BACHELOR OF SCIENCE HONOURS DATA SCIENCE AND SYSTEMS (HDSC)
| Module Code | Module Name | Description |
|---|---|---|
| HASTS101 | Probability Theory | Introduction to the theory of probability with emphasis on understanding of concepts. Probability, probability space, random variables, independence, conditional probability, conditional expectation, integral version of the law of total probability, Bayes' formula for densities, moment generating functions, joint MGF's, characteristic functions, multinomial and multivariate normal distribution. |
| HDSC103 | Introduction to Data Science and Data Mining | This course aims to introduce students to the field of data science and data mining. Data Mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and forecasting, and generally improve their performance through interaction with data. The knowledge discovery process includes data selection, cleaning, coding, using different statistical and machine learning techniques, and visualization of the generated structures. Important related technologies, as data warehousing and on-line analytical processing (OLAP) will be also discussed. The students will be introduced to practical situations. Use of R & Python is recommended throughout this course. At the end of this course, students should be able to: Understand the importance of data science; Apply statistics and mathematics functions in R and Python; Clean and manipulate data using R and Python; To conduct data exploration in R and Python; interpret the contribution of data warehousing and data mining to the decision-support level of organizations; evaluate different models used for OLAP and data pre-processing; categorize and carefully differentiate between situations for applying different data-mining techniques: frequent pattern mining, association, correlation, classification, prediction, cluster, and outlier analysis; design and implement systems for data mining; evaluate the performance of different data-mining algorithms and propose data-mining solutions for different applications. |
| HDSC102 | Calculus | This course aims to gently introduce the rigour of mathematical analysis to first year undergraduate students with some background in A-Level calculus. The concentration is on motivating results and concepts geometrically rather than on providing rigorous proofs. Concepts are defined carefully and results stated precisely, but illustrated by way of vivid, concrete examples. The course covers topics such as: Vectors; Lines and planes. Vector-valued functions. Functions of several variables; Integration. Infinite series and Taylor series. |
| HDSC116 | Linear Mathematics and Ordinary Differential Equations | This course is mostly about algebraic ideas and complements the material in Calculus. It introduces and develops concepts (including complex numbers and the algebra of polynomials) necessary for a first course in linear algebra. Along the way some of the mathematical areas where linear structure emerges as the common feature are included to give motivation and to illustrate the theory. Vector Spaces: Basic definitions and examples, subspaces, dimension and basis, the vector spaces and subspaces. Inner Product spaces: Basic definitions with many examples, the notions of norm (length) and distance (emphasis will be on the Euclidean inner product). Linear Transformations: Basic definitions and results, including images and kernels, with examples; matrix representations of linear transformations. Revision of the basic techniques for the solution of first and second order ODE's. Modelling with first and second-order ODE's. Existence and uniqueness of solutions. Series solutions of linear ODE's, solutions near ordinary points, singular points, Euler equations. Laplace transform methods for solving initial value problem (IVP). Solution of IVP, Heaviside and Dirac functions. |
| HDSC105 | Programming Fundamentals | This course introduces the fundamental concepts of programming and problem solving in a structured program logic environment. This course is aimed at students with little or no prior programming experience but a desire to understand computational approaches to problem solving. Topics include language syntax, data types, program organization, and problem solving methods, algorithm design, and logic control structures. Students will also learn to document and debug software and to utilize software libraries. 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. |
| HASTS111 |
Statistical Inference |
General overview of the ideas of statistical inference. Estimation: Methods of estimation, moments, maximum likelihood and least squares. Desirable properties of estimators such as sufficiency, unbiasedness, consistency and minimum variance. Sampling distributions of the estimators. Confidence intervals for normal random variables. Tests of Hypothesis: Review of Applied Statistics 1 non parametric methods. Introduction, intuitive choice of test statistics and construction of critical regions, types of errors, sampling distribution of test statistics, size and power of the test, tests on the mean, variance and proportion based on samples from normal populations, interpretation of Minitab outputs. |
