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SCHOOL OF COMPUTING
BSc. Data Science
This programme is designed to prepare you to be well-equipped to pursue a successful career in data science, leveraging your skills and knowledge to extract insights from data, drive informed decision-making, and contribute to advancements in various industries and sectors.
Tuition Per Session
$560
Tuition Per Semester
$315
Introduction to BSc. Data Science
Start Your Bachelor’s Degree in Data Science
Learn on your terms with pre-recorded engaging and interactive videos on your educational journey for flexible, convenient, and self-paced study.
Why you should apply :
- Our programme is taught by experienced and knowledgeable faculty members who are passionate about teaching data science.
- We offer a variety of resources to help you succeed, including a state-of-the-art computer lab, a career center, and a variety of student organisations.
- Our programmes are designed to produce highly sought-after graduates.
- A degree in data science can lead to a variety of high-paying and rewarding careers.
Study Level
BSc. Data Science
Study Duration
8 Semesters
Mode of study
Blended Learning
Tuition Per Session
$560
Tuition Per Semester
$315
Applications for May 2026 admission is ongoing.
Apply before 31st May 2026, to secure your place.
Curriculum
Programme Outline
Our curriculum is designed to provide students with the skills and knowledge they need to succeed in a variety of careers in the tech industry. The programme covers a wide range of topics, including programming, data structures, algorithms, operating systems, and artificial intelligence.
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- Digital Resources
- Career Services
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1st Semester
Units
Communication in English I
2
- Identify possible sound patterns in English.
- List notable language skills and classify word formation processes.
- Construct simple and fairly complex sentences in English.
- Apply logical and critical reasoning skills for meaningful presentations.
- Demonstrate an appreciable level of the art of public speaking and listening.
- Write simple and technical reports.
Elementary Mathematics I – Algebra and Trigonometry
2
- Understand the basic definitions of set, subset, union, intersection, complements, and use of Venn diagrams.
- Solve quadratic equations.
- Solve trigonometric functions.
- Understand various types of numbers.
- Solve some problems using the binomial theorem.
General Physics I – Mechanics
2
- Identify and deduce the physical quantities and their units.
- Differentiate between vectors and scalars.
- Describe and evaluate the motion of systems on the basis of the fundamental laws of mechanics.
- Apply Newton’s laws to describe and solve simple problems of motion.
- Evaluate work, energy, velocity, momentum, acceleration, and torque of moving or rotating objects.
- Explain and apply the principles of conservation of energy, linear and angular momentum.
- Describe the laws governing motion under gravity and quantitatively determine the behavior of objects moving under gravity.
General Practical Physics I
1
- Conduct measurements of some physical quantities.
- Make observations of events, collect and tabulate data.
- Identify and evaluate some common experimental errors.
- Plot and analyze graphs.
- Draw conclusions from numerical and graphical analysis of data.
Use of Library, Study Skills and ICT
2
- Understand the significance of Information and Communication Technology (ICT) and its application to libraries and information services.
- Acquire essential ICT skills for information professionals, understand data communication and internet resources in electronic storage systems, and explore web technology resources.
- Learn the impact of ICT on modern libraries, along with ethical considerations and challenges related to applying ICT in library settings, particularly in the context of Nigerian libraries.
Descriptive Statistics
3
- Explain the basic concepts of descriptive statistics.
- Present data in graphs and charts.
- Differentiate between measures of location, dispersion, and partition.
- Describe skewness and kurtosis and their use in a given data set.
- Differentiate rates from ratios and explain how they are used.
- Compute different types of index numbers from a given data set and interpret the output.
- Use frequency distributions to organise and summarise data.
- Create and interpret charts and graphs to visualise data effectively.
- Compute and interpret measures of central tendency.
- Calculate and interpret measures of dispersion.
- Compare probability approaches and calculate conditional probabilities.
- Work with discrete probability distributions, including Bernoulli, Binomial, Uniform, Poisson, Geometric, and Hypergeometric distributions.
- Analyse continuous probability distributions such as Uniform, Normal, and Exponential distributions.
Introduction to Computing Sciences
3
- Explain basic components of computers and other computing devices.
- Describe the various applications of computers.
- Explain information processing and its roles in society.
- Describe the Internet, its various applications, and its impact.
- Explain the different areas of the computing discipline and its specializations.
- Demonstrate practical skills on using computers and the internet.
Environment and Sustainability (Elective)
2
- Grasp fundamental principles of environmental studies, human-environment relationships, and the impact of human activities on nature.
