Code | Faculty | Department |
---|---|---|
12133215 | Faculty of Engineering, Built Environment and Information Technology | Department: Informatics |
Credits | Duration | NQF level |
---|---|---|
Minimum duration of study: 3 years | Total credits: 371 | NQF level: 07 |
Refer also to General Academic Regulation G4.
Important information for all prospective students for 2025
The admission requirements below apply to all who apply for admission to the University of Pretoria with a National Senior Certificate (NSC) and Independent Examination Board (IEB) qualifications. Click here for this Faculty Brochure.
Minimum requirements | ||
Achievement level | ||
English Home Language or English First Additional Language | Mathematics | APS |
NSC/IEB | NSC/IEB | |
5 | 5 | 30 |
The suggested second-choice programme for Bachelor of Information Technology in Information Systems is Bachelor of Information Science.
Life Orientation is excluded when calculating the APS.
Applicants currently in Grade 12 must apply with their final Grade 11 (or equivalent) results.
Applicants who have completed Grade 12 must apply with their final NSC or equivalent qualification results.
Please note that meeting the minimum academic requirements does not guarantee admission.
Successful candidates will be notified once admitted or conditionally admitted.
Unsuccessful candidates will be notified after 30 June.
Applicants should check their application status regularly on the UP Student Portal at click here.
Applicants with qualifications other than the abovementioned should refer to the International undergraduate prospectus 2025: Applicants with a school leaving certificate not issued by Umalusi (South Africa), available at click here.
International students: Click here.
Transferring students
A transferring student is a student who, at the time of applying at the University of Pretoria (UP) is/was a registered student at another tertiary institution. A transferring student will be considered for admission based on NSC or equivalent qualification and previous academic performance. Students who have been dismissed from other institutions due to poor academic performance will not be considered for admission to UP.
Closing dates: Same as above.
Returning students
A returning student is a student who, at the time of application for a degree programme is/was a registered student at UP, and wants to transfer to another degree at UP. A returning student will be considered for admission based on NSC or equivalent qualification and previous academic performance.
Note:
Closing date for applications from returning students
Unless capacity allows for an extension of the closing date, applications from returning students must be submitted before the end of August via your UP Student Centre.
Refer also to General Academic Regulation G4.
A degree (undergraduate) in the School of IT is conferred with distinction on a student who did not repeat any module of his/her final year, obtained a weighted average of at least 75% (not rounded) in all the prescribed modules for the final year, provided that a subminimum of 65% is obtained in each of these modules and provided that the degree is completed in the prescribed minimum period of time. Ad hoc cases will be considered by the Dean, in consultation with the relevant head of department.
Minimum credits: 130
Additional information:
In addition to all the compulsory core modules, students are required to choose their electives from what is referred to as an elective group. Once an elective group has been chosen, the modules listed per year level need to be completed to comply with the degree programme's requirements. These elective groups, along with their respective first-year modules are the following:
Computer Auditing: FRK 111, FRK 121, STK 110, STK 120 and INF 183
Entrepreneurship: FRK 111, FRK 122, STK 110, STK 120 and INF 183
eBusiness: FRK 111, FRK 122, STK 110, STK 120 and INF 183
Geography: ENV 101, BME 120, GGY, 156 and GMC 110
eTaxation: FRK 111, FRK 121, STK 110, STK 120 and INF 183
Data Science Management: EKN 110, EKN 120, STK 110 and STC 122
Module content:
Find, evaluate, process, manage and present information resources for academic purposes using appropriate technology.
Module content:
Apply effective search strategies in different technological environments. Demonstrate the ethical and fair use of information resources. Integrate 21st-century communications into the management of academic information.
Module content:
By the end of this module students should be able to cope more confidently and competently with the reading, writing and critical thinking demands that are characteristic of the field of Information Technology.
Module content:
Introduction to information systems, information systems in organisations, hardware: input, processing, output, software: systems and application software, organisation of data and information, telecommunications and networks, the Internet and Intranet. Transaction processing systems, management information systems, decision support systems, information systems in business and society, systems analysis, systems design, implementation, maintenance and revision.
Module content:
General systems theory, creative problem solving, the business analyst, systems development building blocks, systems analysis methods, process modelling and data modelling.
