At the end of this course, students should be able to:

• 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.

At the end of this course, students should be able to:

• 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.

At the end of this course, students should be able to:

• 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.

At the end of this course, students should be able to:

• 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.

At the end of this course, students should be able to:

• 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.

At the end of this course, students should be able to:

• Explain the basic concepts of descriptive statistics.
• Present data in graphs and charts.
• Differentiate between measures of location, dispersion and partition.
• Describe the basic concepts of Skewness and Kurtosis as well as their utility function in a given data set.
• Differentiate rates from ratio and how they are used.
• Compute different types of index numbers from a given data set and interpret the output.
• Understand and apply frequency distributions to organize and summarize data, create and interpret various types of charts and graphs to visualize data effectively.
• Compute and interpret measures of central tendency to identify the center of a distribution.
• Calculate and interpret measures of dispersion to understand the spread of data points.
• Compare and contrast different approaches to probability, calculate and interpret conditional probabilities to make informed decisions based on given conditions.
• Identify and work with probability distributions in the discrete case, including Bernoulli, Binomial, Uniform, Poisson, Geometric, and Hypergeometric distributions.
• Analyze continuous probability distributions, such as Uniform, Normal, and Exponential distributions.

At the end of this course, students should be able to:

• 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.

At the end of this course, students should be able to:

• Grasp environmental studies’ fundamental principles, human-environment relationships, and the impact of human activities on nature.
• Examine energy resource usage and its environmental consequences, and investigate chemicals and waste effects on ecosystems and health.

At the end of this course, students should be able to:

• Outline contemporary health issues and broadly classify them.
• Discuss some basic concepts related to clinical medicine, disease prevention/management, and population health.
• Explain the aetiology, prevention, and management of key non-communicable diseases.
• Discuss the epidemiology, personal and public health consequences of selected infectious diseases.
• Discuss the 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 sustainable development goals.
• Relate contemporary health issues to global health challenges.

At the end of this course, students should be able to:

• Explain problem-solving processes.
• Demonstrate problem-solving skills.
• Describe the concept of algorithms development and properties of algorithms.
• Discuss the solution techniques of solving problems.
• Solve computer problems using algorithms, flowcharts, pseudocode, etc.
• Solve problems using programming languages such as C, Python, etc.

At the end of this course, students should be able to:

• Analyze 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.
• Analyze the concepts of Trade, Economic, and Self-reliance status of the Nigerian peoples towards national development.
• Enumerate the challenges of the Nigerian State towards Nation building.
• Analyze the role of the Judiciary in upholding people’s fundamental rights.
• Identify acceptable norms and values of the major ethnic groups in Nigeria.
• List and suggest possible solutions to identifiable Nigerian environmental, moral, and value problems.

At the end of this course, students should be able to:

• Differentiate and explain rules in calculus.
• Analyze real-variable functions and graphs.
• Grasp limits and continuity.
• Understand derivatives as the rate of change limits.
• Gain proficiency in integration techniques and definite integrals for solving area and volume problems.

At the end of this course, students should be able to:

• Describe and determine the magnetic field for steady and moving charges.
• Determine the magnetic properties of simple current distributions using Biot-Savart and Ampere’s law.
• Describe electromagnetic induction and related concepts and make calculations using Faraday and Lenz’s laws.
• Explain the basic physical of Maxwell’s equations in integral form.
• Evaluate DC circuits to determine the electrical parameters and the characteristics of AC voltages and currents in resistors, capacitors, and inductors.

At the end of this course, students should be able to:

• Conduct experiments on the 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.

At the end of this course, students should be able to:

• Plan, design, and develop effective web pages with a focus on the practical application of the technologies used in web development.
• Use tools like HTML5, Cascading Style Sheet (CSS) and Javascript.
• Host a website on a selected web server.
• Develop web content development skills.

At the end of this course, the student will be able to:

• Have a deepened understanding of communication skills both in spoken and written English.
• Demonstrate an appreciable level of proficiency in the arts of public speaking, listening, and effective communication.

• 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.

• Describe the 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.

• 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/output from text files.
• Design and implement programming problems involving arrays.

• 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.

• 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.

• 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.

• Solve various problems using concepts of set theory.
• Understand Algebraic structures.
• Understand the meaning of logic in mathematics.

• 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.

• 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.

• 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.

• Develop solutions for a range of problems using object-oriented programming in C++.
• Use modules/packages/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 the concept of exceptions in programming and handle exceptions in programs.
• Write simple multithreaded applications.
• Design and implement simple GUI applications.

