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Faculty of Natural and Applied Sciences Programmes

Period of study: 3 Years

The pervasive use of computers has resulted in an ongoing need for a wide range of highly skilled human capacity in ICT disciplines. The field is subject to rapid technological changes and therefore requires professionals who are well-educated, who keep up-to-date with the latest developments in the field and who can engage future trends and developments in the field.

The purpose of this Diploma qualification is to provide a career-focussed, professional qualification featuring industry-referenced knowledge and skills transfer, technological competencies, critical cross-field skills as well as attitudinal development.

The programme deals with the conceptualisation, design, development, implementation and testing of computer software applications to meet the enormous commercial and social demand for such products.

Students are prepared for careers in Computer Programming and Systems Analysis & Design but will gain exposure to a wide range of related topics.

The diploma graduate will be knowledgeable and competent in the discourse and practice of the ICT discipline and have specialist knowledge of the Applications Development sub-discipline of ICT.

The curriculum features a range of professional and personal development initiatives relevant to the ICT industry and the promotion of lifelong learning skills.

Qualifying students will therefore be proficient in the general discourse of ICT but have specialised knowledge and skills that are focused on Applications Development.

The programme is designed to promote initiative and responsibility in an academic and/or professional context to respond to the local, regional and national, community and industry needs.

Diploma graduates in ICT specialising in Applications Development are able to:

Create and modify general computer applications software or specialised utility programs for stand-alone and distributed environments including the web environment,

Analyse user needs and produce a suitable specification for a system, covering a medium sized business problem,

Develop computer programs to demonstrate the ability to store, locate and retrieve information from different data sources, and

Deploy a suitable, secure software solution based on a given scenario demonstrating an understanding of systems architectures.

ICT practitioners are generally required to collaborate in teams to accomplish a common goal by integrating personal initiative and group cooperation around ICT projects.

The course therefore also focuses on the development of professional communication skills which prepares students to work both independently and as part of a team.

Strong emphasis is placed on creative problem solving and structured project management approaches.

The dynamic nature of the ICT industry demands that practitioners remain aware of developments and continuously update their skills. Practitioners must be au fait with the best practices and benchmark standards of the ICT industry.

The programme therefore focuses on developing the lifelong learning skills of students.

Security of information has become a key concern in the modern business environment.

As future ICT practitioners, students will therefore be exposed to uncompromising approaches in the ethical practice of their profession.

Applicants to the Diploma in ICT qualification must have a Matric with reasonably strong First Language (and English) and Mathematics or Mathematics Literacy marks.

It is highly recommended that applicants should also have Computer Applications Technology (CAT) or Information Technology (IT) as subjects in their Matric curriculum.

Career Opportunities:

An exciting career awaits those who successfully complete the Diploma in ICT specialised in Applications Development qualification. 

A world without ICT and software application is unimaginable! Applications Development manifests in every facet of life – mobile phone apps, ATMs & banking, mining, television, medical equipment, agriculture, finance & accounting, education, geographical information systems, film & video, motorcars and aeroplanes, toys, social media & the internet. The list is endless.

In South Africa and across the globe, ICT is regarded as a scarce skill and opportunity awaits those who have the right knowledge, skills and attitude.

For more information, contact the Faculty Registrar:
Ms Nobulali Mathimba
Telephone (053) 491 0369
Email nobulali.mathimba@spu.ac.za

Period of study: 3 Years

Diploma in Agriculture (Qualification Code: AGR600)

Diploma in Agriculture has been tailored to address agricultural sector’s skill shortage in meeting food security demands in Northern Cape and South Africa. The Diploma is designed to offer more practical and relevant skill set that are required in Agriculture.


The purpose of this programme is to produce students with a solid grounding in principles and practices of producing crops and livestock for commercial purposes, primarily, under water-stressed regions. The programme is structured as a comprehensive introduction to Mixed Farming which permits graduates to emerge as general agriculturalist who is well equipped to become agricultural entrepreneurs or enter postgraduate studies in any sub-disciplines in agriculture. The university approach ensures that students do not specialise too early in their academic orientation but have sufficient foundational knowledge to develop as specialists through practice and/or further studies. Diploma graduates in Agriculture can emerge as general agriculturalist who would be well equipped to become agricultural entrepreneurs or enter postgraduate studies in any sub-discipline in agriculture.


Furthermore, the programme aims to produce graduates that will demonstrate an in-depth understanding of different form of knowledge, Facultys of thoughts and forms of explanation within agricultural development, agricultural entrepreneurship, practices and awareness of processes involved in the production of knowledge in agricultural field. Equip graduates with agricultural problem-solving skills that will enable them to identify, analyse and evaluate appropriate agricultural methods to be applied as solutions based on evidence and sound procedure that are used in agriculture.

