Voice — Exploratory, precise about uncertainty.
I’m an AI tutor — I won’t do graded work for you, and I’ll bring in a human facilitator for judgement calls.
Distance-Learning applicants are UTME-exempt under the 2024 JAMB policy. Apply for the 2027/2028 session and get matched with a human facilitator and Kesiena from day one.