- Examine energy resource usage and its environmental consequences, and investigate chemical and waste effects on ecosystems and health.
Contemporary Health Issues (Elective)
2
- Outline contemporary health issues and broadly classify them.
- Discuss concepts related to clinical medicine, disease prevention and management, and population health.
- Explain the aetiology, prevention, and management of key non-communicable diseases.
- Discuss the epidemiology and consequences of selected infectious diseases.
- Discuss personal and social determinants of health.
- Explain the place of disease prevention and health promotion in personal and population health.
- Explain the connection between contemporary health issues and the Sustainable Development Goals.
- Relate contemporary health issues to global health challenges.
2nd Semester
Units
Problem Solving
3
- Explain problem-solving processes.
- Demonstrate problem-solving skills.
- Describe algorithm development and properties of algorithms.
- Discuss solution techniques for solving problems.
- Solve computer problems using algorithms, flowcharts, and pseudocode.
- Solve problems using programming languages such as C and Python.
Nigerian People and Culture
2
- Analyse the historical foundation of Nigerian culture and arts in pre-colonial times.
- List and identify the major linguistic groups in Nigeria.
- Explain the gradual evolution of Nigeria as a political unit.
- Analyse trade, economic, and self-reliance concepts towards national development.
- Enumerate challenges of the Nigerian state towards nation building.
- Analyse the role of the judiciary in upholding people’s fundamental rights.
- Identify acceptable norms and values of major ethnic groups in Nigeria.
- Suggest solutions to identifiable Nigerian environmental, moral, and value problems.
Elementary Mathematics II – Calculus
2
- Differentiate and explain rules in calculus.
- Analyse real-variable functions and graphs.
- Explain limits and continuity.
- Explain derivatives as limits of rates of change.
- Apply integration techniques and definite integrals to solve area and volume problems.
General Physics II – Electricity & Magnetism
2
- Describe and determine the magnetic field for steady and moving charges.
- Determine magnetic properties of simple current distributions using Biot-Savart and Ampere’s law.
- Describe electromagnetic induction and apply Faraday and Lenz’s laws.
- Explain the basics of Maxwell’s equations in integral form.
- Evaluate DC circuits and analyse AC voltages and currents in resistors, capacitors, and inductors.
General Practical Physics II
1
- Conduct experiments on measurements of physical quantities.
- Make observations of events.
- Collect and tabulate data.
- Identify and evaluate common experimental errors.
- Plot and analyse graphs.
- Draw conclusions from numerical and graphical analysis of data.
Introduction to Web Technologies
3
- Plan, design, and develop effective web pages with a practical focus.
- Use HTML5, CSS, and JavaScript.
- Host a website on a selected web server.
- Develop web content development skills.
Communication in English II
2
- Have a deepened understanding of communication skills in spoken and written English.
- Demonstrate proficiency in public speaking, listening, and effective communication.
1st Semester
Units
Entrepreneurship and Innovation
2
- Explain the concepts, characteristics, and theories of entrepreneurship, intrapreneurship, opportunity seeking, new value creation, and risk-taking.
- Analyse the importance of micro and small businesses in wealth creation, employment, and financial independence.
- Engage in entrepreneurial thinking.
- Identify key elements in innovation and describe the stages in enterprise formation, partnership, and networking, including business planning.
- State the basic principles of e-commerce.
Mathematical Methods I
2
- Describe real-valued functions of a real variable.
- Solve problems using the mean value theorem and Taylor series expansion.
- Evaluate line integrals, surface integrals, and volume integrals.
Computer Programming I
3
- Identify different programming paradigms and their approaches to programming.
- Write programs in C using basic data types and strings.
- Design and implement programming problems using selection and loops.
- Use and implement classes as data abstractions in an object-oriented approach.
- Implement simple exception handling in programs.
- Develop programs with input and output from text files.
- Design and implement programming problems involving arrays.
Discrete Structures
2
- Convert logical statements from informal language to propositional and predicate logic expressions.
- Describe the strengths and limitations of propositional and predicate logic.
- Outline the basic structure of each proof technique.
- Apply proof techniques in the construction of sound arguments.
- Apply the pigeonhole principle in formal proofs.
- Compute permutations and combinations of a set and interpret their meanings.
- Map real-world applications to appropriate counting formalisms.
- Solve basic recurrence relations.
Introduction to Data Science
2
- Demonstrate the principles of working with data across distributions, sizes, and ranges.
- Explain from first principles the operations that power data-driven utilities in the modern computing industry.