Minimum Admission Requirements


(a) NSC pass with Diploma endorsement.
(b) English Home Language: NSC Level 4; or English 1st Additional Language: NSC Level 5.
(c) Mathematics: NSC Level 3; or Mathematical Literacy: NSC Level 5
(d) Physical Sciences at level 3
(e) Life Sciences or Agricultural Sciences at level 3.
(f) Admission Points Score: (APS): Minimum 25 points.


Career Opportunities


Upon completion, graduates have various career paths that exist in agricultural research, extension, farming production, farmer enterprises and marketing. The programme will produce competent agricultural entrepreneurs, technicians, farm managers and agricultural advisors who can effectively manage agricultural production units.

Period of study: 1 Year

The rationale of the qualification is to produce graduates that have a good theoretical knowledge and practical skills of systems analysis, design and applications development. The course is designed to enable a student to, not only apply the concepts and skills of the specialization sufficiently for a smooth transition into the work place, but also to solve complex problems adapting to the requirements of the organization’s environment. The Advanced Diploma is intended to deepen the professional knowledge, practice and attitudinal skills development of students and facilitate the access to higher level qualifications such as the postgraduate diploma, masters and doctoral studies. In addition, this qualification provides learners with continuing specialized learning in their chosen career. ICT as a pervasive discipline is subject to global market forces and the curriculum needs to be responsive to all disruptive innovations that present unique challenges to the related business environment.

The field of Applications Development is one where changes occur at a rapid pace and there is always a constant demand for skilled personnel. Therefore, this offering enables graduates holding the Diploma in ICT in Applications Development to further their learning as they progress in their chosen careers.  The focus of this offering is to provide advanced technical skills in Application Development, as well as to cater for the professional development. The Advanced Diploma is built on the foundations laid by the Diploma in Applications Development and advances the applied and general skills through the core subjects Software Engineering, Development Software, and the Project offerings.

 Admission Requirements

Applicants must be in possession of a three-year Diploma in Information and Communication Technology in Applications Development or equivalent qualification at NQF Level 6 within the same field of study.

An average of at least 60% in the third year exit modules of the NQF Level 6 qualification.

The formal University’s Recognition of Prior Learning (RPL) Policy may be applied in instances where applicants do not meet the minimum admission requirements for entry into the Advanced Diploma qualification.

Programme Structure

The study duration of the Advanced Diploma is minimum one year of full-time study or two years of part-time study.

 In order to satisfy the qualification requirements, students must take and pass at least 120 credits.

The Advanced Diploma comprises of seven compulsory modules.

Career Opportunities

An exciting career awaits those who successfully complete the Diploma in ICT specialised in Applications Development qualification.

A world without ICT and software application is unimaginable! Applications Development manifests in every facet of life – mobile phone apps, ATMs & banking, mining, television, medical equipment, agriculture, finance & accounting, education, geographical information systems, film & video, motorcars and aeroplanes, toys, social media & the internet. The list is endless.

In South Africa and across the globe, ICT is regarded as a scarce skill and opportunity awaits those who have the right knowledge, skills and attitude.

For more information, contact the Faculty Registrar:
Ms Nobulali Mathimba
Telephone (053) 491 0369
Email nobulali.mathimba@spu.ac.za

Period of study: 3 Years

The Bachelor of Science degree (BSc) has been carefully designed to address a critical skills shortage in the country and will provide access to students in the Northern Cape to an advanced area of study in a critical contemporary discipline. The aim of the programme is to:

  • produce science graduates who have: a systematic and coherent body of knowledge and an understanding of underlying concepts and principles; the ability to access and evaluate scientific information including knowing how scientific knowledge is created; a high level of cognitive and other generic skills including problem-solving, written and spoken communication and computer literacy; and competence in applying knowledge through basic research methods and
  • Provide every graduate with a sufficient depth of knowledge and skills that give opportunities for continued personal intellectual growth, including postgraduate study, for gainful economic activity in a range of careers, and for rewarding and constructive contributions to
  • Provide society with science graduates who demonstrate initiative and responsibility, who are professional and ethical in their roles within the economy and society, and who are able to be intellectual leaders within their societ
  • Produce graduates in all scientific fields, in order to increase, widen and transform the leadership base in South Africa, both for innovation and science-based economic and research development, and for the education of future generations of scientists, technologists, engineers and other professional

Admission Requirements

(a) First time entering students who passed Grade 12 with a National Senior Certificate (NSC) and admission requirements for the Bachelor’s Degree, will be admitted into a BSc programme in the School only if s/he has a rating of at least 30 APS (Admission Point Score).

(b) First time entering students with a Matriculation exemption obtained prior to 2008 will be admitted into the BSc programme in the School only if they obtain a minimum APS of 30 as calculated by converting the obtained grade symbols to APS.

(c) In order to be considered for selection into a programme, an applicant is required to comply with the programme’s minimum admission criteria in respect of the total APS. Meeting the School’s minimum requirements for a particular programme does not necessarily guarantee admission to that programme as specific selection criteria may be applied.