Module content:
The entrepreneurial mind-set; managers and managing; values, attitudes, emotions, and culture: the manager as a person; ethics and social responsibility; decision making; leadership and responsible leadership; effective groups and teams; managing organizational structure and culture inclusive of the different functions of a generic organisation and how they interact (marketing; finance; operations; human resources and general management); contextualising Sustainable Development Goals (SDG) in each of the topics.
Module content:
Value chain management: functional strategies for competitive advantage; human resource management; managing diverse employees in a multicultural environment; motivation and performance; using advanced information technology to increase performance; production and operations management; financial management; corporate entrepreneurship.
Module content:
Simple statistical analysis: Data collection and analysis: Samples, tabulation, graphical representation, describing location, spread and skewness. Introductory probability and distribution theory. Sampling distributions and the central limit theorem. Statistical inference: Basic principles, estimation and testing in the one- and two-sample cases (parametric and non-parametric). Introduction to experimental design. One- and twoway designs, randomised blocks. Multiple statistical analysis: Bivariate data sets: Curve fitting (linear and non-linear), growth curves. Statistical inference in the simple regression case. Categorical analysis: Testing goodness of fit and contingency tables. Multiple regression and correlation: Fitting and testing of models. Residual analysis. Computer literacy: Use of computer packages in data analysis and report writing.
Module content:
This module deals with the core principles of economics. A distinction between macroeconomics and microeconomics is made. A discussion of the market system and circular flow of goods, services and money is followed by a section dealing with microeconomic principles, including demand and supply analysis, consumer behaviour and utility maximisation, production and the costs thereof, and the different market models and firm behaviour. Labour market institutions and issues, wage determination, as well as income inequality and poverty are also addressed. A section of money, banking, interest rates and monetary policy concludes the course.
Module content:
This module deals with the core principles of economics, especially macroeconomic measurement the private and public sectors of the South African economy receive attention, while basic macroeconomic relationships and the measurement of domestic output and national income are discussed. Aggregate demand and supply analysis stands core to this course which is also used to introduce students to the analysis of economic growth, unemployment and inflation. The microeconomics of government is addressed in a separate section, followed by a section on international economics, focusing on international trade, exchange rates and the balance of payments. The economics of developing countries and South Africa in the global economy conclude the course.
Module content:
Introducing the basic concepts and interrelationships required to understand the complexity of natural environmental problems, covering an introduction to environmental science and biogeography; including a first introduction to SDGs and Aichi targets.
Module content:
The nature and function of accounting; the development of accounting; financial position; financial result; the recording process; processing of accounting data; treatment of VAT; elementary income statement and balance sheet; flow of documents; accounting systems; introduction to internal control and internal control measures; bank reconciliations; control accounts; adjustments; financial statements of a sole proprietorship; the accounting framework.
Module content:
Property, plant and equipment; intangible assets; inventories; liabilities; presentation of financial statements; enterprises without profit motive; partnerships; companies; close corporations; cash flow statements; analysis and interpretation of financial statements.
Module content:
Budgeting, payroll accounting, taxation – income tax and an introduction to other types of taxes, credit and the new Credit Act, insurance, accounting for inventories (focus on inventory and the accounting entries, not calculations), interpretation of financial statements.
Module content:
This module begins by fostering an understanding of human geography. Then follows with the political ordering of space; cultural diversity as well as ethnic geography globally and locally; population geography of the world and South Africa: and four economic levels of development. The purpose is to place South Africa in a world setting and to understand the future of the country.
Module content:
History, present and future of cartography. Introductory geodesy: shape of the earth, graticule and grids, datum definition, elementary map projection theory, spherical calculations. Representation of geographical data on maps: Cartographic design, cartographic abstraction, levels of measurement and visual variables. Semiotics for cartography: signs, sign systems, map semantics and syntactics, explicit and implicit meaning of maps (map pragmatics). Critique maps of indicators to measure United Nations Sustainable Development Goals in South Africa.