| IDSC101 | Information and Digital Skills | This short course provides a foundational overview of essential digital competencies needed to thrive in modern work and personal environments. It covers key areas including computer basics, file management, internet navigation, online communication, cybersecurity, and productivity tools (such as word processing and spreadsheets). |
| HASTS112 |
Regression Analysis and ANOVA |
Review of Multivariate normal distribution, matrix and vector algebra linear model in matrix notation. Fitting a straight line by least squares; lack of fit; pure error; review of multiple linear regression. Estimation, testing hypotheses and confidence regions for parameters in full rank linear regression model; polynomial regression models. |
| HDSC107 |
Data Structures and Algorithms |
This course introduces data structure and algorithms, types of data structures and programming principles. Recursive function, algorithm efficiency, order of magnitude analysis and Big O notation will be discussed. Students will implement operations that can be applied to data structures using various sorting and searching techniques. Further, students will be exposed to linear data structures such as linked lists, stack and queue. Non-linear data structures such as tree and graphs will also be discussed. |
| SDLSSC101 | Student Development and Life Skills | This course equips learners with essential personal, social, and academic abilities beyond traditional subjects, focusing on self-awareness, critical thinking, communication, emotional regulation (like stress management), financial literacy, and career planning to foster adaptability, responsibility, and success in college and life. It bridges academic knowledge with practical competencies for navigating challenges, making sound decisions, and contributing positively to society |
| LAICXX101 | Language Acquisition and Intercultural Communication | This course explores the inseparable relationship between language, culture, and communication in an increasingly globalized world. It is designed to equip students with the knowledge and skills necessary to interact effectively and appropriately with people from diverse cultural and linguistic backgrounds. |
| HASTS201 |
Theory of Estimation and Testing of Hypotheses |
The course is an extension of Statistical inference with further consideration of theory of estimation and hypothesis testing. |
| HDSC201 | Machine Learning | Definition of learning systems. Goals and applications of machine learning. Aspects of developing a learning system: training data, concept representation, function approximation. Inductive Classification: The concept learning task. Concept learning as search through a hypothesis space. General-to-specific ordering of hypotheses. Finding maximally specific hypotheses. Version spaces and the candidate elimination algorithm. Learning conjunctive concepts. The importance of inductive bias. Decision Tree Learning: Representing concepts as decision trees. Recursive induction of decision trees. Picking the best splitting attribute: entropy and information gain. Searching for simple trees and computational complexity. Occam's razor. Over fitting, noisy data, and pruning. Ensemble Learning: Using committees of multiple hypotheses. Bagging, boosting, and DECORATE. Active learning with ensembles. Experimental Evaluation of Learning Algorithms: Measuring the accuracy of learned hypotheses. Comparing learning algorithms: cross-validation, learning curves, and statistical hypothesis testing. Computational Learning Theory: Models of learnability: learning in the limit; probably approximately correct (PAC) learning. Sample complexity: quantifying the number of examples needed to PAC learn. Computational complexity of training. Sample complexity for finite hypothesis spaces. PAC results for learning conjunctions, kDNF, and kCNF. Sample complexity for infinite hypothesis spaces, Vapnik-Chervonenkis dimension. |
| HDSC210 |
Software Engineering |
The course gives the students practical experience in applying all the 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, Maintenance. |
| HASTS202 |
Statistical Computing |
This course aims to introduce the student to a variety of modern computing techniques, including use of everyday packages for data storage, management, analysis and reporting. Students learn about hard statistical and actuarial packages including programming. Using Statistical Packages- SPSS, STATA, SAS, MINITAB, EPI-INFO, and CSPro for analysing different types of data. Programming - Using R or SPLUS for data analysis and simulation, Latex for typesetting in preparation for writing up Mathematical papers. Using EXCEL programming to solve various problems by designing algorithms and other new specialised programmes. Computing for Actuarial science students should include analysing actuarial data. |