Programme Structure

The programme is offered in three specific sub-field specializations namely;

  • Biological Sciences,
  • Mathematical and Computer Sciences, and
  • Physical Sciences.

Students will enroll into one of the sub-field specialization as listed above.

BSc (Biological Sciences) (Qualification Code: NBSC707)

  • Botany and Zoology are compulsory major subjects.
  • Chemistry and Geography are electives at second year level.

BSc (Mathematical and Computer Sciences) (NBSC705)

  • Applied Mathematics, Computer Science, Mathematics and Statistics are compulsory subjects in this sub-field.
  • A combination of any two of the subjects in (a) above may be taken as majors for the qualification.

BSc (Physical Sciences) (Chemistry: NBSC761)

  • Chemistry, Mathematics and Physics are compulsory subjects at first year level.
  • Statistics is an elective module in year one only.
  • Chemistry is a compulsory major subject.
  • Any two subjects from Botany, Geography, Mathematics and Zoology can be selected to complete the set of modules to be taken in second year.

BSc (Physical Sciences) (Physics: NBSC762)

  • Chemistry, Mathematics and Physics are compulsory subjects at first year level.
  • Because Physics is a compulsory major subject, Mathematics is also compulsory at second year level.
  • Either Chemistry, Computer Science or Geography can be selected to complete the set of modules in second year.

The following tables detail the structure of the BSc Programme in terms of specialization.

For more information, contact the Faculty Registrar:
Ms Nobulali Mathimba
Telephone (053) 491 0369
Email nobulali.mathimba@spu.ac.za

Period of study: 3 Years

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.

It focuses on finding solutions to solving big data problems.

This degree has been designed to address a critical skills shortage in modern applications requiring large-scale data analysis.

The Bachelor of Science in Data Science degree has a strong mathematics, statistics and computers science core. The degree is designed to develop highly skilled graduates in areas in which there are considerable shortages across the country.

Data scientists are a new breed of analytical data experts with technical skills to solve complex problems and the curiosity to explore what problems need to be solved.

Graduates in possession of this degree are highly employable and eligible for postgraduate studies whether it be in the Honours, Masters or PhD programmes.

Data Science is a field in high demand in various industry sectors. Big global companies such as Amazon, Netflix, Facebook, Twitter, Microsoft, Uber and local corporations such as FNB, ABSA, Vodacom, MTN, Accenture, Telkom and Tracker require a skilled data scientists to drive insightful business decisions. 

These are benefits of studying and becoming a Data Scientist:

Data Science is an in-demand and the fastest growing occupation that is required by many industry companies. It is thus a highly employable job sector.

There are very few people who have the required skill set to become Data Scientists which makes it less saturated compared to other IT sectors.

It is one of the most highly paid jobs.

It is a versatile field and therefore provides you with the opportunity to work in various fields.

Minimum Requirements

-English Home Language: NSC Level 4 OR English First Additional Language: NSC Level 5

– Mathematics: NSC Level 5

-APS: 30 Points

Subject Content 

Students will complete the following subjects:

Year 1

Basic Computer Organisation and Architecture, Introduction to Statistics, Calculus, Introduction to Algorithms and Programming, Data Structures and Algorithms, Algebra, Data Science I, Introduction to Numerical methods and mathematical modelling, Probability Theory

Year 2

Operating Systems and Computer Networks, Data Science 2A: Data Analysis and Visualisation, Advanced calculus, Discrete Mathematics, Statistical Inference, Data Science 2B: Large scale Data analysis and visualisation, Applications and Analysis of Algorithms, Database Systems, Linear Algebra, Linear Programming

Year 3

Data Security, Signal and Image processing, Multivariate Statistics, Formal language and automata, Machine Learning, Advanced algorithm analysis, Data Science III: Simulation and Modelling, Capstone Project

Career Opportunities

Science graduates are open to various career opportunities in academic, Government and industry.

Jobs are available in research and development, chemical analysis, quality control, and environmental monitoring among others. Medical and forensic laboratories, public institutions such as government departments, and in teaching profession in secondary schools and higher education institutions.

For more information, contact the School Registrar:
Ms Nobulali Mathimba
Telephone (053) 491 0369
Email nobulali.mathimba@spu.ac.za

Period of study: 1 Years

BSc Honours in Computer Science programme supports the development of research skills and creative interventions in the fields of software engineering, design and analyses of algorithms, security and cryptography, and machine learning. Excellency in these fields is heavily integrated in the development of most largescale enterprise systems in demand today. It is, thus, important that more computer scientists be trained by way of a BSc Honours in Computer Science.

