SAQA

All qualifications and part qualifications registered on the National Qualifications Framework are public property. Thus the only payment that can be made for them is for service and reproduction. It is illegal to sell this material for profit. If the material is reproduced or quoted, the South African Qualifications Authority (SAQA) should be acknowledged as the source.

SOUTH AFRICAN QUALIFICATIONS AUTHORITY

REGISTERED QUALIFICATION:

Master of Science in Data Science

SAQA QUAL ID	QUALIFICATION TITLE
119843	Master of Science in Data Science
ORIGINATOR
University of Johannesburg
PRIMARY OR DELEGATED QUALITY ASSURANCE FUNCTIONARY			NQF SUB-FRAMEWORK
-			HEQSF - Higher Education Qualifications Sub-framework
QUALIFICATION TYPE	FIELD		SUBFIELD
Master's Degree	Field 10 - Physical, Mathematical, Computer and Life Sciences		Mathematical Sciences
ABET BAND	MINIMUM CREDITS	PRE-2009 NQF LEVEL	NQF LEVEL	QUAL CLASS
Undefined	180	Not Applicable	NQF Level 09	Regular-Provider-ELOAC
REGISTRATION STATUS		SAQA DECISION NUMBER	REGISTRATION START DATE	REGISTRATION END DATE
Reregistered		EXCO 0333/25	2025-07-10	2028-07-10
LAST DATE FOR ENROLMENT		LAST DATE FOR ACHIEVEMENT
2029-07-10		2032-07-10

In all of the tables in this document, both the pre-2009 NQF Level and the NQF Level is shown. In the text (purpose statements, qualification rules, etc), any references to NQF Levels are to the pre-2009 levels unless specifically stated otherwise.

This qualification does not replace any other qualification and is not replaced by any other qualification.

PURPOSE AND RATIONALE OF THE QUALIFICATION

Purpose:
The purpose of the Master of Science in Data Science is to equip postgraduate learners with advanced research abilities, and a good knowledge base in the field of Data Science. Learners are prepared for work and research activities in the field of Data Science, Machine Learning Engineering and Data Engineering. Through the conducting of independent research in the field, the learners will develop skills in a specialist area and will be able to work with both researchers or research teams and industry professionals consequently contributing to addressing the skills shortage in the industry. As required by a master's degree, the learners will have a developed self-learning capability which will promote lifelong learning.

The qualification is suited for learners who have a data-related background and want to transition and learners who have a technical/programming background and want to focus on data-related problems. The qualification will attract learners with the following academic background.

Bachelor's degrees in Mathematical Sciences/Engineering.

Data Engineers and Emerging Data Scientists.

Machine Learning Engineers for upskilling the understanding of data science.

Data scientists who require a deeper understanding.

Managers of groups of individuals as stipulated above, with the required technical background.

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

Identify and accurately analyse problems within the data science environment by researching problems creatively and innovatively.

Organize and manage activities responsibly, effectively, and ethically, accept and take responsibility within limits of competence, and exercise judgement based on knowledge and expertise, pertaining to the field of research.

Plan and conduct applicable levels of investigation, research and/or experiments by applying appropriate theories and methodologies and performing appropriate data analysis and interpretation.

Communicate effectively, both orally and in writing, with specific research audiences and the community at large, in so far as they are affected by the research, using appropriate data analysis and interpretation.

Demonstrate, where applicable, cultural, and aesthetic sensitivity across a range of social and environmental contexts in the execution of research/development activities.

Rationale:
The world is inundated with data, constantly moving, and renewing itself. The vast amounts of data are not useful if there are no people able to collect, collate, analyse, and apply it to the benefit of society. Nationally there is a well-documented extreme shortage of high-level skills in Data Science, Big Data, and data analytics in general. According to Data Science Association, Data Science is the scientific study of the creation, validation, and transformation of data to create meaning. The qualification becomes a fundamental steppingstone toward South Africa's growing need for scientists and engineers capable of utilising data to its full. Both structured and unstructured, various scientific components must be collated and structured within an often-existing technological framework to obtain insight from data in various forms with various outcomes. Data pipelines must be in place to design relevant machine learning models. Data must be appropriately analysed and engineered to ensure its usefulness and the implementation of the model must consider the business context and limitations. Only in this manner can the true benefit of data science and machine learning be realised.

