BACHELOR OF SCIENCE HONOURS DATA SCIENCE AND INFORMATICS (HDS)

Module Code	Module Name	Description
HDS101	Principles of Programming Languages	This module examines the concepts and structures governing the design and implementation of programming languages. One major outcome is for students to be able to express syntax and semantics in formal notation, apply suitable programming paradigms and understand high level language design and implementation. Upon completion, students should be able to manage files with operating system commands, use top-down algorithm design, implement algorithmic solutions in a programming language and write small programs that allow them to accomplish useful goals.
HDS102	Operating Systems	This module explores the design and implementation of computer operating systems. Topics include historical aspects of operating systems development, systems programming, process scheduling, synchronization of concurrent processes, deadlocks, virtual machines, memory management, virtual memory and paging, I/O and file systems, system security, os/architecture interaction, and distributed operating systems.
HDS103	Calculus	This module provides an introduction to the basic knowledge of limit theorem, including functional limit, continuity of function, and the calculation of limit; includes calculus methods such as derivative and differential, integral, ordinary differential equation and Improper integrals; series, convergence criteria, convergence domain and uniform convergence of function series, term-by-term limit, term-by-term summation, term-by-term integral.
HDS104	Discrete Mathematics	This module introduces and discusses the fundamentals of the discrete as apply to computer science, focusing on providing a basic theoretical foundation for further work. Students are exposed to logic and proof techniques, set theory, elementary number theory, functions and relations, graphs, trees, modelling computations and abstract algebra. This module integrates symbolic tools, graphical concepts, and numerical calculations. Techniques of counting: permutations, combinations, recurrences, algorithms are also covered
HDS111	Introduction to Data Science	Introduction to Data Science is a module designed to familiarize students with the operation of computers and various application programs and equip them to use computers in different areas of study as used in today’s global environment. It presents the following concepts; Basic Computer Concepts, Data Processing Cycle, Number System, Computer Arithmetic, Types of Computer, Evolution of Computers, Computer Applications. It also introduces topics such as computer hardware and software, file management, the Internet, the social web, green computing, security and computer ethics.
HDS112	Web Technologies	Students will cover Web design fundamentals and internet technologies. They will have an appreciation of Web communication protocols and methods. They will learn how to use Scripting languages, interface design tools, Application Programming Interface (API), Web Services and design patterns in Web Application Development
HDS113	Data Structures and Algorithms	This module builds on the programming skills acquired in Principles of Programming Languages. It couples work on Program Design, Analysis, and Verification with an introduction to the study of Data Structures. Students are able to identify appropriate data structures for solving computing problems in respective language and write several programs, ranging from very short programs to more elaborate systems, in any language. Emphasis is placed on the development of clear, modular programs that are easy to read, debug, verify, analyze, and modify.
HDS114	Database Systems	This module provides knowledge of database design and data management principles. Students develop skills in relational modelling, SQL, and normalization. Transaction processing and data integrity concepts are emphasized. Exposure to NoSQL databases is provided. Ethical and secure data handling practices are introduced. The module prepares learners for managing organizational data resources. Practical database design projects are undertaken. Learners understand scalable data storage solutions. Topics covered Database concepts; data models; entity-relationship modelling; relational algebra; SQL; normalization; transactions and concurrency control; database security; backup and recovery; introduction to NoSQL databases.
HDS201	Software Engineering	The module gives the students practical experience in applying Software Life Cycle Models, standards and technologies. Topics covered include: Software Process and Problems, Requirements, Specifications and Software Design approaches, Modular Designs, CASE tools, Case Studies. Object Oriented Analysis and Design using the Unified Modeling Language (UML) to capture Requirements, Model User Interactions and Business Process, Verification and Validation, Implementation and Integration Issues, Reuse Concepts and Maintenance
HDS202	Artificial Intelligence	The module develops in students a working knowledge of intelligent systems as well as the understanding of the application of artificial reasoning in industrial processes. Topics covered include: An introduction to intelligence, historical and current trends and characterisation of knowledge-based systems, knowledge representation, reasoning, problem solving, and learning. It then explores those further by surveying current applications in selected areas such as game playing and natural language processing.
HDS203	Applied Statistics	This module equips learners with practical statistical knowledge and analytical skills required for data-driven decision-making in computing and related disciplines. It focuses on the application of statistical methods to real-world problems, emphasizing data exploration, inference, and interpretation of results. Learners develop competence in selecting appropriate statistical techniques, analysing datasets, and communicating findings effectively using statistical software. Indicative content includes descriptive statistics, probability distributions, sampling techniques, hypothesis testing, confidence intervals, correlation and regression analysis, analysis of variance (ANOVA), non-parametric methods, time series analysis, and introduction to multivariate analysis. The module also addresses data quality, statistical assumptions, ethical use of data, and reporting of statistical results in research and professional contexts. Practical laboratory sessions and case studies reinforce theoretical concepts through hands-on analysis of real datasets.