Admission Requirements 

  • The General Rules of Sol Plaatje University in respect of admission to Bachelor Honours Degrees (aligned with the Higher Education Qualification Sub-Framework: HEQSF) are applicable to this degree.
  • To be admitted to the Bachelor of Science (Honours) programme, a student must be in possession of an acknowledged Bachelor qualification at NQF Level 7 or cognate qualification, with an average of at least 60% in for major subjects in the final undergraduate year.
  • The formal university’s Recognition of Prior Learning (RPL) Policy may be applied in instances where applicants do not meet the minimum admission requirements for entry into the Honours Degree.

Programme Structure

In order to satisfy the qualification requirements, students must take and pass at least 120 credits.  The BSc.Hons in Computer Science comprises of five compulsory modules.

Module Code

NCOS84040

Module Name

Research Project

Module Description

The research methods and project is a core module of the programme. It introduces students to research, mainly focusing on topics related to research planning, proposal and abstract writing. It gives details regarding how to introduce a research topic, how to state the statement of the problem, how to provide grounds and background to a research topic, as well as how to motivate and justify the worthiness of a topics. Approaches to conducting valuable literature reviews, giving the important parts of literature reviews, elucidating on referencing styles, and how to filter relevant literature towards pinpointing gaps in the body of knowledge are discussed.)

Module Content

·         Introduction to research methods

·         Topic choosing and supervisor relationship

·         Research project management

·         Scientific and technical writing

·         Conducting an electronic based literature search

·         Ethics of research: Honesty and integrity

·         Data analysis using statistical software

·         Publication of research output

Learning Outcomes

Upon completion of this module, students should be able to:

Select a topic, plan, and develop a research project proposal for the topic.

·         Introduce a research topic, clearly stating the statement of the problem, giving sound background, motivation, and envisioned contributions of the work.

·         Filter and present relevant literature, culminating the review with a gap to fill.

·         Select and justify an appropriate research methodology, choosing a suitable paradigm, valid data collection strategy, participants, and relevant statistics.

·         Develop good experiment designs for evaluating and testing selected strategies.

·         Make informed ethical considerations before and during the study.

·         Demonstrate research competencies by completing the independent research assignment in time, using proper referencing and writing styles, with the aim of possible publication of the research output.

Module Code

NCOS84120

Module Name

Advanced Algorithms Analysis and Application

Module Description

This course introduces students to advanced techniques for the design, analysis and evaluation of algorithms, and explores a variety of areas of applications of the same skills. It discusses different methodologies used to solve real world problems, exposing students to a variety of algorithms and computational resources available in the literature, critically analyzing apparent limitations on solving problems efficiently. The course seeks to develop in students, appropriate mathematical skills for algorithm design, analysis, and evaluation, as well as to develop skills to design and implement efficient programming solutions to various problems.

Module Content

·         Asymptotic notations, recurrences, algorithm analysis

·         Divide and conquer algorithms

·         Greedy algorithms

·         Dynamic programming algorithms

·         Amortized analysis

·         Graph algorithms,

·         Network Flow: steepest assent, Edmonds-Karp, matching:

·         Number-Theoretic Algorithms

·         NP-Completeness

Module Code

NCOS84220

Module Name

Computer Security

Module Description

The module, mainly, investigates policies, standards, protocols, algorithms, services, and mechanism used for detecting, preventing, and reversing computer security attacks in order to ensure confidentiality, integrity, and availability/accessibility/accountability/authenticity of data. It covers aspects of computer security from a wide perspective, including language-based security, network security, and operating systems security using technical mechanisms. Graduates of this module should particularly demonstrate an informed understanding of the number theories applied in cryptography, including the divisibility and division algorithms, uses of greatest common divisors, Euclidean algorithms, modular arithmetic, prime numbers, Fermat’s and Euler’s Theorems, Chinese remainder theorem, as well as testing entries for primality.

Module Content

·         Computer security environment

·         Cryptography fundamentals

·         Protection mechanisms for computer systems

·         Authentication and inside attacks

·         Various system attacks and defensive mechanism

·         Non-technical Aspects: Administration of security systems; policies; physical security; economics of security; legal and ethical issues

Module Code

NCOS84320

Module Name

Software Engineering

Module Description

This module introduces students to software engineering and its key concepts, practice, standards, and models used for planning, managing, designing, analyzing, and evaluating software projects. It focuses on describing software development life cycle activities required to ensure that software products are developed and delivered on time and within the budget. Students should acquire sufficient software engineering skills, methods, practices, and appropriately apply these skills in the development of environment-specific tailored ICT solutions for the industry, commerce, and education.

Module Content

·         Software process

·         Software modelling

·         Design and implementation

·         Software testing strategies

·         Software security and dependability

·         Professional software practices and ethics

·         Advanced topics in software engineering

·         Software management (project, process and configuration management)

Learning Outcomes

At the end of the module, students are expected to be able to:

·         Describe the software engineering process, stating why it requires management attention, the challenges related to risk management, human resources management, and its impact on productivity and quality.

·         Describe the various types of software systems, different software engineering techniques, and ethical and professional issues important to software engineers.