Data science is a multi-disciplinary field, involving a wide variety of knowledge areas from applied mathematics to statistics and artificial intelligence to machine learning, which places it within the realm of the institution's vision for driving the fourth industrial revolution (4IR). Advances in computer technology and processing speed, the relatively low cost to store data, and the massive availability of data from the Internet and other sources have provided the ideal platforms for taking data and making meaning from it for the benefit of society. As a result, the rationale behind the development of the qualification is due to:

The resurgence of data science as a driver of business development is seen across all sectors which require the skills of a data scientist to be realised.

The increase in relevant data and computational abilities has made machine learning a vital tool in obtaining business value through data analytics key metrics, predictive analytics, and consumer behaviour analytics.

The nature of the skillset needed for development in data intelligence is specialised, and as a result, there is often a shortage of human resources.

The level of scientific expertise along with the practical experience of engaging with the technicalities of application, require individuals to straddle both academia and industry. Effective research entities are often ideal to host qualifications in a field, given their collaborative mindset with regard to research endeavours, and engagement with the industry. The qualification provides the opportunity to source projects from the industry, which often leads to highly impactful and practical research.

Furthermore, given the unique and diverse skillset needed by Data Scientists, the demand for individuals has grown exponentially, far outstripping the current supply. Globally and locally, the education system needs to create opportunities for further skills development to stay abreast of this demand. The industry does not have the capacity or necessary skills to conduct the required training without the support of formal higher education institutions. As a result, the only way the next generation of Data Scientists can be trained is through appropriate and effective academic courses, often designed to consider part-time study.

The training of postgraduate learners would further allow for the development of cutting-edge research in Data Science, Machine Learning Engineering and Data Engineering. We would be a part of training the next generation of scientists or engineers capable of converting data intelligence into business value for any industry. The individuals who are trained through this qualification will have the ability to tackle problems across social, economic, and technical fields, allowing them to apply a cross-disciplinary and complex lens to the multi-layered challenges of the 21st century. Examples include for instance: investing in building healthier communities, optimizing both social outcomes and financial performance, alleviating the pressures on the working poor, addressing environmental stresses while generating jobs, and isolating which social impact metrics are most powerful in predicting future business performance.

The impact of individuals who have gone through this qualification in various sectors is not quantifiable. This will be a flagship qualification, leading the way into a data-driven society where individuals with relevant skills are of key importance. There is much demand in finance, telecommunications, agriculture, and the health industry for the upskilling up staff in Data Science, Machine Learning Engineering and Data Engineering. Given the wide scope of the field, being a Data Scientist covers a range of professions including, but not limited to, engineers, computer scientists, applied mathematicians, physicists, and machine learners.

Currently, many organisations are spending much time and money to upskill their staff, specifically because the drive to become an organisation which makes data-driven decisions has become the primary focus of any manager. The learners who complete this qualification will be in the position to take up important roles in any organisation. They will have gained skills in an area of vital interest and necessity in industry. The research group is comprised of a group of individuals who are all experts in appropriate fields. They are active in research, teaching and postgraduate learner supervision and have links with international and local collaborators in academia and industry. These partnerships will provide the prospects for the sustainability of the proposed qualification. Furthermore, the qualification has been structured in a way which will allow it to speak to and support future qualifications in Machine Learning Engineering and Data Engineering for instance and allows articulation between a variety of Honours and master's qualifications in Science, the Academy of Computer Science, and the School of Electrical Engineering at the very least. The qualification has been designed to be a natural follow-on from four-year degrees in Engineering, of Honours qualifications in Science and Information Technology. It is intended that qualification will assist in fulfilling the National Development Plan, whilst simultaneously enhancing the research profile.

LEARNING ASSUMED TO BE IN PLACE AND RECOGNITION OF PRIOR LEARNING

Recognition of Prior Learning (RPL):
The institution has an approved Recognition of Prior Learning (RPL) policy which is applicable to equivalent qualifications for admission into the qualification. RPL will be applied to accommodate applicants who qualify. RPL thus provides alternative access and admission to qualifications, as well as advancement within qualifications.

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 should they be allowed entrance into the qualification.

Entry Requirements:
The minimum entry requirement for this qualification is:

Bachelor of Data Science, NQF Level 8.
Or

Bachelor of Science Honours in Data Science, NQF Level 8.
Or

Postgraduate Diploma in Data Science, NQF Level 8.
Or

Postgraduate Diploma in Data Analytics, NQF Level 8.

RECOGNISE PREVIOUS LEARNING?

QUALIFICATION RULES

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

Compulsory Modules, Level 9,180 Credits:

Dissertation, 180 Credits.

