Bachelor of Science in Computer Science

Purpose

The Bachelor of Science in Computer Science has as aim to train computer professionals who show a fundamental theoretical understanding of the complex world of integrated digital platforms, and who can translate that understanding into solutions to difficult theoretical and practical problems in the industry.

The context is therefore to provide highly skilled professionals, with ample career opportunities, on a national level. The expected knowledge and skills surpass a simple background of technical knowledge of specific products or programming environments. Indeed, it is the analysis of a problem in the context of theoretical knowledge, the design of a solution, and the practical implementation of the design, in a cyclic fashion, which forms the backbone of the module structures.

As a concerted effort to provide a solid theoretical foundation, the mathematical background is provided through mathematically based courses throughout the three years of the qualification (Mathematics, Mathematical Statistics). On the other hand, the application and practical programming aspects are covered extensively in the Computer Science, Applied Mathematics and Operations Research modules. The provision of mathematical foundations in pure mathematics courses allows for more in-depth coverage of computer-specific topics.

There are four possible sample curricula. Here, learners may concentrate on theory, or hardware design, or software engineering, or data science. However, in all four sample curricula, the foundational theory and practical programming aspects are embodied in the core computer science subjects. The selection of electives provides for broader exposure to different fields but was chosen to be relevant for specific directions in a later postgraduate qualification or possible career.

Rationale

Internationally and nationally there is a dire shortage of skilled computer professionals. The establishment of this degree would address this shortage.

This qualification addresses the needs of the profession by allowing more computer science content in the undergraduate curriculum.

There is no single recognised professional body for computer scientists. However, Computer Science at the institution has strong ties with industry, and various industry role-players were consulted on their views. Without exception, the companies indicated that more Computer Science content would be advantageous to them in terms of hiring graduates.

The qualification addresses a subsection of typical learners, namely, those that are mathematically strong and would be able to progress to theoretically-oriented postgraduate studies. The new intended qualification creates an additional pathway for technically-oriented learners that are not necessarily mathematically inclined, but could easily progress to technically-oriented postgraduate studies.

This qualification forms a strong but wider curriculum for learners who intend to obtain a qualification that is highly desirable and will lead to many job opportunities. As the economy requires a large contingent of trained computer professionals, the advantages are immediate and clear.

Recognition of Prior Learning (RPL)

Learners can specifically apply to be accepted to the qualification by the RPL or CAT route. The Science Faculty's Academic Committee will consider all such applications and weigh the formal (CAT) and non-formal or informal learning (RPL) against the knowledge required to complete the requirements of the BSc in Computer Science. This selection will be performed following the CAT and RPL rules accepted by the Council of Stellenbosch University, and according to the rules of the Faculty of Science.

In particular, RPL will be considered for learners who are returning to studies after spending time in the workplace in a similar field.

Entry Requirements

The minimum entry requirement for this qualification is

• National Senior Certificate, NQF Level 4 granting access to Bachelor studies.

Or

• Senior Certificate, NQF Level 4 with endorsement.

This qualification consists of the following compulsory modules at National Qualifications Framework Level 6 and 7 totalling between 376 to 440 Credits.

Focal Area 1: Theoretical Computer Science Modules

Compulsory Modules, Level 6, 172 Credits.

• Computer Science, 64 Credits.
• Science in Context, 12 Credits.
• Mathematics, 48 Credits.
• Probability Theory, 16 Credits.
• Operations Research, 32 Credits.

Elective Modules, Level 6: Select any of the listed modules totalling a minimum of 80 Credits and a maximum of 96 Credits:

• Physics 114 (16 Credits); 144 (16 Credits); 214 (16 Credits); 224 (16 Credits); 244 (16 Credits); 254 (16 Credits).
• General Linguistics, 24 Credits, General Linguistics 178 (24 Credits); 278 (32 Credits).
• Applied Mathematics 144 (16 Credits); 214 (16 Credits); 244 (16 Credits).
• Mathematical Statistics 214 (16 Credits); 245 (16 Credits); 246 (32 Credits).
• Mathematics 214(16 Credits); 244 (16 Credits); 278 (32 Credits).

Compulsory Modules, Level 7, 96 Credits

• Computer Science, 80 Credits.
• Applied Mathematics, 16 Credits.

