The B.Sc. Data Science programme prepares you to understand, analyse, and interpret data to solve real-world problems. In a world driven by digital information, organisations across all sectors rely on data scientists to extract insights, guide decision-making, and create predictive models that improve efficiency and innovation. This programme trains you to work confidently with data—how it is collected, stored, processed, visualised, and used to build intelligent systems.
At Olabisi Onabanjo University, you will develop strong analytical thinking, statistical reasoning, programming competence, and problem-solving skills. You will learn how to transform raw data into valuable information, create predictive models using machine learning, and communicate findings clearly to support strategic decisions.
Course Overview
The B.Sc. Data Science programme provides a solid foundation in mathematics, statistics, computing, and analytical techniques. You will learn how to work with structured and unstructured data, develop models, evaluate algorithms, and use modern tools to interpret complex datasets. The programme blends theoretical knowledge with hands-on laboratory work, project-based tasks, and practical exposure through SIWES. Key study areas include:
- Data analytics and statistical modelling
- Database systems and data management
- Python and R programming for data science
- Machine learning and predictive analytics
- Data visualisation and storytelling
- Big data platforms and cloud technologies
- Artificial intelligence fundamentals
- Data ethics, governance, and cybersecurity basics
The programme duration is four years for UTME students and three years for Direct Entry students.
Course Curriculum
The curriculum is drawn from the NUC Core Curriculum and Minimum Academic Standards (CCMAS) for Computing. It integrates mathematical foundations, computing principles, statistical methods, artificial intelligence, and project-based learning to ensure you become a competent data practitioner. Your learning experience combines lectures, laboratory sessions, practical exercises, SIWES training, and a final-year project.
Your first year builds a strong grounding in mathematics, programming, and foundational computing.
- Introduction to Computer Science
- Fundamentals of Programming
- Logic and Critical Thinking
- Elementary Mathematics for Computing
- Introduction to Probability and Statistics
- Communication in English
- Nigerian Peoples and Culture
- Introduction to Web Technologies
- General Physics and Laboratory Sessions
You advance into deeper statistical concepts and essential tools for data analysis.
- Object-Oriented Programming
- Data Structures and Algorithms
- Linear Algebra for Data Science
- Introduction to Data Science
- Database Systems
- Discrete Mathematical Structures
- Operating Systems
- Statistical Inference
- Entrepreneurship Studies
This stage focuses on machine learning foundations, applied analytics, and systems for managing large datasets.
- Machine Learning I
- Data Mining Techniques
- Big Data Systems
- Predictive Analytics
- Statistical Modelling
- Introduction to Artificial Intelligence
- Data Visualisation and Communication
- Systems Analysis and Design
Student Industrial Work Experience Scheme (SIWES)
Your final year strengthens your professional and analytical capacity while allowing you to specialise.
- Machine Learning II
- Deep Learning Foundations
- Cloud Computing for Data Science
- Advanced Data Analytics
- Natural Language Processing
- Ethics and Governance in Data Science
- Final-Year Project / Dissertation
Electives such as Time Series Analysis, Recommender Systems, or Computational Statistics
Entry Requirements
1. UTME Admission (Four-Year Programme)
- Five credit passes in relevant O-level subjects, including English Language and Mathematics, in not more than two sittings.
- A satisfactory performance in the UTME with English Language, Mathematics, and other appropriate science subjects.
- A successful performance in the OOU Post-UTME screening exercise.
- Your O-level subjects must demonstrate strong competence in mathematics and the sciences, as these are fundamental to data analysis and computational thinking.
2. Direct Entry Admission (Three-Year Programme)
- At least two A-Level passes in subjects such as Mathematics, Physics, Statistics, or other relevant sciences.
- An ND, HND, NCE, or equivalent qualification in Computer Science, Statistics, Mathematics, or a related field, with at least upper credit or its equivalent.
- Any other qualification recognised by the University Senate.
- All candidates must also satisfy the general entry regulations of Olabisi Onabanjo University.
Why study at OOU?
Studying Data Science at Olabisi Onabanjo University gives you a competitive advantage in one of the most sought-after fields of the global digital economy. Our programme is designed not only to teach you technical skills but also to help you develop a deep analytical mindset capable of solving real-world problems. You will benefit from:
- A curriculum aligned with national and global best practices in data science education.
- Lecturers with expertise in machine learning, analytics, statistics, AI, and computational modelling.