| CCLSSC201 | Critical Consciousness and Life Skills for Science | This course focuses on achieving an in-depth understanding of the world, allowing for the perception and exposure of social and political contradictions. Critical consciousness also includes taking action against the oppressive elements in one's life that are illuminated by that understanding. The course also emphasizes critical thinking, problem-solving, communication, collaboration, and adaptability. These skills are vital in a rapidly changing world, where the ability to learn and apply knowledge in real-life situations is more important than ever. |
| SDLSSC201 |
Student Development & Life Skills for Science |
This course equips learners with essential personal, social, and academic abilities beyond traditional subjects, focusing on self-awareness, critical thinking, communication, emotional regulation (like stress management), financial literacy, and career planning to foster adaptability, responsibility, and success in college and life. It bridges academic knowledge with practical competencies for navigating challenges, making sound decisions, and contributing positively to society |
| HASTS211 | Time Series Analysis and Econometrics | The course focuses on model building for time series data in time domain. The role of econometrics in Zimbabwe, Review of General linear Model: least squares estimators, correlation matrix,partial correlation coefficients regression coefficients, Significance tests and confidence intervals. Multicollinearity, specification error, dummy variables. Generalised least squares: GLS estimator, heteroscedasticity, pure and mixed estimation, group observations and grouping of equations. Sources, consequences and conventional tests including Durbin-Watson, Thiel BLUS procedure, estimation and prediction. Stochastic Regressors: definition, instrumental variables, errors in variables. Lagged explanatory variables, lagged dependent variables and estimation including Koyck's lag Scheme. Simultaneous equations systems, endogenous and exogenous variables identification problem, restrictions on structural parameters, Two-stage and Three-stage least squares. |
| HASTS215 |
Multivariate Methods |
The course focuses on analysis of two or more variables. Properties; sampling distribution of sample mean and sample covariance matrix; Wishart distribution. Transformations to near normality; testing for normality. Hotelling's T2 distributions and likelihood ratio test; comparison with one dimensional case; confidence regions and simultaneous comparisons of component means; Bonferroni method of multiple comparisons. Comparing mean vectors from two populations; comparisons of several multivariate population means (one way MANOVA); simultaneous confidence intervals for treatment effects; profile analysis; ideas of two way MANOVA. Multivariate linear regression Review of eigenvalues and eigenvectors; spectral decomposition of symmetric matrix. Principal component analysis. Factor Analysis. Canonical correlation analysis. Discrimination and classification. Discriminant analysis, cluster analysis. |
| HDSC213 |
Database Design and Development |
This course focuses on the design and implementation of relational databases and includes extensive exposure to SQL. Practical methodologies for data analysis, data modelling and database design are examined, coupled with study of the relational database model. The course builds applied skills in data modelling, normalization, database design and the creation and management of database objects using SQL. The course operates within a framework that focuses on developing business problem-solving and communication skills, and extensive use is made of business case studies of limited scope. |
| HDSC204 | Artificial Intelligence and Expert Systems | The course develops among students a working knowledge of expert systems as well as the understanding of the application of artificial reasoning in industrial processes. Topics covered include: An introduction to intelligence, historical and currenttrends and characterization of knowledge-based systems, Search, logic and deduction, Knowledge representation, production system, expert systems, architecture of expert systems, criteria for selecting expert system shells, end-user interface, developer interface, system interface, inference engine, knowledge base, data interface. |
| RISSC201 | Research and Innovation Skills for Science | The course teaches students to conduct rigorous scientific inquiry, identify problems, design studies, analyze data, and transform new knowledge into practical solutions, products, or policies, bridging the gap between discovery and real-world impact through critical thinking, methodology, and innovative application of research findings |
| HDSC380 |
Industrial Attachment |
Students have to be attached to an institution (ZIMSTAT, research organizations, banks, insurance company, pension fund, government ministries, and parastatals) where statistics is applied for the purpose of gaining practical skills and professional experience for at least 9 months. |
| HDSC370 |
Industrial Attachment Project |
The project gives opportunity for a student to demonstrate his problem-solving skills in circumstances similar to those existing in everyday life for an Actuarial Scientist |