·         Discuss the processes involved in discovering and documenting systems requirements, including user and system requirements, functional and non-functional requirements.

·         Describe agile software development, including an understanding of the rationale for agile software development methods, the agile manifesto, and the differences between agile and plan-driven development.

·         Describe graphical models for presenting software systems, including the fundamental system modelling perspectives of context, interaction, structure, and behaviour.

·         Describe the concepts of software architecture and architectural design, indicating their importance, and the decisions associated with the architectural design processes and architectural patterns.

·         Describe the principles underlying object-oriented thinking and the methods used to accomplish object-oriented analysis and design.

·         Explain software security and dependability, in relation to system errors, acknowledging that change is inevitable if systems are to remain relevant.

·         Describe strategies and tactics for software testing and test case design. Students should demonstrate an understanding of the stages of testing (from testing during development to acceptance testing). They should be able to identify those techniques that help them choose test cases geared to discovering program defects using test-first development.

·         Describe changeover approaches, maintenance, and the costs involved.

Module Code

NCOS84420

Module Name

Machine Learning

Module Description

The module equips the students with knowledge of machine learning concepts to enable them design, test, implement and evaluate general purpose algorithms that facilitates how machines perceive the environment characterized by data. The technical aspect of the course provides students with mathematical concepts and software tools to use machines (computers) to learn to discriminate behavior of interest from the rest and be able to take reasonable decisions. The module emphasizes on the concept of learning, data models and learning approaches, as well as on mathematical underpinnings of learning algorithms.

Module Content

·         Concept of learning, data models and learning approaches

·         Mathematical underpinnings of learning algorithms

·         Classification, association, regression and clustering methods

·         Design and analyze machine learning algorithms

·         Implement various machine learning algorithms in a range of real-world applications

Learning Outcomes

At the end of the module, students are expected to be able to:

·         Demonstrate an understanding of the theoretical mathematical foundations of machine learning concepts.

·         Demonstrate a good understanding of the fundamental issues and challenges of machine learning, including, but not limited to, data, model selection, model complexity.

·         Show an understanding of popular machine learning paradigms and approaches

·         Analyze, design and evaluate learning systems in unfamiliar problem domains.

·         Implement real-world ICT solutions using machine learning algorithms.

 
Career opportunities

Careers include data scientist, data architect, data analyst, business analyst, data/analytics manager, data engineer, intelligence analyst, data mining engineer, solutions architect and data manager.

Please contact the School Registrar Ms Nobulali Mathimba on (053) 491 0369 or postgrad.nas@spu.ac.za

Period of study: 1 Year (Full-time); 2 Years (Part-time)

The programme has been designed to specifically focus on computing structures that support “Big Data” challenges. Graduates will contribute immensely in solving analytically complex problems in real life settings such as in industry, Government and other forms of organisations at national and international level.  This Honours programme is designed to address challenges of digital transformation that can be attributed to lack of skills and knowledge to efficiently transform data science and its technologies.

Admission requirements

(a) The General Rules of Sol Plaatje University in respect of admission to Bachelor Honours Degrees (aligned with the Higher Education Qualification Sub-Framework: HEQSF) are applicable to this degree.

(b) To be admitted to the Bachelor of Science (Honours) programme, a student must be in possession of an acknowledged Bachelor qualification at NQF Level 7 or cognate qualification, with an average of at least 60% in for major subjects in the final undergraduate year.

(c) The formal university’s Recognition of Prior Learning (RPL) Policy may be applied in instances where applicants do not meet the minimum admission requirements for entry into the Honours Degree.

 Programme Structure

The Bachelor of Science Honours in Data Science is a postgraduate qualification at NQF Level 8 and consists of at least 120 Credits.The BSc.Hons in Data Science comprises of seven compulsory modules and one elective module.  The Elective Module offered is prescribed by the Head of Department, pursuant to relevant staff, resources and current topic of interest considerations.

1.1.1 Module Information

Module Code

NRPJ840

Module Name

Research Project

Module Description

The module is about exploring real world data science challenges and applying relevant research ethics, language and processes such as quantitative or qualitative approaches to address problems.

Module Content

Students will take full responsibility of work and use appropriate resources where necessary. This module is the research project of the programme

Learning Outcomes

At the end of the module the students will be expected to demonstrate knowledge in the application of research methodologies, frameworks and research skills acquired from the programme to engage Data Science challenges emanating for industry, government and other organisations.

Module Code

NCSD841

Module Name

Computer systems for big data

Module Description

The module is an introduction to large-scale distributed systems with an emphasis on big-data processing and storage infrastructures. This course focuses on the computer systems aspects and how various parts of a big data computer system (hardware, system software, and applications) are put together, what are the appropriate approaches to realize high performance, scalability, and reliability in practical big data computer systems.

Module Content

Content include fundamental tradeoffs in distributed systems, techniques for exploiting parallelism, big-data computation and storage models, design and implementation of various well-known distributed systems infrastructures, and concrete exposure to programming big-data applications on top popular, open-source infrastructures for data processing and storage systems.

