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.

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

• Explain the concepts, characteristics, and theories of entrepreneurship, intrapreneurship, opportunity seeking, new value creation, and risk-taking.
• Analyze 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.

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

• 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.

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

• 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.
• Design and implement programming problems using loops and use & implement classes as data abstractions in an object-oriented approach.
• Implement simple exception handling in programs.
• Develop programs with input/output from text files and design and implement programming problems involving arrays.

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

• Convert logical statements from informal language to propositional and predicate logic expressions.
• Apply each of the proof techniques correctly in the construction of a sound argument.
• Map real-world applications to appropriate counting formalisms subject to constraints on the seating arrangement.
• Solve a variety of basic recurrence relations.

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

• Demonstrate the principles of working with data across distributions, sizes, and ranges.
• Explain from first principles the operations that power data-driven utilities that have transformed the modern computing industry.
• Demonstrate foundational technological processes that enable various data functions.

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

• Utilize the R programming language for data-driven functions and utilities that have been lauded across the computing industry.
• Explain the structures, functions, and operations that power the utilities of this language across various application domains.
• Apply the R programming language to various data-driven use-cases in practical problem domains in the real world.

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

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

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

• 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.

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

• 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 qualification theory.
• Explain the nature of arguments, validity, and soundness.
• Use techniques for evaluating arguments and distinguish between inductive and deductive inferences.

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

• Develop solutions for a range of problems using object-oriented programming in C++.
• Use modules/packages/namespaces for program organization.
• Use API in writing applications.
• Apply the divide and conquer strategy to searching and sorting problems using iterative and/or recursive solutions.
• Explain the concept of exceptions in programming and how to handle exceptions in programs.
• Write simple multithreaded applications and design and implement simple GUI applications.

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

• Explain different instruction formats, such as addresses per instruction and variable length vs. fixed length formats.
• Describe the organization of the classical von Neumann machine and its major functional units.
• Explain how subroutine calls are handled at the assembly level.
• Describe the basic concepts of interrupts and I/O operations.
• Write simple assembly language program segments.
• Show how fundamental high-level programming constructs are implemented 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.

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

• Draw conclusions based on statistical assumptions, models, and results.
• Draw inferences on statistical outcomes and real-world implications.
• Demonstrate the various considerations applied for communicating statistical solutions to real problems.
• Make conclusions based on statistical models and results by applying a broad range of statistical tools and packages.
• Demonstrate logical, meaningful skills that bother not just on the relevance of the data that informed the statistical outcomes but also on the real-world implications of how these outcomes are factored into decision-making processes.

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

• Master linear algebra concepts, including solving linear equations, change of basis, eigenvectors, eigenvalues, Caley-Hamilton theorem, symmetric matrices, positive definite matrices, and orthogonal diagonalization.
• Confidently use similar matrices, linear transformations, singular value decomposition, and orthogonal projections.
• Apply orthonormal bases and the Gram-Schmidt process effectively, making them adept at handling various mathematical problems and real-world applications.

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

• 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.

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

• Work in a private and public organization for a period of three months.
• Acquire practical experience and develop skills in all areas of data science.
• Produce a comprehensive report summarizing the knowledge gained and the experiences encountered throughout the training, demonstrating their proficiency in the field of data science.

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

• Demonstrate a comprehensive understanding of fundamental programming concepts and data structures in C++ such as primitive types, arrays, records, strings, and string processing.
• Have a solid grasp of data representation in memory and be able to effectively allocate memory on the stack and heap.
• Be proficient in implementing and applying various data structures including queues and trees, utilizing appropriate implementation strategies.
• Be skilled in managing run-time storage effectively through pointers and references, and adept at working with linked structures.
• Gain practical experience in writing C++ functions and implementing algorithms for arrays, records, string processing, queues, trees, pointers, and linked structures, further enhancing their proficiency in C++ programming and data structure manipulation.

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

• Exhibit the ability to 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, safeguarding information assets, and assessing the impact of cybersecurity on various institutions and applications.
• Recognize the methods and motives of cybersecurity incident perpetrators, the countermeasures employed by organizations and agencies, and the ethical obligations of security professionals.
• Evaluate cybersecurity and national security strategies and define evolving cybersecurity requirements and strategies to mitigate significant risks.

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

• Learn data wrangling concepts and data quality assessment.
• Understand data integrity and Python programming basics.
• Identify three dimensions of data quality: validity, reliability, and representativeness.
• Improve data quality through cleaning and augmentation.
• Handle structured and unstructured data in Python.
• Access and process data from files and web sources.
• Master data manipulation: join, reshape, and visualize data.
• Perform data aggregation and group operations.
• Handle categorical data and advanced group-by techniques.
• Utilize method chaining for data analysis.
• Explore Python libraries for modeling and analysis.
• Apply modeling libraries for data analysis and prediction.
• Use advanced NumPy for numerical operations.
• Interact with the operating system using Python.
• Work with XML, JSON, and APIs; Acquire web scraping skills for data retrieval.

