Bachelor of Science in Data Science

Purpose

The purpose of the Bachelor of Science in Data Science is to develop learners who are able to demonstrate

• An understanding of the basic physical principles as well as the basic statistical concepts and its applications.
• An understanding of the fundamental design, analysis, and implementation of basic data structures and algorithms, the analysis and evaluation of the data structure needs of particular problems, as well as gaining hands-on experience in the design, analysis, and implementation of C programmes by using basic data structures and algorithms.
• A theoretical background and understanding of how computer hardware functions and the competence to relate to his/her computer programme algorithm development and implementation to an efficient and optimal execution of the code in the hardware.
• An understanding of core aspects of information System (IS), focusing on the knowledge, skills and processes involved in developing and/or acquiring information systems.
• A solid understanding of fundamental architectural techniques used to build high-performance processors and systems.

The Bachelor of Science in Data Science has a strong mathematical core and a focus on data science and applications thereof. The Degree is designed to develop highly skilled learners in areas in the field of data science. Learners will be equipped to deal with large data, understand and analyse systems, and have the mathematical and information technology skills to be able to engineer solutions to the analysis, management and manipulation of large data.

Rationale

The introduction of a Bachelor of Science Degree will address a critical skills shortage in the country and will provide access for learners in South Africa to an advanced area of study in a critical contemporary discipline. This qualification in Data Science will ensure vertical articulation possibilities and further encourage the development of academic qualifications in this field. Learners with this qualification may expect to find work in a wide variety of positions, such as data scientists, software engineers, business analysts, and solutions architects. They will be able to work as researchers, statisticians, computer network professionals, network administrators, network analysts, software programmers, systems and intelligence analysts.

In addition, this qualification forms an important part of the evolving Academic Plan of the institution. The academic posture adopted by the institution has been to focus on the unique characteristics and needs of the general Northern Cape region in a manner that raises intellectual matters of local and global interest. The institution is keen to develop capacity for academic engagement in Data Science that is both wide in its reach and deep in the levels of intellectual competence. The qualification will provide access to learners in the Northern Cape to an advanced area of study in a critical contemporary discipline.

Since there are considerable shortages in these skills and competences across the country, learners in possession of The Bachelor of Science in Data Science will thus be both employable and eligible for further study, at honours or a Postgraduate Diploma level.

Recognition of Prior Learning (RPL)

For admission via RPL learners will be required to demonstrate suitability either through work experience and/or other prior learning that has taken place. The institution makes provision for RPL intake, in line with the policies of the institution.

Entry Requirements

The minimum level of learning required for a learner to enter and complete the Bachelor of Science in Data Science successfully is:

• Senior Certificate with endorsement.

Or

• A National Senior Certificate granting access to Degree studies.

Or

• A National Certificate (Vocational) at NQF Level 4 granting access to Degree studies.

The qualification comprises 28 compulsory modules and 1 elective module at NQF Levels 5, 6 and 7, totalling 360 Credits

Modules at NQF Level 5, 68 Credits

• Basic Computer Organisation, 10 Credits.
• Statistics I, 10 Credits.
• Algebra I, 10 Credits.
• Calculus I, 10 Credits.
• Undergrad Core Curriculum, 8 Credits.
• Physics for Engineers, 10 Credits.
• Applied Mathematics for Computing, 10 Credits.

Modules at NQF Level 6, 144 Credits

• Introduction to Algorithms and Programming, 12 Credits.
• Introduction to Data Structures and Algorithms, 12 Credits.
• Introduction to Object Oriented Programming, 12 Credits.
• Discrete Mathematics, 12 Credits.
• Operating Systems (Theory and Practice), 12 Credits.
• Applications and Analysis of Algorithms, 12 Credits.
• Introduction to Data Science, 12 Credits.
• Information Systems Engineering and Design IIA, 12 Credits.
• Electric Circuits and Electronics, 12 Credits.
• Statistics II, 12 Credits.
• Information Systems IIB, 12 Credits.
• Mobile Computing, 12 Credits.

Modules at NQF Level 7, 136 Credits

• Linear Algebra and Operations Research, 12 Credits.
• Data Science II, 12 Credits.
• Professional Practice and Ethics in Software Development, 12 Credits.
• Computer Architecture, 12 Credits.
• Statistics III, 12 Credits.
• Computer Networks, 12 Credits.
• Machine Learning, 12 Credits.
• Capstone Project, 40 Credits.
• Signals and Systems for Applied Computing, 12 Credits.

Elective Modules at NQF Level 7, (choose one) 12 Credits

• Artificial Intelligence, 12 Credits.

Or

• Microprocessors, 12 Credits.