Elective Modules, Level 7: Select any of the listed modules totalling 32 Credits

• Computer Science, 16 Credits.
• Mathematical Statistics 312 (16 Credits); 316 (16 Credits); 344 (16 Credits); 354 (16 Credits).
• Mathematics 314 (16 Credits); 324 (16 Credits); 325 (16 Credits); 344 (16 Credits); 345 (16 Credits); 365 (16 Credits); 378 (32 Credits).
• Operations Research 314 (16 Credits); 324 (16 Credits); 344 (16 Credits); 354 (16 Credits).
• Applied Mathematics 324 (16 Credits); 354 (16 Credits); 364 (16 Credits).
• Physics 314 (16 Credits); 334 (16 Credits); Physics 384 (16 Credits).
• General Linguistics 378 (32 Credits).

Focal Area 2: Software Engineering

Compulsory Modules Level 6, 176 Credits

• Computer Science 64 Credits.
• Science in Context, 16 Credits.
• Mathematics 48 Credits.
• Probability Theory and Statistics, 16 Credits.
• Operations Research 32 Credits.

Elective Modules, Level 6: Select any of the listed modules totalling a minimum of 100 Credits and a maximum of 112 Credits:

• Biology 114 (16 Credits); 144 (16 Credits).
• Chemistry 124 (16 Credits); 144 (16 Credits); 234 (16 Credits); 254 (16 Credits).
• Economics 114 (16 Credits); 144 (16 Credits); 214 (16 Credits); 244 (16 Credits).
• Geo-environmental Science 124 (16 Credits); 154 (16 Credits).
• Music Technology 112 (6 Credits), 142 (6 Credits); 222 (8 Credits), 252 (8 Credts).
• Physics 114 (16 Credits); 144 (16 Credits); 214 (16 Credits); 224 (16 Credits); 244 (16 Credits); 254 (16 Credits).
• General Linguistics 178 (24 Credits), General Linguistics 278 (32 Credits).
• Mathematics 154 (16 Credits).
• Applied Mathematics 214 (16 Credits); 244 (16 Credits).
• Mathematical Statistics 214 (16 Credits); 245 (8 Credits), 246 (8 Credits).
• Mathematics 214 (16 Credits); 244 (16 Credits); 278 (16 Credits).
• Genetics 214 (16 Credits); 244 (16 Credits).
• Geography and Environmental Studies 214 (16 Credits).
• Geographical Information Technology 211 (16 Credits); 241 (16 Credits); 242 (16 Credits).

Compulsory Modules, Level 7, 80 Credits

• Computer Science, 80 Credits.

Elective Modules, Level 7: Select any of the listed modules totalling 32 Credits.

• Applied Maths 314(16 Credits); 324 (16 Credits); 364 (16 Credits).
• Mathematical Statistics 312 (16 Credits); 316 (16 Credits); 344 (16 Credits); 354(16 Credits).
• Mathematics 314 (16 Credits); 324 (16 Credits); 325 (16 Credits); 344 (16 Credits); 345 (16 Credits); 365 (16 Credits); 378 (32 Credits).
• Chemistry 324 (16 Credits); 364 (16 Credits).
• Computer Science 315 (16 Credits); 345(16 Credits).
• Economics 318 (24 Credits); 348 (24 Credits).
• Genetics 314 (16 Credits); 324 (16 Credits); 344 (16 Credits); 354(16 Credits).
• Geographical Information Technology 311 (16 Credits); 312 (16 Credits); 341 (16 Credits); 342 (16 Credits).
• Music Technology 379 (48 Credits).
• Physics 314 (16 Credits); 334 (16 Credits), 384 (16 Credits).
• General Linguistics 379 (48 Credits).
• Operations Research 314 (16 Credits); 324 (16 Credits); 344 (16 Credits); 354 (16 Credits).

Focal Area 3: Computer Engineering

Compulsory Modules, Level 6, 231 Credits

• Computer Science, 48 Credits.
• Science in Context, 12 Credits.
• Mathematics, 64 Credits.
• Probability Theory and Statistics, 16 Credits.
• Electrotechnique, 15 credits.
• Computer Science, 16 Credits.
• Computer Systems, 30 Credits.
• Systems and Signals, 30 Credits.