| SDLSSC301 | Student Development and Life Skills | This course equips learners with essential personal, social, and academic abilities beyond traditional subjects, focusing on self-awareness, critical thinking, communication, emotional regulation (like stress management), financial literacy, and career planning to foster adaptability, responsibility, and success in college and life. It bridges academic knowledge with practical competencies for navigating challenges, making sound decisions, and contributing positively to society |
| HASTS401 |
General Linear Models |
Form a unified underlying theory that covers simple and multiple linear regressions with special emphasis on full rank models. The course also covers aspects on the general linear hypothesis, models of not full rank and introduction to variance components. |
| HDSC402 |
Machine Learning and Neural Networks |
This module provides a broad view of machine learning and neural networks. You’ll learn how to solve common machine learning problems such as regression, classification, clustering, matrix completion and pattern recognition. You’ll explore how neural networks can be trained and optimised. You’ll learn how to develop machine learning systems rapidly, and you will learn how to verify and evaluate the results. |
| HDSC405 | Deep Learning | This course covers: Introduction to deep learning, deep sequence modelling, and introduction to tensor flow, deep computer vision, deep generative modelling, de-biasing facial recognition systems, deep reinforcement learning, limitations and new frontiers, pixels to control learning, robot learning, neural rendering and ML for scent. |
| HASTS416 |
Stochastic Processes |
The course covers classification of the general stochastic processes like the Poisson and Brownian Motion or Wiener Processes and review probability, Markov chains, Stochastic models, Queuing Theory, Renewal processes, Brownian motion and Martingales. |
| HDSC410 |
Big Data Technologies |
This course covers, Analytics: Names of Big Data; Case for Big Data; Big Data Options Team Challenge; Features of Big Data: Security; Compliance; Auditing and Protection; Big Data Characteristics: Volume, Velocity, Variety and Veracity; Major Modern Data Base Types, Database Systems Architecture; Relational, Federated, Map/Reduce; Systems Design and Architecture Considerations: Storage Engines; Query Languages; Concurrency; Physical Database Systems; Component/Architecture Considerations: Discs; Memory; Network; Distributed Architecture Share Architecture; Controllers. Analytic Scalability; Convergence; Parallel Processing Systems; Cloud Computing; Grid Reduce; Map Reduce; Enterprise Analytic Sandbox; Analytic Data Sets; Analytic Methods; Analytic Tools Cognos; Micro-strategy Pentaho. Analysis Approaches: Statistical Significance; Business Approaches Analytic Innovation; Traditional Approaches; Hadoop; Hive; No SQL Databases; Data for Commerce. |
| HDSC401 |
Data Mining and Data Analytics |
Using programs to automatically find structure in complex data sets has become fundamental in science and industry. This course will give advanced techniques for building programs that can model data. Topics include Attribute-oriented analysis: Attribute generalization, Attribute relevance, Class comparison, Statistical measures, Experiments with Weka - using filters and statistics; Data mining algorithms: Association rules; Motivation and terminology, Basic idea: item sets, Generating item sets and rules efficiently, Correlation analysis, Experiments with Weka - mining association rules. Data mining algorithms: Classification: Basic learning/mining tasks, Inferring rudimentary rules: 1R algorithm, Decision trees, Covering rules, Experiments with Weka - decision trees, rules. Data mining algorithms: Prediction: The prediction task, Statistical (Bayesian) classification, Bayesian networks, Instance-based methods (nearest neighbour), Linear models, Experiments with Weka – Prediction |
| HCT423 |
Design and Analysis of Algorithms |
The course introduces students to the design of computer algorithms, as well as analysis of sophisticated algorithms. Topics include asymptotic ("Big-oh") notation, sorting and searching, divide and conquer(master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts); data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis); greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees); shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search). |
| HASTS410 | Bioassays and Survival Models |
The course introduces students to methodologies used in modelling of binary and survival data. Biological, Agricultural and Medical applications of Linear models: regression diagnostics, constraints for parameters, covariance analysis models. Logistic and probit models for analysis of proportions and percentages: Use of bioassays including estimation of ED90 and ED50 values, variable selection methods for more complex logistic models e.g. Medical data with binary response. Censored data, Survivor function, hazard function, Kaplan Meier type of estimators, comparison of two groups of survival data, modelling survival data, Cox regression model. |
| HASTS470 |
Research Project |
Application of knowledge of Data Science through an in-depth research project to solve problems in society |