Module content:
Introduction to data and exploratory data analysis: Graphical representations and descriptive measures for numerical and categorical data; relationships between explanatory and response variables; data transformations. Foundations of inference: Simulation; sampling with and without replacement; confidence intervals with bootstrapping; hypothesis testing with randomization; inference with mathematical models (normal distribution and central limit theorem). Statistical inference: Inference for a single proportion, for comparing two proportions, for two-way tables, for a single mean, for comparing two independent means, for comparing paired means, and for comparing many means. Regression and inferential modelling: Correlation; simple linear regression models with numerical or categorical predictors; least squares regression; residual analysis; goodness-of-fit; outliers; prediction and extrapolation; inference. All module content is demonstrated and interpreted through practical coding and simulation within a data science framework.
This module is also presented as a summer school for students who initially elected and passed STK 120 or STK 121 or STC 121 with a final mark of at least 60% and then decide to further their studies in Statistics as well as for students who failed STC 122 during semester 2.
Module content:
PART A: Mathematical concepts for the business student: Statistical applications of quantitative techniques. Systems of linear equations: solving and application. Differentiation: Rules and application using the rules. Optimisation, linear functions, non-linear functions, Integration: Rules and application using the rules, Marginal and total functions, Stochastic and
deterministic variables in a statistical and practical context: producers' and consumers' surplus. Linear programming. Matrix algebra. Limits and continuity.
PART B: Descriptive statistics: Sampling and the collection of data; frequency distributions and graphical representations. Descriptive measures of location and dispersion. Probability. Introductory probability theory and theoretical distributions. Statistical and mathematical concepts are demonstrated and interpreted through Excel (practical coding) and simulation within a data science framework.
Exam entrance requires a subminimum of 40% in both Part A and Part B. To pass the module a student has to pass both Part A and Part B.
Module content:
Students can only get credit for one of the following two modules: STK 120 or STK 121 or STC 121. This module is also presented as STK 121/STC 121, an anti-semester module in the first semester. This is a terminating module.
Sampling distributions. Estimation theory, i.e. point estimation and confidence intervals. Hypothesis testing of sampling averages and proportions (one and two-sample cases). Non-parametric methods. Analysis of variance. Categorical data analysis. Curve fitting and regression analysis. The analysis of time series. Statistical concepts are demonstrated and interpreted through Excel (practical coding) and simulation within a data science framework.
Module content:
Students can only get credit for one of the following two modules: STK 120 or STK 121.
Analysis of variance, categorical data analysis, distribution-free methods, curve fitting, regression and correlation, the analysis of time series and indices. Statistical and economic applications of quantitative techniques: Systems of linear equations: solving and application. Optimisation, linear functions, non-linear functions. Marginal and total functions. Stochastic and deterministic variables in statistical and economic context: producers' and consumers' surplus. Supporting mathematical concepts. Statistical concepts are illustrated using simulation within a data science framework.
This is a terminating module.
Minimum credits: 121
Additional information:
In addition to all the compulsory core modules, students are required to choose their electives from what is referred to as an elective group. Once an elective group has been chosen, the modules listed per year level need to be completed to comply with the degree programme's requirements. These elective groups, along with their respective second-year modules are the following:
Computer Auditing: BAC 200 and IAU 200
Entrepreneurship: OBS 210, OBS 220 and OBS 211
eBusiness: OBS 211, OBS 212, OBS 214 and KOB 283
Geography: GGY 283, GIS 220 and GMA 220
eTaxation: BAC 200 and BEL 200
Data Science Management: STK 210, STK 220 and WST 212
Module content:
The Joint Community Project module is a credit-bearing educational experience where students are not only actively engaging in interpersonal skills development but also participate in service activities in collaboration with community partners. Students are given the opportunity to practice and develop their interpersonal skills formally taught in the module by engaging in teamwork with fellow students from different disciplines and also with non-technical members of the community. The module intends for the student to develop through reflection, understanding of their own experience in a team-based workspace as well as a broader understanding of the application of their discipline knowledge and its potential impact in their communities, in this way also enhancing their sense of civic responsibility. Compulsory class attendance 1 week before Semester 1 classes commence.