HDS204	Linear Algebra	This module will introduce basic concepts and techniques from linear algebra that will be required in later modules in areas such as computer graphics and machine learning. Topics include eigenvalues and eigenvectors, vector linear independence and bases for vector spaces, spaces and subspaces, orthogonal vector spaces and the Gram-Schmidt orthogonalisation process, matrices, inverse matrices, solution of linear systems, elementary matrix factorisations, determinants of matrices, linear transformations
HDS211	Data Communications and Computer Networks	This module explores the principles underlying the design of computer networks. Topics covered include: Computer network technologies and applications, Transmission Media, Signaling, Communication protocols, Communication architectures, Network connections, Network types, Routing and routing algorithms, Spanning tree protocol and IP addressing.
HDS212	Business Intelligence	The objective of this module is to give students a broad overview of managerial, strategic and technical issues associated with Business Intelligence Topics covered will include introduction to business intelligence; Effective and timely decisions – Data, information and knowledge – Role of mathematical models – Business intelligence architectures: Cycle of a business intelligence analysis – Enabling factors in business intelligence projects – Development of a business intelligence system – Ethics and business intelligence; Knowledge delivery; The business intelligence user types, Standard reports, Interactive Analysis and Ad Hoc Querying, Parameterized Reports and Self-Service Reporting, dimensional analysis, Alerts/Notifications, Visualization: Charts, Graphs, Widgets, Scorecards and Dashboards, Geographic Visualization, Integrated Analytics, Considerations: Optimizing the Presentation for the Right Message. Efficiency; Efficiency measures – The CCR model: Definition of target objectives- Peer groups – Identification of good operating practices; cross efficiency analysis – virtual inputs and outputs – Other models. Pattern matching – cluster analysis, outlier analysis
HDS213	Machine Learning	This subject examines the design, implementation, and analysis of machine learning algorithms. It covers examples of supervised learning algorithms (including decision tree learning, support vector machines, and neural networks), unsupervised learning algorithms (including k-means and expectation maximization), and optionally reinforcement learning algorithms (such as Q learning and temporal difference learning). It introduces methods for the evaluation of learning algorithms, as well as topics in computational learning theory. The module develops predictive analytics and modelling competence. Students gain knowledge of supervised and unsupervised learning techniques. Skills in model training, evaluation, and validation are developed. Feature selection and performance optimization are emphasized. Ethical use of machine learning is introduced. The module supports data-driven innovation. Learners implement models on real datasets. The module strengthens analytical experimentation skills.Topics Covered- Machine learning overview; supervised learning; unsupervised learning; regression and classification; clustering; model evaluation metrics; feature engineering; overfitting and regularization; practical machine learning tools.
HDS260	Group Project	The students work in a group to produce a working Product including a Project Proposal, a Project Plan, a System Specification, User Documentation and perform Project Management Activities to ensure the product is delivered on time. The Implementation includes Developing the Product, Testing and Evaluation and Presentation. This module develops teamwork and applied problem-solving skills. Students collaboratively address real-world data science problems. Project planning, execution, and reporting skills are developed. Data analysis and solution development are emphasized. Professional communication and collaboration are assessed. The module enhances employability and practical competence. Learners manage time and resources effectively. Industry-relevant problem contexts are emphasized. Topics Covered- Project identification; problem definition; project planning; teamwork and collaboration; data collection and analysis; solution development; project documentation; presentations; evaluation and reflection.
HDS301	Big Data Technologies	This module covers: Introduction to Big data: definition, characteristics, Big Data systems organisation and Design, Big Data Analytics, Big Data analytics platforms and tools (including Hadoop, Spark, and cloud based Big Data services), Big Data Infrastructure management and operation, Big Data systems for application domains, Big Data software (systems) architectures. This module introduces large-scale data processing concepts and tools. Students acquire knowledge of distributed storage and computing frameworks. Skills in handling high-volume and high-velocity data are developed. Performance and scalability considerations are emphasized. Practical exposure to big data platforms is provided. The module prepares students for advanced analytics environments. Learners address real big data challenges. Cloud-based processing concepts are introduced. Topics Covered- Big data characteristics; distributed computing concepts; Hadoop ecosystem; Spark framework; distributed storage; stream processing; scalability and performance; cloud-based big data solutions.
HDS311	Data Warehousing and Mining	This module covers; Introduction to Data Mining, data warehousing, Differences between operational data base systems and data Ware House, Data Ware House characteristics, Data Ware House Architecture and its components, Extraction-Transformation-Loading, Logical (Multidimensional), Data Modeling, Schema Design, star and snow-Flake Schema, Fact Constellation, Fact Table, Fully Addictive, Semi-Addictive, Non-Addictive Measures; FactLess-Facts, Dimension Table characteristics; Fact-Less-Facts, Dimension Table characteristics; OLAP cube, OLAP Operations, OLAP Server Architecture-ROLAP, MOLAP and HOLAP. Data Pre-processing, Classification and Prediction, Clustering, Association Rules, Applications and other Data mining techniques
HDS360	Industrial Attachment	This module provides structured industrial training experience. Students apply academic knowledge in professional environments. Practical skills and industry-relevant competencies are developed. Professional ethics and workplace conduct are emphasized. Students gain exposure to real data science workflows. The module enhances career readiness. Industry supervision ensures skills relevance. Reflective learning and reporting are required. Topics Covered- Workplace orientation; application of data science tools; professional ethics; organizational processes; teamwork; problem solving in industry; technical reporting; reflective practice.