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

• Explain data protection and privacy concepts.
• Develop privacy algorithms for secure data querying.
• Identify privacy aspects in data use.
• Explain privacy laws and data protection.
• Apply fair information principles for privacy.
• Manage privacy incidents and operations.
• Explain IT security and threats.
• Explain cryptology and encryption practices.
• Explain AAA security (Authentication, Authorization, Accountability).
• Secure networks and protect cloud data.
• Address SQL vulnerabilities and data breaches.
• Comply with data privacy laws in the cloud.
• Explain digital security and ethics.

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

• Explain IoT concepts and applications.
• Explain IoT device programming and communication.
• Describe IoT protocol stacks and networking.
• Explore data science and cloud platforms for IoT.
• Recognize legal and ethical considerations in IoT.
• Explain cybersecurity and digital twins in IoT.
• Apply IoT mindsets in product and business design.
• Gain practical experience through lab work.
• Study IoT infrastructure and networking.

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

• Analyze 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 organizations, media, and traditional institutions in peacebuilding.

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

• Identify business opportunities in Nigeria through environmental scanning and market research, considering social, climate, and technological factors.
• Understand entrepreneurial finance options like venture capital, equity finance, microfinance, and small business investment organizations.
• Grasp the principles of marketing, customer acquisition, and retention, as well as e-commerce models (B2B, C2C, B2C), learning from successful e-commerce companies.
• Acquire skills in small business management, family business dynamics, negotiation, and modern business communication methods.
• Demonstrate their ability to generate business ideas and explore emerging technologies for market solutions and digital business strategies.

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

• Install and use Cloudera VM, Jupyter, Spark, Hadoop, MongoDB, and Postgres.
• Analyze Twitter data using Spark and MongoDB.
• Build machine learning dashboards and web apps with NoSQL databases.
• Design cost-efficient big data solutions and consider security aspects.

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

• 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 and conduct market research.
• Deliver effective technical presentations.
• Analyze successful entrepreneurial ventures.

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

• Recognize 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.

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

• Apply supervised and unsupervised learning techniques.
• Use Decision Trees, linear regression, and logistic regression.
• Implement SVM, clustering, and ensemble methods.
• Understand probabilistic methods and model evaluation.
• Get an introduction to neural networks and Auto-encoders.

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

• 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.
• Optimize performance and scalability in the Cloud environment.
• Explore legal aspects and Service Level Agreements.
• Utilize Data Science tools and services in the Cloud.

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

• Analyze and interpret real-world statistical events.
• Utilize various principles and concepts from the broad theory of probability and adjoining statistical and mathematical fields.
• Apply statistical principles and concepts to analyze data.
• Analyze and interpret real-world statistical events by applying various principles and concepts from the broad theory of probability and adjoining statistical and mathematical fields.

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

• Describe the components of a database system and give examples of their use.
• Describe the differences between relational and semi-structured data models.
• Explain and demonstrate the concepts of entity integrity constraint and referential integrity constraint.
• Apply queries, query optimizations and functional dependencies in relational databases.
• Describe properties of normal forms and explain the impact of normalization on the efficiency of database operations.
• Describe database security and integrity issues and their importance in database design.
• Explain the concepts of concurrency control and recovery mechanisms in databases.

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

• Apply practical experience and skills in Data Science during a three-month attachment with private and public organizations.
• Have acquired hands-on training and supervision to develop proficiency in various Data Science areas.
• Maintain records to monitor their performance during the training period.
• Submit a comprehensive report on their gained experience and defend their reports to showcase their learning and achievements.

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

• 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.

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

• 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.

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

• 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.

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

• 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.

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

• 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.

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

• 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.

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

• Understand data warehousing fundamentals, architecture, and data modelling.
• Implement data warehouses and data marts using SQL servers.
• Grasp the concepts of data lakes and Hadoop File System.
• Optimise data lakes for self-service and govern data access.
• Perform practical experiments in building data warehouses, marts, and lakes.

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

• 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.

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

• 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.

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

• Successfully implement the project based on their chosen topic.
• Evaluate the outcomes and results of the project.
• Prepare a formal written report comprising chapters 1-5 with the approval of the supervisor.
• Present their findings and project outcomes orally.
• Demonstrate proficiency in data science skills and techniques through project completion.

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

• 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 behavior and performance of Linux, and develop various system programs under Linux to make use of OS concepts related to process synchronization, shared memory, mailboxes, file systems, etc.

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

• Analyze 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.