EXIT LEVEL OUTCOMES

1. Demonstrate the ability to identify and accurately analyse problems within the Data Science environment by researching problems creatively and innovatively.
2. Organize and manage activities responsibly, effectively, and ethically, accept and take responsibility within limits of competence, and exercise judgement based on knowledge and expertise, pertaining to the field of research.
3. Plan and conduct applicable levels of investigation, research and or experiments by applying appropriate theories and methodologies and performing appropriate data analysis and interpretation.
4. Communicate effectively, both orally and in writing, with specific research audiences and the community at large, in so far as they are affected by the research, using appropriate data analysis and interpretation.
5. Demonstrate, where applicable, cultural, and aesthetic sensitivity across a range of social and environmental contexts in the execution of research development activities.

ASSOCIATED ASSESSMENT CRITERIA

Associated Assessment Criteria for Exit Level Outcome 1:

Identify and formulate a problem statement.

Conduct a thorough investigation of existing knowledge as reflected in the appropriate scientific literature.

Select appropriate research to solve the problem.

Apply and develop intellectual independence and advanced research skills, specialist knowledge and research methodologies to the solution of complex, unfamiliar problems in Data Science.

Analyse complex research questions in Data Science and apply specialised problem-solving skills in identifying, conceptualising, designing, and implementing methods of inquiry to solve problems within the specialisation area.

Associated Assessment Criteria for Exit Level Outcome 2:

Interpret, analyse, and critique scientific literature.

Apply high levels of responsibility, self-reflexivity, and adaptability in own management of learning and analyse and evaluate ethical implications of research which affect knowledge production in Data Science.

Apply evidence of good judgement and be cognizant of the time and cost implications of the research study.

Work with minimal supervision.

Critically contribute to the development of ethical standards in Data Science research.

Associated Assessment Criteria for Exit Level Outcome 3:

Select methodologies that are appropriate to address the research problem.

Apply qualitative and quantitative research methods and apply them in the research study.

Undertake data collection, presentation, analysis, and interpretation of data.

Apply relevant theoretical underpinnings for the arguments.

Produce a written report of the research project, including inter alia the findings, conclusions, and recommendations.

Associated Assessment Criteria for Exit Level Outcome 4:

Scientifically communicate the results in the form of a dissertation.

Communicate with various stakeholders during all phases of the study.

Integrate the theoretical underpinnings with the data gathered, in a cohesive manner.

Make recommendations regarding the findings of their research and how this relates to or can influence future research in specialisation.

Associated Assessment Criteria for Exit Level Outcome 5:

Apply and obtain the required ethical clearance.

Demonstrate an awareness of the environmental impact of the research, if relevant.

Comply with Health and Safety legislation during their research activities.

Incorporate social values into the development of solutions and modify those solutions to accommodate broader factors.

INTEGRATED ASSESSMENT
The assessment of Higher Degrees is done in accordance with the Institution's Policy on Assessment.
The qualification is by full research (dissertation). The assessment utilised is formative and summative.

Formative Assessment:
Formative assessment of written and oral research submissions is undertaken by the supervisor and institution staff to provide continuous feedback and support for the learner during the research process.
Learners will be required to submit a research dissertation. The supervisor meets with the learners on a regular basis, agreed upon mutually, to give formative feedback on the progress of the learners. When the learners complete the minor research dissertation, they submit the document, usually with the consent of the supervisor, to be assessed externally. The Faculty Higher Degrees committee (FHDC) will ratify the decision of the external assessors in consultation with the supervisor.

The assessment process is governed by institutional policy documents. When submitted for assessment, the minor dissertation, dissertation, or thesis must be accompanied by a copy of the assessors' report form and the assessment guidelines, as stipulated in faculty rules and regulations. No minor dissertation, dissertation or thesis may be submitted for final assessment without the express permission of the supervisor. Where a supervisor withholds this permission and a candidate believes the minor dissertation, dissertation, or thesis to be ready for submission, the candidate may appeal the supervisor's reluctance to the HOD and Executive Dean of the faculty in conjunction with the FHDC, in that order.

In the assessment of any component of a master's or Doctoral qualification, the faculty Postgraduate learners Assessment Committee (FPAC) is not bound to award a simple aggregate of all assessors' marks if persuasive reasons exist for awarding a different mark. Faculty regulations will stipulate the methodology to be employed in comparing the various recommended assessment outcomes. In all cases, though, the cumulative weight of the external assessors' marks. The FPAC should pay particular attention to final marks and final marks. The following results are possible for a minor dissertation or dissertation:

Acceptance and awarding of a pass mark.