HDS380	Internship Project	This module develops applied research and problem-solving competence. Students undertake an industry-based data science project. Analytical, technical, and documentation skills are demonstrated. Independent learning and innovation are emphasized. Results are formally reported and presented. The module integrates theory with practice. Learners address real organizational challenges. Professional reporting standards are enforced. Topics Covered- Project proposal development; applied data analysis; system or model development; evaluation of solutions; technical documentation; presentation of findings; professional practice
HDS401	Deep Learning	This module builds on Machine Learning, It covers: Feedforward Neural networks. Gradient descent and the backpropagation algorithm. Unit saturation, aka the vanishing gradient problem, and ways to mitigate it. RelU Heuristics for avoiding bad local minima. Heuristics for faster training. Nestors accelerated gradient descent. Regularization. Dropout; Convolutional Neural Networks, Recurrent Neural Networks, LSTM, GRU, Encoder Decoder architectures; Deep Unsupervised Learning, Attention and memory models, Dynamic memory networks, Applications of Deep Learning in Computer Vision, Applications of Deep Learning in NLP.
HDS402	Information Security	The module covers concepts and applied methods in information security encompassing the confidentiality, integrity, and availability model of information assets. Topics include security threats, cryptography, security analysis (attack taxonomy), access control, threat protection, Security Policies, Laws, Computer Crimes and Cyber Security. Themodule provides learners with foundational and applied knowledge of information security principles as they relate to data-driven systems and analytics environments. It focuses on protecting data, models, and computational infrastructures from unauthorized access, misuse, and cyber threats throughout the data lifecycle. Learners examine security policies, risk management practices, and technical controls essential for safeguarding data assets in data science projects. Indicative content includes information security fundamentals, confidentiality–integrity–availability (CIA) triad, data classification and governance, cryptography basics, access control and identity management, secure data storage and transmission, privacy and data protection regulations, security in databases and data warehouses, securing data pipelines and cloud-based analytics platforms, threat modeling, incident response, and ethical considerations in data science. Practical case studies reinforce secure handling and analysis of data in real-world data science applications.
HDS411	Natural Language Processing	This module equips students with text processing theories and techniques. Therefore, the module covers: the definition of NLP, Tokenization, N-grams and Scriptio continua, Stemming and Lemmatization, Synsets and Hypernyms, POS Tagging and Stopwords, Text “Features” and TF-IDF Classification, Named Entity Recognition (NER), Sentiment Analysis, Topic Modeling Basics, Stylometry & Stylometric Analysis, Dendograms, PCA scatterplots & k-means, Document Clustering and Word Vectors, Doc2vec, Word2vec
HDS412	Image Analysis and Computer Vision	This subject will cover the following: Overview, computer imaging systems, lenses; Image formation and sensing, Image analysis, preprocessing, Binary image analysis, Edge detection, Edge detection performance, Hough transform, corner detection, Segmentation, Morphological filtering, Fourier transform, Feature extraction, shape, histogram, color, spectral, texture, using CVIPtools; Pattern classification (face recognition, object detection)
HDS460	Capstone Project 1	The Capstone Design Project involves supervision. This project is intended for the student to demonstrate skills acquired to develop systems for an organisation of their choice. Candidates must be able to demonstrate the highest level of innovation.This module introduces learners to the first phase of the data science capstone project, focusing on problem identification, research design, and project planning. Students apply data science principles to define a real-world problem, formulate objectives, and conduct a comprehensive literature review. Emphasis is placed on data acquisition strategies, ethical considerations, and selection of appropriate analytical methodologies and tools. Indicative content includes problem scoping, research questions and hypotheses, data sourcing and collection, data governance and ethics, exploratory data analysis planning, methodology selection, project proposal writing and project management techniques. Learners produce a formally structured project proposal that demonstrates readiness for implementation in Capstone Project 2.
HDS461	Capstone Project 2	This module constitutes the implementation and completion phase of the data science capstone project. Learners design, develop, and evaluate a complete data science solution using real-world datasets, integrating analytical, computational, and communication skills. The module emphasizes model development, validation, interpretation of results, and deployment considerations. Indicative content includes data preprocessing and feature engineering, statistical and machine learning model development, model evaluation and optimization, data visualization and storytelling, reproducibility and documentation, ethical and legal compliance, and preparation of the final project report and presentation. The module culminates in the submission and defense of a comprehensive capstone project demonstrating professional-level data science competence. The Capstone Design Project also involves supervision. This project is intended for the student to demonstrate skills acquired to develop systems for an organisation of their choice. Candidates must be able to demonstrate the highest level of innovation.