- Demonstrate foundational technological processes enabling various data functions.
Introduction to R Programming
2
- Utilize the R programming language for data-driven functions and utilities across computing domains.
- Explain the structures, functions, and operations of the R language.
- Apply the R programming language to practical data-driven use-cases.
Set, Logic, and Algebra
2
- Solve various problems using concepts of set theory.
- Understand algebraic structures.
- Understand the meaning of logic in mathematics.
Introduction to Numerical Analysis
2
- Solve algebraic and transcendental equations numerically.
- Perform curve fitting and error analysis.
- Use interpolation and approximation techniques.
- Find zeros of non-linear equations.
- Solve systems of linear equations numerically.
- Apply numerical differentiation and integration.
- Solve initial value problems in ordinary differential equations numerically.
SIWES I
3
- Work in a private and public organisation for three months.
- Acquire practical experience and develop skills in all areas of data science.
- Produce a comprehensive report summarizing the knowledge gained and experiences encountered.
2nd Semester
Units
Philosophy, Logic and Human Existence
2
- Provide a survey of the main branches of philosophy, symbolic logic, and special symbols in symbolic logic.
- Understand the method of deduction using rules of inference and bi-conditionals.
- Discuss types of discourse, nature of arguments, validity, and soundness.
- Evaluate techniques for evaluating arguments and distinguish between inductive and deductive inferences.
Computer Programming II
3
- Develop solutions for a range of problems using object-oriented programming in C++.
- Use modules, packages, and namespaces for program organization.
- Utilize APIs in writing applications.
- Apply divide and conquer strategy to searching and sorting problems using iterative and/or recursive solutions.
- Explain exceptions and handle exceptions in programs.
- Write simple multithreaded applications.
- Design and implement simple GUI applications.
Computer Architecture and Organisation
2
- Explain different instruction formats and variable length vs. fixed length formats.
- Describe the organisation of the classical von Neumann machine and its major functional units.
- Handle subroutine calls at the assembly level.
- Explain basic concepts of interrupts and I/O operations.
- Write simple assembly language program segments.
- Implement fundamental high-level programming constructs at the machine-language level.
- Compare alternative implementations of data paths.
- Discuss control points and generation of control signals using hardwired or micro-programmed implementations.
Statistical Computing Inference and Modelling
3
- Draw conclusions based on statistical assumptions, models, and results.
- Make inferences on statistical outcomes and their real-world implications in decision-making processes.
- Demonstrate the application of statistical tools and packages for data analysis.
- Communicate statistical solutions effectively.
Linear Algebra for Data Science
2
- Master linear algebra concepts including solving linear equations, eigenvectors, eigenvalues, and more.
- Confidently use similar matrices, linear transformations, and orthogonal projections.
- Apply orthonormal bases and the Gram-Schmidt process effectively in mathematical and real-world applications.
Data Engineering
3
- Explain data engineering concepts and processes.
- Work with data engineering tools and technologies.
- Develop data pipelines for data preparation and analysis.
- Apply Python skills for data manipulation and web scraping.
- Implement ETL processes and work with data repositories.
- Perform practical data engineering tasks through hands-on lab work.
1st Semester
Units
Data Structures
3
- Demonstrate a comprehensive understanding of fundamental programming concepts and data structures in C++.
- Effectively allocate memory on the stack and heap.
- Implement and apply various data structures including queues and trees.
- Manage run-time storage effectively through pointers and references.
- Write C++ functions and algorithms for arrays, records, string processing, queues, trees, pointers, and linked structures.
Introduction to Cybersecurity and Strategy
2
- Articulate cybersecurity concepts, methods, terminologies, and elements.
- List and explain common cyber-attacks, threats, challenges, and solutions.
- Apply techniques for identifying, detecting, and defending against cybersecurity threats.
- Evaluate cybersecurity and national security strategies.
- Recognize ethical obligations of security professionals.
Data Quality and Data Wrangling
3
- Learn data wrangling concepts and data quality assessment.
- Understand data integrity and Python programming basics.
- Improve data quality through cleaning and augmentation.
- Handle structured and unstructured data in Python.
- Master data manipulation techniques.
- Apply Python libraries for data analysis and prediction.
- Utilize advanced NumPy for numerical operations.
- Acquire web scraping skills for data retrieval.
Introduction to Data Protection and IT Security
3
- Explain data protection and privacy concepts.
- Develop privacy algorithms for secure data querying.
- Manage privacy incidents and operations.
- Explain IT security, threats, cryptology, and encryption practices.