Learning Outcomes

At the end the module, students are expected to synthesis and anayse large scale-data problems emanating from distributed infrastructures for application to real-life scenarios.

Module Code

NLSO841

Module Name

Large scale optimization

Module Description

This module focuses on optimization techniques used to find solutions of large-scale problems that typically appear in statistical learning / data analysis tasks with big data.

Module Content

Content include projected gradient methods, accelerated first order algorithms, conjugate gradient methods, quasi-Newton methods, block coordinate descent, proximal point methods, stochastic sub-gradient algorithms, alternating direction method of multipliers, semi-definite programming, interior-point algorithms for conic optimization, interior-point algorithms for conic optimization, Conic optimization and Barrier functions

Learning Outcomes

At the end of the module the students will be able to conceptualize and synthesize modern optimization techniques suitable for large-scale/big-data problems and be able to apply, and/or modify efficient methods for their own scientific/research problems.

Module Code

NAML841

Module Name

Advanced Machine Learning

Module Description

The module provides students with advanced machine learning techniques necessary for computational analysis that support various learning algorithms such as those used in robotics, data mining, computer vision, text and web data processing.

Module Content

Content include Statistical Theory: Maximum likelihood; Bayes, minimax, parametric versus non-parametric methods; Mathematical Underpinning of theories; Utilization of Models; Deep Learning and Comparative analysis

Learning Outcomes

At the end of the module students are expected to be able to:

·         Conceptualise principles and theory of machine learning for algorithmic design.

·         Problematise models for supervised, unsupervised, and reinforcement machine learning for analysis of strength and weakness of respective models.

·         Interpretation and solve mathematical equations from Linear Algebra, Statistics, and Probability Theory used in these machine learning models.

·         Design test procedures in order to evaluate a model

·         Experiment several models in order to gain better results

·         Analyse and make choices for modelling new machine learning tasks based on reasoned argument.

Module Code

NHPC841

Module Name

High Performance Computing

Module Description

This module introduces students to the architecture of several types of high performance computers and their implications on the performance of algorithms on these architectures in order to design and implement efficient algorithms for high-performance computers.

Module Content

The content include High-performance computer architecture, enhancement of performance on single and multi-processor computers, parallelization overheads; performance evaluation; introduction to parallel algorithms.

Learning Outcomes

At the end of the module the students will be expected to synthesize and demonstrate theoretical knowledge of the architecture of several types of high performance computers and be able to design and apply efficient algorithms on such architectures. Further students would be able to conceptualize the current state-of-the art in parallel programming environments, portable software libraries and program development.

Module Code

NDEV842

Module Name

Data Exploration and Visualization

Module Description

The module is to provide students with advanced concepts and roles of data exploration and visualization through use of techniques such as data mining.

Module Content

·         Introduction to Upstream exploratory analysis

·         Machine learning and Clusters

·         Introduction to Scala

·         Spark Applications

·         Configuration of Spark Nodes

·         Machine learning and Spark

·         Working with Distributed Datasets

·         Streaming

Learning Outcomes

At the end of the module students are expected to be able to:

·         Investigate and synthesize a data-oriented problem area

·         Apply specialist knowledge through use of specialized architectures and operations.

·         Experiment, perform data analysis and demonstrate results through use of upstream programs such as Spark.

·         Application of log mining, textual entity recognition and collaborative filtering techniques to real-world data questions

Module Code

NDSC842

Module Name

Data Security and Cryptographic Systems

Module Description

The module introduces students to the theoretical and practical aspects of data security and cryptographic algorithms and protocols.

Module Content

Content include classical cryptography techniques; mathematical foundations; secret key cryptography; public key cryptography; authentication and digital signature; network cryptographic protocols.

Learning Outcomes

At the end of the module the student is expected to be able to synthesize theoretical aspects of data security and cryptographic algorithms and protocols and further be able to design and apply techniques, algorithms, architectures and tools used for data security and cryptography in the data science project environments.

Module Code

NMSP842

Module Name

Multidimensional Signal Processing

Module Description

This module is based introduces students to theory and practical tools used in processing large scale data arising from problems in engineering and computer science.

Module Content

The content includes processing algorithms suitable for large-scale data tasks involving sparse signals as the Sparse Fourier transform. Other introductory topics in the module are the extension of classical signal processing on data indexed by graphs (discrete signal processing on graphs, DSPG). At the end of each topic, illustrative examples with their respective application scenarios, either PYTHON language or in MATLAB, are provided.

Learning Outcomes

At the end of the module the students will be expected to synthesize and demonstrate theoretical knowledge in the application of tools and modelling used in processing large scale data arising from problems in engineering and computer science.