- Hands-on training using data science tools and programming languages such as Python and R.
- Access to well-equipped computer laboratories for practical learning.
- SIWES placement opportunities that give you industry experience with data-driven organisations.
- A teaching approach that encourages innovation, problem-solving, critical thinking, and ethical practice.
- Preparation for careers in business intelligence, tech, finance, health, academia, public sector decision-making, and advanced computing fields.
This programme positions you to thrive in a world where data shapes decisions and drives development.
Research Project
In your final year, you will complete a supervised research project that allows you to demonstrate your ability to analyse data, build models, and communicate insights effectively. This project integrates statistical reasoning, programming, machine learning, and analytical techniques. You will be expected to:
- Identify a relevant problem or dataset suitable for investigation.
- Conduct a literature review and explore existing solutions or analytical methods.
- Clean, transform, and analyse data using appropriate tools and frameworks.
- Design models or algorithms that address the research problem where applicable.
- Present your findings clearly in a written dissertation and defend your work before an academic panel.
Your research project serves as strong evidence of your readiness to work as a data professional or to pursue postgraduate studies in data science, statistics, or artificial intelligence.
Graduation Requirements
To qualify for the award of the Bachelor of Science in Data Science, you must fulfil the graduation requirements approved by the National Universities Commission (NUC) and Olabisi Onabanjo University.
Academic Requirements
- Complete and pass all compulsory and elective courses in the programme.
- Earn the minimum credit units required:
- 120 credit units for UTME entrants
- 90 credit units for Direct Entry students
- Maintain a minimum Cumulative Grade Point Average (CGPA) of 1.00.
- Complete and successfully defend your final-year research project.
These requirements confirm your ability to apply statistical, computational, and analytical methods to real-world data problems.
Grading and Degree Classification
Your final classification is determined by your Cumulative Grade Point Average (CGPA), as follows:
Upon satisfying these requirements, you will be awarded the B.Sc. (Hons) Data Science, a qualification valued across data-driven industries and essential to organisations relying on digital transformation and evidence-based decision-making.
Career Opportunities
A degree in Data Science opens the door to a wide range of career paths across industries that depend on data-driven insights. Your training in analytics, modelling, coding, and machine learning prepares you to work in environments where large and complex datasets must be understood and utilised effectively.
You may pursue roles such as:
- Data Analyst
- Data Scientist (entry-level)
- Business Intelligence Analyst
- Machine Learning Assistant
- Data Engineer (junior level)
- Statistical Analyst
- Quantitative Research Assistant
- AI Support Technician
- Big Data Assistant
- Product Data Associate
- Data Visualisation Specialist
- Operations and Performance Analyst
- Risk and Compliance Data Assistant
You may work in:
- Technology companies and startups
- Banks, insurance firms, and financial institutions
- Healthcare organisations and health analytics units
- Telecommunications companies
- Government ministries and planning agencies
- Research institutes and think-tanks
- International development organisations
- Manufacturing and FMCG companies
- Consulting firms specialising in analytics
- Media, marketing, and advertising agencies
The programme also provides an excellent foundation for postgraduate studies in Data Science, Statistics, Machine Learning, Artificial Intelligence, or related fields.
How to Apply?
UTME Admission
- Select Olabisi Onabanjo University as your first choice and choose Data Science as your preferred course.
- Sit for the UTME with English Language, Mathematics, and other relevant science subjects.
- Possess at least five O-level credits, including English Language and Mathematics, in not more than two sittings.
- Participate in the OOU Post-UTME screening and meet departmental requirements.
Direct Entry Admission
- Hold acceptable A-Level passes or an ND/HND/NCE in Computer Science, Statistics, Mathematics, or related fields, with at least upper credit or its equivalent.
- Provide the required O-level subject credits.
- Upload academic transcripts and all supporting documents through the OOU Admissions Portal.
Course Coordinator
The B.Sc. Data Science programme is overseen by a team of experienced academics and data professionals within the Department of Computer Science, Faculty of Science.
The current programme coordinator is:
Dr [Full Name]
Contact Information:
- Email:
- Phone:
- Office:
Course Overview
- Next Admission Process
- Current Academic Year
- Associated programmes = Undergraduate
- Course Duration = 4 Years
- Degree = Bachelor of Science (B.Sc.)
- Faculty = Science
- Course Type = Full Time
- Course Delivery = On campus
- Location = Faculty of Science, Main Campus
- Scholarship opportunities