- Secure networks and protect cloud data.
- Comply with data privacy laws and regulations.
- Understand digital security and ethics.
Internet of Things
3
- Explain IoT concepts, applications, device programming, and communication.
- Describe IoT protocol stacks, networking, and infrastructure.
- Explore data science and cloud platforms for IoT.
- Understand legal and ethical considerations in IoT.
- Apply IoT mindsets in product and business design.
- Gain practical experience through lab work.
SIWES II
3
- Apply practical experience and skills in Data Science during a three-month attachment.
- Develop proficiency in various Data Science areas.
- Maintain records and monitor performance during the training period.
- Submit a comprehensive report demonstrating their learning and achievements.
2nd Semester
Units
Peace and Conflict Resolution
2
- Analyse the concepts of peace, conflict, and security.
- List major forms, types, and root causes of conflict and violence.
- Differentiate between conflict and terrorism.
- Enumerate security and peacebuilding strategies.
- Describe roles of international organisations, media, and traditional institutions in peacebuilding.
Venture Creation
2
- Identify business opportunities through environmental scanning and market research.
- Understand entrepreneurial finance options like venture capital, equity finance, and microfinance.
- Grasp principles of marketing, customer acquisition, retention, and e-commerce models.
- Acquire skills in small business management, negotiation, and modern business communication.
- Demonstrate ability to generate business ideas and explore emerging technologies for digital business strategies.
Big Data Computing
2
- Install and use Cloudera VM, Jupyter, Spark, Hadoop, MongoDB, and Postgres.
- Analyse Twitter data using Spark and MongoDB.
- Build machine learning dashboards and web apps with NoSQL databases.
- Design cost-efficient big data solutions considering security aspects.
Data Science Innovation and Entrepreneurship
2
- Generate innovative business ideas.
- Develop and market products.
- Exhibit strong leadership skills.
- Identify Data Science entrepreneurial opportunities.
- Understand legal and ethical aspects of business.
- Create business plans, conduct market research, and deliver technical presentations.
- Analyse successful entrepreneurial ventures.
Ethics and Legal Issues in Data Science
2
- Recognise legal and ethical consequences in Data Science.
- Apply techniques like Digital Data Repositories and Digital Object Identifiers.
- Understand Open Science, Open Data, and FAIR principles.
- Address data ownership, privacy, and bias concerns.
Machine Learning
2
- Apply supervised and unsupervised learning techniques.
- Use decision trees, linear regression, logistic regression, SVM, clustering, and ensemble methods.
- Understand probabilistic methods and model evaluation.
- Get an introduction to neural networks and auto-encoders.
Probability for Data Science
3
- Analyze and interpret real-world statistical events.
- Utilise principles and concepts from probability theory in data analysis.
- Apply statistical principles to analyze data and draw conclusions.
Data Management I
3
- Describe components of a database system and give examples of their use.
- Explain differences between relational and semi-structured data models.
- Demonstrate entity integrity constraint, referential integrity constraint, and query optimisations in relational databases.
- Describe properties of normal forms and their impact on database operations.
- Explain database security, integrity issues, concurrency control, and recovery mechanisms.
1st Semester
Units
Research Methodology and Technical Report Writing
3
- Distinguish qualitative and quantitative research methodologies and their applications.
- Identify and define a research problem in a given area.
- Select appropriate data collection methods for specific situations.
- Design and conduct simple research including analysis and interpretation of results.
- Document the research process from problem definition to report writing.
- Defend the written research report.
- Address ethical issues in the conduct of research.
Algorithms and Complexity Analysis
2
- Explain big-O, omega, and theta notation for algorithm analysis.
- Determine asymptotic upper, lower, and tight bounds on time and space complexity.
- Analyse time and space complexity of simple algorithms.
- Formulate and solve recurrence relations for recursive algorithms.
- Apply strategies such as brute-force, greedy, divide-and-conquer, backtracking, and dynamic programming.
- Use pattern matching to analyse substrings.
- Apply numerical approximation methods to solve mathematical problems.
Project Management
2
- Understand planning, scheduling, and resource utilisation in projects.
- Manage project resources and procurement effectively.
- Monitor and execute projects with strong communication and time management skills.
- Adapt to project complexities and changing circumstances.
- Lead projects to timely and successful completion.
Final Year Project I
3
- Conduct independent or group investigations in Data Science.
- Submit a written proposal outlining the project.
- Address software, hardware, communication, or network-related problems.
- Analyse data using computer resources.