• 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 the concept of control points and the generation of control signals using hardwired or micro-programmed implementations.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• Distinguish qualitative and quantitative research methodologies and their applications.
• Identify and define a research problem in a given area.
• Identify different methods of data collection and select the methods appropriate to a given situation.
• Design and conduct simple research including analysis and interpretation of research results.
• Document research problem, methodology all the way to research report writing.
• Defend the written research report.
• Familiarize themselves with ethical issues in the conduct of research.

• Explain the use of big-O, omega, and theta notation to describe the amount of work done by an algorithm.
• Use big-O, omega, and theta notation to give asymptotic upper, lower, and tight bounds on time and space complexity of algorithms.
• Determine the time and space complexity of simple algorithms.
• Deduce recurrence relations that describe the time complexity of recursively defined algorithms.
• Solve elementary recurrence relations.
• Identify practical examples for different strategies (brute-force, greedy, divide-and-conquer, recursive backtracking, and dynamic programming).
• Use pattern matching to analyze substrings.
• Use numerical approximation to solve mathematical problems, such as finding the roots of a polynomial.

• Acquire comprehensive knowledge and understanding of project management, including planning, scheduling, and resource utilization.
• Be adept at efficiently managing project resources, making procurement decisions, and effectively monitoring and executing projects with excellent communication and time management skills.
• Be prepared to successfully lead and oversee projects, ensuring their timely and successful completion.
• Be equipped with the necessary skills to handle project complexities, adapt to changing circumstances, and make informed decisions to achieve project goals.
• Be prepared for real-world project management scenarios, enabling them to excel in managing and delivering successful projects.

• Conduct independent or group investigations in Data Science.
• Submit a written proposal outlining their project.
• Address software, hardware, communication, or network-related problems in Data Science.
• Analyze data using computer resources.
• Produce a formal written report and deliver an oral presentation.

• Understand various methods for data visualisation and how to choose between them.
• Use tables, graphs, images, and video animations for effective data presentation.
• Create engaging and interactive visualisations to communicate data insights.
• Summarise and work with data using tables, graphs, and plots.
• Employ video animation to present data in an engaging manner.
• Conduct practical experiments and hands-on lab work to master data visualisation techniques.

• Understand the fundamentals of Neural Networks and Deep Learning.
• Implement supervised learning using Neural Networks.
• Grasp the concepts of activation functions and backpropagation.
• Build and optimize deep neural networks.
• Perform practical experiments with Python, Jupyter Notebooks, and NumPy.
• Apply key concepts in Neural Networks to real-world problems.

• Grasp Cloud computing fundamentals and parallel algorithms.
• Use languages, tools, and systems for parallel processing.
• Implement Cloud services for data analytics and storage.
• Explain distributed systems, databases, and file systems.
• Apply Cloud infrastructure and manage Cloud operations.
• Optimise performance and scalability in the Cloud environment.
• Explore legal aspects and Service Level Agreements.
• Utilise Data Science tools and services in the Cloud.

• Apply statistical concepts and BI techniques.
• Use SQL and Tableau for data analysis and visualisation.
• Create reports, metrics, and dashboards for business insights.
• Perform data preprocessing and historical analysis.
• Understand database theory and SQL best practices.
• Apply data for improved business decision making.
• Conduct practical lab work on data manipulation and visualisation.

• Master the data science process from preprocessing to model deployment.
• Apply supervised and unsupervised learning techniques.
• Implement and evaluate machine learning models.
• Use popular data science libraries like scikit-learn, PyTorch, TensorFlow, and Keras.

• Successfully implemented the project based on their chosen topic.
• Evaluated the outcomes and results of the project.
• Prepared a formal written report comprising chapters 1-5 with the approval of the supervisor.
• Presented their findings and project outcomes orally.
• Demonstrated proficiency in data science skills and techniques through project completion.

• Recognise operating system types and structures.
• Describe OS support for processes and threads.
• Recognise CPU scheduling, synchronisation, and deadlock.
• Resolve OS issues related to synchronisation and failure for distributed systems.
• Explain OS support for virtual memory, disk scheduling, I/O, and file systems.
• Identify security and protection issues in computer systems.
• Use C and Unix commands, examine behaviour and performance of Linux.
• Develop various system programmes under Linux to make use of OS concepts related to process synchronisation, shared memory, mailboxes, file systems, etc.

• Analyse time series data using statistical methods.
• Conduct forecasting and identify patterns in time series.
• Apply Autoregressive (AR), Moving Average (MA), ARMA, and ARIMA models.
• Utilize smoothing techniques and handle stationarity in time series.
• Interpret and present results using various plots and forecasts.