Module content:
In this module students are equipped with an understanding of the moral issues influencing human agency in economic and political contexts. In particular philosophy equips students with analytical reasoning skills necessary to understand and solve complex moral problems related to economic and political decision making. We demonstrate to students how the most important questions concerning the socio-economic aspects of our lives can be broken down and illuminated through reasoned debate. Examples of themes which may be covered in the module include justice and the common good, a moral consideration of the nature and role of economic markets on society, issues concerning justice and equality, and dilemmas of loyalty. The works of philosophers covered may for instance include that of Aristotle, Locke, Bentham, Mill, Kant, Rawls, Friedman, Nozick, Bernstein, Dworkin, Sandel, Walzer, MacIntyre, Bujo, Wiredu, and Gyekye.
Module content:
Database design: the relational model, structured query language (SQL), entity relationship modelling, normalisation, database development life cycle; practical introduction to database design. Databases: advanced entity relationship modelling and normalisation, object-oriented databases, database development life cycle, advanced practical database design.
Module content:
Database management: transaction management, concurrent processes, recovery, database administration: new developments: distributed databases, client-server databases: practical implementation of databases.
Module content:
Systems analysis. Systems design: construction; application architecture; input design; output design; interface design; internal controls; program design; object design; project management; system implementation; use of computer-aided development tools.
Module content:
To use a conceptual understanding of intermediate foundational knowledge of International Financial Reporting Standards (IFRS) in order to prepare, present and interpret company and basic group company financial statements in a familiar business context and to propose clear solutions with adequate justification to solve financial problems in an ethical manner.
Module content:
To use a conceptual understanding of intermediate foundational knowledge of International Financial Reporting Standards (IFRS) in order to prepare, present and interpret company and basic group company financial statements in a familiar business context and to propose clear solutions with adequate justification to solve financial problems in an ethical manner.
Module content:
This module introduces students to taxation in the context of its history, its basic principles and its interdisciplinary nature as it relates to policy, legislation and governance. It also addresses the inherent demand for ethical and responsible conduct by all tax practitioners/professionals and taxpayers in pursuit of sustainable development in South Africa. The module is principles-based and will enable a student to interpret and apply the fundamental principles and concepts of taxation, specifically related to the Income Tax Act (No. 58 of 1962). In addition, the module will enable a student to interpret and apply specific sections in the Income Tax Act relating to donations and deceased estates.
Module content:
Introduction to Geographic Information Systems (GIS), theoretical concepts and applications of GIS. The focus will be on the GIS process of data input, data analysis, data output and associated technologies. This module provides the foundations for more advanced GIS and Geoinformatics topics. Practical assessments and a mini-project make use of South African and African examples and foster learning and application of concepts aligned to the UN Sustainable Development Goals.
Module content:
The nature of geographical data and measurement.Application of statistics in the geographical domain. Probability, probability distributions and densities, expected values and variances, Central Limit theorem. Sampling techniques. Exploratory data analysis, descriptive statistics, statistical estimation, hypothesis testing, correlation analysis and regression analysis. Examples used throughout the course are drawn from South African and African case studies and taught within the framework of the UN Sustainable Development Goals.
Module content:
This module aims to provide students with a working knowledge and skills to learn methods and techniques for collecting, processing and analysing remotely sensed data. Throughout the module, emphasis will be placed on image processing, image analysis, image classification, remote sensing and applications of remote sensing in geographical analysis and environmental monitoring. The module is composed of lectures, readings, practical exercises research tasks and a project or assignments of at least 64 notional hours. In particular, the practical exercises and research tasks incorporate South African examples using satellite remotely-sensed data, as well as field spectral data measurements, to promote understanding of the state of land cover and land use types (e.g. spanning agricultural resources, water resources, urbanization) and how changes over time could impact on the changing climate in accordance with the United Nation’s Sustainable Development Goals.
Module content:
*Module content will be adapted in accordance with the appropriate degree programme. Only one of KOB 281– 284 may be taken as a module where necessary for a programme.
Applied business communication skills
Acquiring basic business communication skills will enhance the capabilities of employees, managers and leaders in the business environment. An overview of applied skills on the intrapersonal, dyadic, interpersonal, group (team), organisational, public and mass communication contexts is provided. The practical part of the module (for example, the writing of business reports and presentation skills) concentrates on the performance dimensions of these skills as applied to particular professions.