Module Code

NSTD842

Module Name

Special Topics in Data Science

Module Description

Special Topics in Data Science is a unique module based on various emerging technologies of data science.  The topics are taught in the last semester of the programme and selected from recent developments and trends in data science or big data technology.  The module introduces new or emerging data science or big data technology, and showcase the advanced tool currently used in the industry.

Module Content

Topics covered in module vary and are based on different fields of data science, some include, Astro-informatics, Advanced Big Data Analytics, Advanced Distributed systems, Statistical Machine Learning, Advance R and Python programming languages, SAS programming environment, Data Mining tools, Internet of Things (IoT), New SQL Database Management Systems, Cloud Computing and Data center Networking, etc.

Learning Outcomes

At the end of the module the students will be expected to have exposure with current advances of technical industry based tools used in Data science.

Career opportunities

Science graduates are open to various career opportunities in academic, government and industry. 

A Bachelor of Science (BSc) degree emphasises coursework in the sciences, mathematics or career-specific topics. 

The degree can serve as foundation for a variety of careers. Job prospects for BSc graduates offer a diverse range of professional fields to work in, such as healthcare, finance, biology, chemistry, forensic sciences, consulting, engineering and computer programming.

Contact the School Registrar Ms Nobulali Mathimba on (053) 491 0369 or postgrad.nas@spu.ac.za

Period of study: 1 Year (Full-time); 2 Years (Part-time)

This programme is designed to respond to skills development needs of South Africa and the world at large. The programme is designed to develop highly qualified students who are analytical and independent thinkers with knowledge of how to model, evaluate and solve both quantitative and qualitative problems in the biodiversity conservation sciences. The programme also provides a sound theoretical and practical base and exposure to Biological Sciences’ disciplines. 

Admission requirements

 (a) The General Rules of Sol Plaatje University in respect of admission to Bachelor Honours Degrees (aligned with the Higher Education Qualification Sub-Framework: HEQSF) apply to this degree.

(b) To be admitted to the Bachelor of Science (Honours) programme, a student must have an acknowledged Bachelor qualification at NQF Level 7 or cognate qualification, with an average of at least 60% in four major subjects in the final undergraduate year. 

(c) The formal university’s Recognition of Prior Learning (RPL) Policy may be applied in instances where applicants do not meet the minimum admission requirements for entry into the Honours Degree.

 Programme Structure

The Bachelor of Science (Honours) in Biological Sciences degree can be completed in one year on a full-time basis or in two years on a part-time basis. 

The programme carries 120 credits allocated as follows: 

  • 67% of course work and, 
  • 33% as a compulsory research project

The modules in the programme have no rules of progression. The curriculum is developed for two fields of specialization in Biological Sciences, namely:

  • Botany and
  • Zoology

Career opportunities

Students engage with the necessary theory and practice that will broaden, deepen and intensify their scope of theoretical concepts and expertise in particular areas associated with the biological sciences.

This qualification also includes a research component which aims at providing students with professional research skills that will enable them to embark on a career as a researcher.

Upon successful completion, graduates may articulate to a master’s degree in a related field. Qualifying learners can be employed in many industries which include: conservation, biodiversity sciences, environmental sciences, forensic sciences and academia. In all these areas, biologists work closely with other scientists and researchers to develop biological research techniques, adapt existing techniques, design experiments, and focus analyses on species and habitat conservation at molecular and organismal level.

Contact the School Registrar Ms Nobulali Mathimba on (053) 491 0369 or postgrad.nas@spu.ac.za

The programme is designed to respond to skills development needs of South Africa and the world at large. The program is designed to develop highly qualified students who are analytical and independent thinkers with knowledge of how to model, evaluate and solve both quantitative and qualitative problems in science and technology.  The programme also provides a sound theoretical and practical base and exposure to mathematical sciences’ disciplines. 

Students engage with the necessary theory and practice that will broaden, deepen and intensify their scope of theoretical concepts and expertise in particular areas associated with the mathematical sciences. This qualification also includes a research component which aims at providing students with professional research skills that will enable them to embark on a career as a researcher. Upon successful completion of an honours in mathematical sciences, graduates may articulate to a master’s degree in a related field.

Admission Requirements 

  • The General Rules of Sol Plaatje University in respect of admission to Bachelor Honours Degrees (aligned with the Higher Education Qualification Sub-Framework: HEQSF) are applicable to this degree.
  • To be admitted to the Bachelor of Science (Honours) programme, a student must be in possession of an acknowledged Bachelor qualification at NQF Level 7 or cognate qualification, with an average of at least 60% in for major subjects in the final undergraduate year.
  • The formal university’s Recognition of Prior Learning (RPL) Policy may be applied in instances where applicants do not meet the minimum admission requirements for entry into the Honours Degree.

