Master of Science in Computer and Information Sciences

Purpose

The primary purpose of the Master of Science in Computer and Information qualification is to deepen a learner's mastery of a chosen field in the domain of computer and information sciences. A learner admitted to the qualification will either specialise in data science or computer science. Learners will be trained in relevant scientific methods to prepare them for roles as professional scientists in academia or industry.

Many employers in South Africa and beyond require a master's degree in data science or computer science as part of the minimum requirements of many data science roles such as data scientist, machine learning engineer, machine learning researcher, etc. Similarly, many academic and industry research-based roles require a master's degree as the minimum qualification. Additionally, technical lead roles in the domain of computer science and data science require a master's degree as the minimum qualification. Thus, the qualification will embody aspects of practical training essential for functioning as a senior scientist in a work environment able to solve real-world problems.

The purpose is also to equip learners with the tools necessary for them to be professional academic practitioners in the domains of data science and computer science. The qualification also prepares learners for entry into academia.

Computer Science and Data Science are among the scarce skills in South Africa and beyond. As such, the qualification also serves the purpose of bridging the gap between the demand and supply for these computational skills.

In certain industries, such as higher education or research-based organizations, graduate degrees offer mandatory training and the best path for certain jobs or promotions. This qualification also has the advantage of developing the learner's skill set in science and academic writing. As a result, one can become a better problem solver and handle challenging tasks with greater ease. One can continue to build upon a wealth of expertise by obtaining a graduate degree, preparing one for a life of continuous learning with vertical articulation to a PhD in their area of specialisation.

Upon completion of this qualification, qualifying learners will be able to

• Undertake research competently and independently in Data Science or Computer Science.
• Demonstrate a thorough understanding of research methodologies and techniques used in Data Science or Computer Science scientific research.
• Apply proficiency in acquiring technical skills through the use of specialised equipment or software, as well as the ability to conduct fieldwork relevant to their research project.
• Demonstrate the capability to write and interpret research reports, displaying a high level of analytical and critical thinking skills.
• Demonstrate effective communication skills to convey outputs from the research project to academics and the wider scientific community in a clear, concise, and coherent manner.

Rationale

The proposed qualification responds to a real need in South Africa in that there is a critical shortage of graduates with science qualifications, especially at the Masters level. The goal is to promote human capital development and growth of the knowledge system that drives the development of new scientific knowledge to achieve a knowledge economy, economic growth and development. A Master of Science degree can also open many career doors, including certain career fields, advancement opportunities, and increased learning potential. The qualification also provides people who are or aspire to be professionally involved in higher education a unique opportunity to expand and improve their expertise and develop research skills in a field that is directly applicable to their professional positions and responsibilities.

The qualification will also help learners to focus on a particular field of study, which allows one to gain specialised knowledge to advance in their field of specialization and become more competitive. This qualification also makes it easier to transition into more senior positions.

A typical learner in this qualification is someone who has a BSc honours degree in a science-related discipline and meets the minimum admission criteria. The learner would be eligible to apply for a PhD degree at any university after successful completion of this course.

Recognition of Prior Learning (RPL)

RPL for access

• Learners who do not meet the minimum entrance requirements or the required qualification that is at the same NQF level as the qualification required for admission may be considered for admission through RPL.
• To be considered for admission in the qualification based on RPL, applicants should provide evidence in the form of a portfolio that demonstrates that they have acquired the relevant knowledge, skills, and competencies through formal, non-formal and/or informal learning to cope with the qualification expectations.

RPL for credit

• Learners may also apply for RPL for credit for or towards the qualification, in which they must provide evidence in the form of a portfolio that demonstrates prior learning through formal, non-formal and/or informal learning to obtain credits towards the qualification.
• Credit shall be appropriate to the context in which it is awarded and accepted.

Entry Requirements

The minimum entry requirement for this qualification is

• Bachelor of Science Honours in Computer Science, NQF level 8.

Or

• Bachelor of Science Honours in Computer Science and Information Systems, NQF level 8.

Or

• Postgraduate Diploma, NQF Level 8 in a cognate field.

This qualification consists of the following modules at National Qualifications Framework Level 9 totalling 180 Credits.

Compulsory Module, NQF Level 9, totalling 180 Credits (Select one)

• Dissertation in Data Science, 180 Credits.
• Dissertation in Computer Science, 180 Credits.

1. Undertake research competently and independently in Data Science or Computer Science.
2. Demonstrate a thorough understanding of research methodologies and techniques used in Data Science or Computer Science scientific research.
3. Apply proficiency in acquiring technical skills using specialised equipment or software, as well as the ability to conduct fieldwork relevant to their research project.
4. Demonstrate the capability to write and interpret research reports, displaying a high level of analytical and critical thinking skills.
5. Demonstrate effective communication skills to convey outputs from the research project to academics and the wider scientific community in a clear, concise, and coherent manner.

Associated Assessment Criteria for Exit Level Outcomes 1.

• Illustrate a clear understanding of the research question and objectives, and formulate an appropriate research design and methodology.
• Collect and analyze relevant data using appropriate techniques and tools.
• Draw conclusions and make recommendations based on the research findings.
• Manage the research project effectively, including planning, time management, and resource allocation.

Associated Assessment Criteria for Exit Level Outcomes 2.

• Illustrate a comprehensive knowledge of research methodologies and techniques used in Data Science or Computer Science scientific research.
• Critically evaluate the strengths and weaknesses of different research methods and select the most appropriate approach for their research project.
• Apply advanced statistical and computational methods to analyse data and draw conclusions.
• Evaluate and critique the research methods used in published studies in the chosen field.

Associated Assessment Criteria for Exit Level Outcomes 3.

• Apply specialised equipment or software relevant to the research project.
• Plan and conduct fieldwork effectively, including data collection and management and addressing ethical and safety issues.
• Troubleshoot technical problems and adjust research methods as needed.

Associated Assessment Criteria for Exit Level Outcomes 4.

• Write a clear and well-structured research report that effectively communicates the research question, objectives, methodology, findings, and conclusions.
• Apply appropriate language, style, and format for a scientific research report.
• Apply high level analytical and critical thinking skills in interpreting the research findings and drawing conclusions.
• Identify the limitations of the research and suggest areas for future research.

Associated Assessment Criteria for Exit Level Outcomes 5.

• Effectively communicate the research outputs to academics and the wider scientific community, using clear, concise, and coherent language.
• Communicate to different audiences and formats, such as conference presentations or journal articles.
• Apply appropriate visual aids, such as graphs or tables, to convey the research findings.
• Respond to questions and critique from peers and reviewers in a professional manner.