Module content:
Logistics management
The role of logistics in an enterprise; definition and scope of customer service; electronic and other logistics information systems; inventory management; materials management with special reference to Japanese systems; management of the supply chain. Methods of transport and transport costs; types and costs of warehousing; electronic aids in materials handling; cost and price determination of purchases; organising for logistics management; methods for improving logistics performance.
Module content:
Creativity, innovation and identification of opportunities: the role of creativity; techniques to facilitate creativity; barriers to creativity; creative versus critical thinking within the broad business managerial context. Creative problemsolvingand identification of opportunities: identification of opportunities; development of ideas; evaluation and prioritising of ideas, ideation and design thinking. Creativity and its role in design thinking towards facilitating business innovation. Design thinking techniques are applied with an emphasis on customer empathy. Business innovation is translated from the process of design thinking into incremental or disruptive new products, services and or processes. A clear understanding is created with regards to the following elements in business innovation: types and forms; technology waves; models; processes and sources. The management of innovation is also an integral part of the module.
Module content:
Creating a new product, service or process to market. Comprehensive prototype feasibility and business modelling. Designing business models aligned with the market realm. Value-to-customer building and business efficiency development. Translation of business models into bankable business plans.
Module content:
Business ethics; sustainability and the economic system; key social challenges; key environmental challenges; key economic challenges; conventional vs. progressive measure of progress; short-term vs long-term orientation; development as an outcome of growth; sustainable development as opposed to conventional development; sustainable development goals; sustainable development goals and the changing role of business in society; implications for the notion of corporate citizenship; global responses and solutions; local
responses and solutions.
Module content:
Project management and negotiations:
Introduction Project management concepts; needs identification; the project, the project manager and the project team; types of project organisations; project communication and documentation. Planning and control: planning, scheduling and schedule control of projects; resource considerations and allocations; cost planning and performance evaluation.
Negotiation and collective bargaining: The nature of negotiation; preparation for negotiation; negotiating for purposes of climate creation; persuasive communication; handling conflict and aggression; specialised negotiation and collective bargaining in the South African context.
Module content:
Statistical problem solving. Causality, experimental and observational data. Probability theory. Multivariate random variables. Discrete and continuous probability distributions. Stochastic representations. Measures of association. Expected values and conditional expectation. Simulation techniques. Supporting mathematical concepts. Statistical concepts are demonstrated and interpreted through practical coding and simulation within a data science framework.
Module content:
Multivariate probability distributions. Sampling distributions and the central limit theorem. Frequentist and Bayesian inference. Statistical learning and decision theory. Simulation techniques enhancing statistical thinking. Supervised learning: linear regression, estimation and inference. Non-parametric modelling. Supporting mathematical concepts. Statistical algorithms. Statistical concepts are demonstrated and interpreted through practical coding and simulation within a data science framework.
Module content:
Introduction to Databases. Database design and use. Data preparation and extraction: basic SQL queries, SQL joins and subqueries. Statistical modelling using database structures. Aims of data analysis (descriptive, inferential and predictive). Stages of conducting a data analysis to solve real-world problems. Sources and types of data and characteristics of extremely large or complex data sets. Introductory machine learning concepts: bias/variance trade-off, model complexity, cross-validation, regularisation, overfitting/underfitting, precision, recall, F1 score, ROC curve and confusion matrix. Data visualisation, data wrangling, supervised learning (linear, local and logistic regression) and unsupervised learning (k-means clustering). Statistical concepts are demonstrated and interpreted through practical coding and simulation within a data science framework.
Minimum credits: 120
Additional information:
In addition to all the compulsory core modules, students are required to choose their electives from what is referred to as an elective group. Once an elective group has been chosen, the modules listed per year level need to be completed to comply with the degree programme's requirements. These elective groups, along with their respective third-year modules are the following:
Computer Auditing: IAU 300
Entrepreneurship: OBS 310 and OBS 330
eBusiness: OBS 359 and OBS 370
Geography: GIS 310 and GIS 320
eTaxation: BEL 300
Data Science Management: STK 310 and STK 353
Module content:
A review of current trends which are relevant to the application of information systems within a business environment.
Module content:
Application of systems analysis and design in a practical project; programming; use of computer-aided development tools.