Programme Structure

The length of the program is one year on a full-time basis and two years on a part-time basis, with 120 credits allocated as 75% of course work and 25% as a compulsory research project. The modules in the programme have no rules of progression. The curriculum is developed for three fields of specializations in Mathematical Sciences, namely:

  1. Applied Mathematics
  2. Mathematics
  3. Statistics


Career opportunities

The qualifying learners can also be employed in many industries including: finance, economics, engineering, public health, education, and medicine. In all these areas, mathematical scientists work closely with other scientists and researchers to develop mathematical techniques, adapt existing techniques, design experiments, simulations and direct analyses of surveys and retrospective studies. 

Contact the School Registrar Ms Nobulali Mathimba on (053) 491 0369 or postgrad.nas@spu.ac.za

Period of study: 1 Year (Full-time); 2 Years (Part-time)

This programme is designed to equip graduates with vast knowledge in the physical sciences, including advanced topics in Chemistry, Physics and Geography.

Furthermore, this qualification also includes a research project which is primarily aimed at introducing students to the postgraduate-level academic experience. The required set of skills and the knowledge creation that takes place during the course facilitate the student’s journey as a professional researcher, scientist and academic.

Admission requirements

  • (a) The General Rules of Sol Plaatje University in respect of admission to Bachelor Honours Degrees (aligned with the Higher Education Qualification Sub-Framework: HEQSF) are applicable to this degree.

(b) To be admitted to the Bachelor of Science (Honours) programme, a student must be in possession of an acknowledged Bachelor qualification at NQF Level 7 or cognate qualification, with an average of at least 60% in four major subjects in the final undergraduate year.

(c) The University’s formal Recognition of Prior Learning (RPL) Policy may be applied in instances where applicants do not meet the minimum admission requirements for entry into the Honours Degree.

 Programme Structure

The Bachelor of Science (Honours) in Physical Sciences can be completed in one year on a full-time basis or in two years on a part-time basis.  

Students must take and pass courses totalling at least 120 credits allocated as follows: 

  • At least five or six compulsory modules and elective modules
  • From the above, choose ONE with 16 credits or TWO with 8 credits
  • Compulsory research project

The curriculum is developed for three fields of specialisations in Physical Science, namely:

  • Chemistry
  • Geography
  • Physics

Career opportunities

Science graduates are open to various career opportunities in academic, Government and industry. Jobs are available in research and development, chemical analysis, quality control, and environmental monitoring among others. Medical and forensic laboratories, public institutions such as government departments, and in teaching profession in secondary schools and higher education institutions.

Contact the School Registrar Ms Nobulali Mathimba on (053) 491 0369 or postgrad.nas@spu.ac.za

Period of study: 2 Years (Full-time); 3 Years (Part-time)

The Master of Science in e-Science degree at Sol Plaatje University is completed by coursework and a mini-dissertation research report.

A candidate must successfully complete the following courses to obtain this degree:

– Research Report: Data Science
– Research Methods and Capstone Project in Data Science
– Data Privacy and Ethics

And any four courses from the list below are subject to the approval of the Senate:

– Adaptive Computation and Machine Learning
– Data Visualisation and Exploration
– Large Scale Optimisation for Data Science
– Large Scale Computing Systems and Scientific Programming
– Mathematical Foundations of Data Science
– Special Topics in Data Science
– Statistical Foundations of Data Sciences

This is a research-focused Master of Science (e-Science) degree conferred after accepting a mini-dissertation on an approved topic in the field of Data Science embodying original research as approved by the Senate.

Admission requirements

  • The minimum admission requirement is a relevant Bachelor of Science Honours Degree in a relevant discipline in Science (Computer Science, Mathematics, Physics, and Statistics) or an appropriate NQF level 8 qualification.
  • A “professional” Bachelor’s Degree with a minimum of 96 credits at Level 8 or a cognate Postgraduate Diploma with demonstrable knowledge of basic principles of Computing, Calculus, Linear Algebra, Probability, and Statistics may also be recognised as meeting the minimum entry requirement to the Master of Science (e-Science) degree programme. 
  • Ad eundem gradum students should submit a portfolio of satisfactory evidence of their suitability for admission into the degree to the Head of Department.  
  • Students should have at least 65% of all the courses completed in their NQF level 8 qualification. 
  • Candidates with less than 65%, who have relevant Industry/professional experience, will be considered upon recommendation from the Head of Department. 
  • University’s Policy governs RPL admissions on Recognition of Prior Learning
  • In addition to the above, admission is dependent upon the supervision capacity of the department in terms of the availability of a supervisor.    

Career opportunities

Science graduates are open to various career opportunities in academic, Government and industry.

Jobs are available in research and development, chemical analysis, quality control, and environmental monitoring among others. Medical and forensic laboratories, public institutions such as government departments, and in teaching profession in secondary schools and higher education institutions.

Contact the School Registrar Ms Nobulali Mathimba on (053) 491 0369 or postgrad.nas@spu.ac.za

For more information, contact the School Registrar:
Ms Nobulali Mathimba
Telephone (053) 491 0369
Email nobulali.mathimba@spu.ac.za

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