1. Develop an understanding and apply the basic physical principles as well as the basic statistical concepts.
2. An understanding of the fundamental design, analysis, and implementation of basic data structures and algorithms, the analysis and evaluation of the data structure needs of particular problems, as well as gaining hands-on experience in the design, analysis, and implementation of C programmes by using basic data structures and algorithms.
3. Have a theoretical background and understanding of how computer hardware functions and the competence to relate to his/her computer programme algorithm development and implementation of an efficient and optimal execution of the code in the hardware.
4. Explore techniques of designing, analysing and implementing algorithms by using graph algorithms as a case study.
5. An understanding of core aspects of IS, focusing on the knowledge, skills and processes involved in developing and/or acquiring information systems.
6. A solid understanding of fundamental architectural techniques used to build high-performance processors and systems.

Associated Assessment Criteria for Exit Level Outcome 1

• The essential factors in a physical system and of the physical principles or processes that are involved are recognised.
• Concepts and laws are defined, interpreted and applied.
• Problems presented in verbal, numerical or diagrammatic form, and know how to proceed are understood.
• A strategic problem-solving approach appropriate to each problem is developed and executed.
• Appropriate techniques, such as diagrams depicting the key features of a problem are selected and applied.
• The appropriate equations are formulated and the mathematical solution is performed.
• Numerical results with appropriate significant figures and units are presented; working by orders of magnitude, physical reasoning, and dimensional analysis are checked.

Associated Assessment Criteria for Exit Level Outcome 2

• Advanced C programming techniques such as pointers, dynamic memory allocation and structures to develop solutions for particular problems are applied.
• Using C as the programming language for abstract data types such as linked list, stack, queue and tree are implemented and designed by using static or dynamic implementations.
• Appropriate abstract data types and algorithms are analysed, evaluated and selected to solve particular problems.
• Fundamental building blocks of algorithms (sequence, selection, repetition, abstraction) are applied for developing solutions.
• Some fundamental algorithms are recalled and algorithmic methods are transferred and skills are applied to new problems.
• Simple problems are analysed, and algorithms are constructed for their solution.
• Simple algorithms are translated into working C programmes, and efficient use of an integrated development environment is made.

Associated Assessment Criteria for Exit Level Outcome 3

• The functional units of a basic computer is identified.
• Statements expressed in natural language into formal descriptions using Boolean algebra are translated.
• Binary arithmetic is performed, truth table and design logic circuits are constructed.
• Simple machine learning programmes are analysed and the output is traced.

Associated Assessment Criteria for Exit Level Outcome 4

• What is meant by "best", "expected", and "worst" case behaviour of an algorithm is explained.
• In the context of specific algorithms, the characteristics of data and/or other conditions or assumptions that lead to different behaviours are identified.
• The time and space complexity of simple algorithms are informally determined.
• Standard complexity classes are listed and contrasted.
• Empirical studies are performed to validate hypotheses about runtime stemming from mathematical analysis.

Associated Assessment Criteria for Exit Level Outcome 5

• An understanding of the Design and Implementation phases of the Systems Development Life cycle through both written assessment and project work are demonstrated.
• An information systems project are designed and implemented to specified requirements.
• The project management issues surrounding an information systems project are designed and implemented to specifications.
• An understanding of the importance of interacting with and satisfying the end users of an information system is demonstrated.
• Using a professional drawing tool (such as Microsoft Visio) is demonstrated to represent requirements and logical models.
• Databases using Microsoft SQL Server, allowing for the capture, management and use of data using SQL queries are designed and implemented.
• The importance of report design in information systems is designed and implemented.

Associated Assessment Criteria for Exit Level Outcome 6

• The overall machine architecture is described.
• The design of a pipelined CPU and cache hierarchy is understood.
• The CPU and memory hierarchy performance is analysed and evaluated.
• Trade-offs in modern CPU design including issues affecting superscalar and dynamically scheduled architectures are understood.
• A network and its components are described and between different types of networks are differentiated.
• Different parameters of networks are used to model and solve network problems.
• Exchange of information/messages over a network are performed using basic protocol commands of well know network protocols.
• The working mechanism of DNS which is at the core of internet addressing is performed.
• Different variants of Reliable Data Transfer Protocols with understanding of associated pros and cons are performed.
• The working mechanism and differences of connection-less (UDP) and connection-oriented (TCP) transport layer protocols are described.

Integrated Assessment

Quality assessment is central to credible certification and recognition of leaner achievement. The institution will ensure credibility in assessment through the application of clear and rigorous procedures and practices, in keeping with the principles of fairness, validity, reliability and practicability.

Integrated assessment is used extensively across the qualification, including in the Work Integrated Learning. Self and formative assessment takes place in various ways in the face to face context, including classroom activities, assignments, and written work. Summative assessments are integrated into the learning in that they take place at the end of each of the constituent modules of the qualification.