Module content:
The purpose of the module is to enable the learner to calculate the value-added tax liability and to journalise transactions; calculate the normal tax liability (including the determination of taxable capital gains and assessed capital losses) of individuals, companies, estates and trusts,discuss tax principles on value-added tax and normal tax; and calculate and discuss provisional and employees' tax and to object against an assessment.
Module content:
Advanced theory and practice of Geographic Information Systems; GIS applications; design and implementation of GIS applications. A project or assignments of at least 64 notional hours. Diverse South African examples will be used to expose the students to various data sources, geospatial analyses, and data representation to support the UN Sustainable Development Goals.
Module content:
Construction of Raster Geovisualisations, spatial model construction and use, multi-criteria decision analysis. Factor analysis: Principle component analysis. Geostatistics: Spatial dependence modelling, ordinary kriging. Markov chains and cellular Automata, combined models. Examples using data from South Africa are implemented. A project or assignment of at least 64 notional hours.
Module content:
General and application information technology controls. The identification of weaknesses, risks, controls and engagement procedures for the human resources and payroll, inventory and bank and cash business processes. Assurance engagements (control, compliance and financial audit engagements). Safety, health and environmental audit engagements. Sustainability assurance engagements. Quantitative techniques, data analytics and computer assisted audit techniques. Risk-based, compliance, operational, forensic and consulting audit engagements. Introduction to the public sector internal audit environment. Corporate Governance, relevant legislation and other guidelines that affect the internal audit profession. Audit communication.
Module content:
Strategy formulation: the deliberate strategy process of formulating a vision and mission statement, conducting internal and external environmental analyses and selecting appropriate strategies. It will enhance an understanding of the level of strategy formulation, gaining competitive advantage in your market place and thinking strategically.
Module content:
Strategy execution: The role of management in strategy implementation; budgets as instrument in the implementation process; leading processes of change within enterprises; supporting policies, procedures and information systems for implementation in the various functional areas; evaluation and control of implementation. South African case studies to create contextual relevance.
Module content:
Introduction to international management
International business management; the process of internationalisation; growth in international trade and investment; the evolution of multinational enterprises; management perspectives on international trade and international trade theories; international trade regulation; economic integration; the formation of trading blocks, and free-trade areas.
The international business environment
The cultural environment of international business; the political and legal environments as well as the economic environment of international business; the international monetary system; the foreign exchange market; and international capital markets.
Module content:
Evaluates how to strategically align, plan for and direct investments in, and governance of, processes for continuous renewal of analytic deployments in business. An overview of analytics in the business context will be provided that will cover: concepts of strategic and operational analytics; overview of concepts like dimensional modeling, the Model Life cycle, data mining, big data, KPIs and metrics, ERP and analytics, in-database/memory analytics; real-time analytics and data stream analysis. The applied decision making aspect will focus on mastering quantitative modeling tools and techniques for business decision-making and deterministic optimisation techniques.
Module content:
Supervised learning. Linear and non-linear regression. Ordinary least squares and maximum likelihood estimation. Violations of the assumptions, residual analysis. Cross validation. Statistical inference. Bootstrap inference. Supporting mathematical concepts. Statistical concepts are demonstrated and interpreted through practical coding and simulation within a data science framework.
Module content:
Introduction to coding: data types, basic arithmetic, logical comparisons, functions, loops, conditional statements, packages. Data exploration and visualisation. Visualisation best practices. Data wrangling: data cleaning, missing values, duplicate data, outliers. Data transformation. Principal component analysis. Statistical coding. Algorithmic thinking. Sampling: basic techniques in probability, non-probability, and resampling methods, Monte Carlo, probability integral transformation, bootstrap method, acceptance/rejection algorithm. Machine learning: train/test split, performance metrics, classification and clustering, performance metrics, cross-validation. Supervised and unsupervised learning: linear regression, decision tree, random forest, naïve Bayes, K-nearest neighbour, hierarchical clustering. Interpretation and communication of results. Text mining and analytics: topic modelling and word embeddings. Statistical concepts are demonstrated and interpreted through practical coding and simulation within a data science framework.
Copyright © University of Pretoria 2025. All rights reserved.
Get Social With Us
Download the UP Mobile App