Elective Modules, Level 6: Select any of the listed modules totalling a minimum of 32 Credits and a maximum of 64 Credits:

• Mathematics, 154 (16 Credits).
• Applied Mathematics 144 (16 Credits); 214 (16 Credits); 244 (16 Credits).
• Physics 114 (16 Credits), 144 (16 Credits).
• Operations Research 214 (16 Credits); 244 (16 Credits).
• Physics 214 (16 Credits); 224 (16 Credits).

Compulsory Modules, Level 7, 126 Credits

• Computer Science, 96 Credits.
• Systems and Signals, 30 Credits.

Electives Modules, Level 7: Select any of the listed modules for 16 Credits

• Applied Maths 314 (16 Credits); 324 (16 Credits); 354 (16 Credits); 364 (16 Credits).
• Mathematics 314 (16 Credits); 324 (16 Credits); 325 (16 Credits); 344 (16 Credits); 345 (16 Credits); 365 (16 Credits).
• Computer Science 315 (16 Credits).

Focal Area 4: Data Science

Compulsory Modules, Level 6 (216 Credits)

• Computer Science, 48 Credits.
• Science in Context, 12 Credits.
• Mathematics, 80 Credits.
• Probability Theory and Statistics, 16 Credits.
• Computer Systems, 30 Credits.
• Systems and Signals, 30 Credits.

Elective Modules, Level 6: Select any of the listed modules totalling a minimum of 32 Credits and a maximum of 64 Credits:

• Physics 114 (16 Credits); 144 (16 Credits).
• General Linguistics 178 (24 Credits).
• Applied Mathematics 144 (16 Credits).
• Mathematics 214 (16 Credits); 244 (16 Credits).
• Applied Mathematics 214 (16 Credits); 244 (16 Credits).

Compulsory Modules, Level 7, 80 Credits

• Computer Science, 80 Credits.

Elective Modules, Level 7: Select any of the listed modules totalling 48 Credits

• Operations Research 314 (16 Credits); 324 (16 Credits); 344 (16 Credits); 354 (16 Credits).
• Mathematical Statistics 312 (16 Credits); 316 (16 Credits); 344 (16 Credits); 354 (16 Credits).
• Applied Mathematics 314 (16 Credits); 324 (16 Credits); 354 (16 Credits); 364 (16 Credits).

1. Demonstrate integrated knowledge and an understanding of the fundamental concepts and principles of the specific subject field of the chosen curricula.
2. Recognise and explore a range of methods of inquiry in the selected field.
3. Identify, analyse, evaluate and critically reflect on and address complex problems, current theories, models and techniques, applying evidence-based solutions and theory-driven arguments.
4. Make informed decisions on effective solutions/methods to solve a problem, implement the method efficiently and interpret the results in a meaningful way.
5. Demonstrate an understanding of the building blocks of the web.
6. Work effectively alone and as a member of a team to develop quality software.
7. Communicate scientific understanding in writing, orally and/or other forms of representation.
8. Demonstrate the ability to design appropriate solutions in one or more application domains using software engineering approaches that integrate ethical, social, legal, and economic concerns.
9. Demonstrate an understanding and appreciation for the importance of negotiation, effective work habits, leadership, and good communication with stakeholders in a typical software development environment.

Associated Assessment Criteria for Exit Level Outcome 1

• Apply modelling principles and mathematical.
• Be familiar with the use of vectors and vector operations, with particular application to mechanical systems.
• Develop problem-solving skills and honed mathematical tools to be able to solve more complex problems.
• Discuss the fundamental concepts of problems and algorithms, without restrictions to specific computers, languages or implementations.
• Discuss the concepts of automata, regularity, context-free, computability, decidability, nondeterminism, NP-completeness and other related concepts.
• Discuss and explain the boundaries for computability.