- Produce a formal written report and deliver an oral presentation.
Data Visualisation for Data-driven Decision Making
2
- Understand various methods for data visualisation and their applications.
- Use tables, graphs, images, and animations for effective data presentation.
- Create interactive visualisations to communicate insights.
- Summarise data using tables, graphs, and plots.
- Conduct hands-on lab work to master visualisation techniques.
Neural Nets and Deep Learning
3
- Understand fundamentals of neural networks and deep learning.
- Implement supervised learning using neural networks.
- Apply activation functions and backpropagation techniques.
- Build and optimise deep neural networks.
- Perform practical experiments using Python, Jupyter Notebooks, and NumPy.
- Apply neural network concepts to real-world problems.
2nd Semester
Units
Cloud Computing
2
- Understand cloud computing fundamentals and parallel algorithms.
- Use tools and systems for parallel processing.
- Implement cloud services for analytics and storage.
- Explain distributed systems, databases, and file systems.
- Manage cloud infrastructure and optimise performance.
- Explore legal aspects and service level agreements.
- Utilise data science tools in the cloud environment.
Business Intelligence
3
- Apply statistical concepts and BI techniques.
- Use SQL and Tableau for data analysis and visualisation.
- Create dashboards, metrics, and reports for business insights.
- Perform data preprocessing and historical analysis.
- Apply data to improve business decision-making.
- Conduct practical lab work on data manipulation and visualisation.
Model Engineering
2
- Master the data science process from preprocessing to deployment.
- Apply supervised and unsupervised learning techniques.
- Implement and evaluate machine learning models.
- Use libraries such as scikit-learn, PyTorch, TensorFlow, and Keras.
Final Year Project II
3
- Implement the approved project successfully.
- Evaluate outcomes and results.
- Prepare a formal written report with supervisory approval.
- Present findings and outcomes orally.
- Demonstrate proficiency in data science skills.
Operating Systems
3
- Recognise operating system types and structures.
- Describe process and thread management.
- Understand CPU scheduling, synchronisation, and deadlock.
- Explain virtual memory, disk scheduling, I/O, and file systems.
- Identify security and protection issues.
- Use C and Unix commands and develop system programs under Linux.
Time Series Analysis
2
- Analyse time series data using statistical methods.
- Conduct forecasting and identify patterns.
- Apply AR, MA, ARMA, and ARIMA models.
- Use smoothing techniques and address stationarity.
- Interpret and present results using appropriate plots and forecasts.
Admission Requirements
Admission Requirements for BSc. Data Science
100 Level Entry Requirements for BSc. Data Science
Here’s what you need to study for a bachelor’s programme at Miva Open University
A copy of your O’Level result
The result must include a minimum of five credits in the following subjects in not more than two sittings:
- Mathematics
- English Language
- Physics
- Any other two (2) science subjects
Please note that submission of Joint Admissions and Matriculation Board (JAMB) results is not mandatory at this stage. However, upon admission to the university, the provided results will be thoroughly verified for authenticity and compliance with the stated criteria, including JAMB Regularisation.
Direct Entry Admission Requirements for BSc. Data Science
Here’s what you need to study for a bachelor’s programme at Miva Open University
Direct Entry Candidates must meet ‘O’ Level requirements for the programme:
- Two (2) 'A' Level passes in science subjects including Mathematics.
- NCE merit passes in Mathematics and one other Science subject.
- ND lower credit in Computer Science or other Mathematics/Computing/Physics/Electronics based programmes.
- Very good passes in three (3) JUPEB subjects: Physics, Mathematics, Chemistry or Biology.
- 'A' Level passes chosen from English Language, Mathematics, Environmental Science, Biology, Chemistry, Physics, Further Mathematics, Technical Drawing, Computer Studies and Information Technology
- International Baccalaureate (IB) Diploma in relevant subjects.
Careers
Potential Roles for BSc. Data Science Degree Holders
- Data Analyst
- Data Scientist
- Business Intelligence Analyst
- Data Consultant
- Data Engineer
- Healthcare Data Analyst
- Market Research Analyst
- Quantitative Analyst
- Data Visualization Specialist
- Machine Learning Engineer
Tuition
Payment Plans
Miva Open University offers a flexible payment plan for its degree programmes. You may choose to pay the year’s fee or per semester.
Tuition Per Semester
$315
/Semester
- Pay Per Semester.
- No hidden charges.
- No additional costs.
Tuition Per Session
$560
/Session
- Pay Per Session
- No hidden charges.
- No additional costs.