Associated Assessment Criteria for Exit Level Outcome 2

• Organise tasks and data to choose the correct algorithm structure and supporting structure for implementation.
• Understand performance analysis which includes topics such as efficiency, speedup, overheads analysis and scalability.
• Understand the operating system's responsibility with regards to managing processes, including processes, including process creation, scheduling, and synchronisation; compute with matrices and vectors.
• Apply matrix theory to solve a variety of practical problems (Examples are: solution of a system of linear equations, both over and underdetermined, solution of systems of difference and differential equations, handling of projections, reflections and rotations in 2D and 3D, as well as applications in image processing), and use MATLAB in these activities.
• Discuss the fundamental concepts of problems and algorithms, without restrictions to specific computers, languages or implementations.
• Discuss the concepts of automata, regularity, context-free, computability, decidability, nondeterminism, NP-completeness and other related concepts.
• Discuss and explain the boundaries for computability.

Associated Assessment Criteria for Exit Level Outcome 3

• Construct a mathematical model of the problem.
• Concrete and abstract problems, in familiar and unfamiliar contexts, are formulated, analysed and solved.
• Visualize vectors, lines and planes in 2 and 3.
• Dimensions so that mathematical results can also be interpreted physically.
• Solve practical problems by suitable automata-based programming methodologies.
• Formulate algorithmic solutions.
• Design effective algorithms, regardless of a specific computer, language or implementation.
• Use decomposition, ordering and mapping techniques to identify concurrency.
• Organise tasks and data to choose the correct algorithm structure and supporting structure for implementation.

Associated Assessment Criteria for Exit Level Outcome 4

• Analyse a model, either analytically or by the computer.
• Interpret the solution in terms of the original problem.
• Test solutions against experimental data.
• Formulate algorithmic solutions using stacks, queues, symbol tables, graphs.
• Understand the space and time requirements for various implementations.
• Implement stacks, queues, symbol tables, graphs and know how to use various implementations of these abstract data types that are available in the standard java library.
• Make an informed decision as to which computer method is most effective for the problem.
• Implement the chosen method efficiently on a computer, taking into account the speed at which it executes as well as numerical stability.
• Interpret the results in a meaningful way and, if necessary, improve the algorithm and/or implementation.

Associated Assessment Criteria for Exit Level Outcome 5

• Design and develop web user front-end.
• Design and develop web server back-ends.
• Understand the building of scalable solutions.
• Know how to effectively test a website.

Associated Assessment Criteria for Exit Level Outcome 6

• Reconcile conflicting project objectives, finding acceptable compromises within limitations of cost, time, knowledge, existing systems and organisations.
• Collaboration with other team members to plan and solve complex problems in a group.

Associated Assessment Criteria for Exit Level Outcome 7

• Scientific language is used correctly to produce clear and coherent written documents.
• Communicate findings in written and oral form.
• Scientific information is presented verbally in front of others.
• Appropriate referencing conventions are used and intellectual property is respected.
• Non-verbal forms of representations are used correctly and appropriately.
• Communicate with self-confidence on problems that arise from the human-environment interaction.
• Oral presentations on own work or sections of the prescribed material.
• Create electronic presentations using appropriate software.

Associated Assessment Criteria for Exit Level Outcome 8

• Design production systems through applying the scientific method.
• Design effective algorithms, regardless of a specific computer, language or implementation.
• Plan and develop web user front-end.
• Design and develop web server back-ends.

Associated Assessment Criteria for Exit Level Outcome 9

• Learn new models/techniques/technologies as they emerge and appreciate the necessity of ongoing professional development.
• Communicate with self-confidence on problems that arise from the human-environment interaction.

Integrated Assessment

The number and types of assessments may vary between modules. However, each module follows the policies and rules of assessment of the university. The two systems are flexible assessment or examination.

Formative and summative assessment: The practicals and tutorials serve as the formative assessment components of each module, while the tests and examinations serve as the summative assessment component.

Internal and external moderation of assessments: According to the institution's policy and faculty guidelines, the assessments for all undergraduate modules have to be internally moderated, and the assessments for all exit level (third year) modules have to be internally and externally moderated.

Experiential learning: Kolb's model of experiential learning [1] moves in a cycle, through active experimentation to concrete experience to reflective observation to abstract conceptualization, and back. In particular, for all Computer Science modules, the required practical programming component matches this model exactly. Other subjects in the Science Faculty follow this model as well, based on tutorial sessions where practical problems are presented, and learners work with support to solve these and hence cements the abstract concepts involved.