Acceptance and awarding of a cum laude mark.

Conditional acceptance, with the awarding of a mark, as subject to minor corrections being made to the satisfaction of the supervisor(s).

Recommendation of substantial amendments, without the awarding of a mark, and with a recommendation/request by the assessor(s) for resubmission and reassessment within a period of three months.

Rejection and awarding of a mark reflecting a failure, in which case no reassessment is recommended or considered.

Summative Assessment:
Summative assessment is provided in the form of an external examination of the completed dissertation by at least two examiners in Data Science or cognizant disciplines at other institutions or suitably qualified industry experts, in South Africa.

The Master's or Doctoral degree can only be awarded after the successful completion of every requirement of each component of the respective degree qualification, including the successful submission of a research-based dissertation or thesis, or by the successful assessment of the candidate's achievements in each relevant coursework module together with a successfully completed minor dissertation. Faculties are responsible for ensuring that no plagiarism occurs during the finalisation of a minor dissertation, dissertation or thesis and are encouraged to consider applying commercial software qualifications to an electronic copy of the final document to rule this out.

A supervisor's report which contextualizes the supervision process is mandatory for the postgraduate learner's examination results for the final assessment process at the faculty Postgraduate learners Assessment Committee (FPAC) or equivalent committee and the SHDC. When a minor dissertation, dissertation or thesis is failed, supervisors must provide details of the quality of their assessment of the minor dissertation, dissertation, or thesis, and why it was allowed to be sent for examination.
Appropriate feedback must be given to all assessors once the outcome has been approved; this should include some indication of how specific recommendations made by assessors have been addressed.
The assessment outcome may only be revealed to the candidate once the outcome has been approved by the FPAC, FHDC, Faculty Board (for master's degrees) and SHDC/Senate (Doctoral degrees).

INTERNATIONAL COMPARABILITY

This qualification has been compared with similar qualifications offered by the following international countries.

Country: United Kingdom.
Institution: University of Exeter
Qualification Title: Master of Science in Data Science
Duration: One-year full-time and two years part-time

Entry requirements:
Bachelor's degree equivalent to at least a UK 2:2 Honours degree in a strongly numerate subject (e.g., computer science, mathematics, physics) and must be able to show evidence of good programming ability in a recognised modern computer language.

Purpose/Rationale:
Data Science is changing the way people do business. Mountains of previously uncollectable data, generated by huge growth in online activity and appliance connectivity, is becoming available to businesses in every sector. The opportunities for businesses and individuals who can manage, manipulate, and extract insights from these enormous data sets are limitless. A direct result of this is the dramatic increase in demand for individuals with the skills to turn this information into insight is outstripping supply.

The qualification is designed for those interested in learning the underpinning theory of Data Science together with methods for implementation and application. A large component of the degree involves a research project which is a one-to-one engagement with a mentor from the institution who will be an active data scientist and leader in their field. This qualification is for anyone with a foundational level of coding and mathematical knowledge who wants a thorough grounding in the fundamentals of modern data science as well as its application to real-world problems.

Qualification structure:
The qualification consists of the following compulsory and elective modules.

Compulsory Modules:

Introduction to Data Science, 15 Credits.

Fundamentals of Data Science, 15 Credits.

Machine Learning, 15 Credits.

Learning from Data, 15 Credits.

Data Science Research Project, 60 Credits.

Elective Modules (Select a total of 60 credits from the following options):

Digital Business Models, 15 Credits.

Statistical Data Modelling, 15 Credits.

Nature-Inspired Computation, 15 Credits.

Research Methodology, 15 Credits.

Evolutionary Computation and Optimisation, 15 Credits.

Computer Vision, 15 Credits.

Social Networks and Text Analysis,15 Credits.

Stochastic Processes, 15 Credits.

High-Performance Computing,15 Credits.

Statistical Modelling in Space and Time, 15 Credits.

Bayesian Philosophy and Methods in Data Science, 15 Credits.

Data Governance and Ethics, 15 Credits.

Assessment:
The assessment strategy for each module is explicitly stated in the full module descriptions given to learners. Group and team skills are addressed within modules dealing with specialist and advanced skills. Assessment methods include essays, closed book tests, exercises in problem-solving, use of the Web for tool-based analysis and investigation, mini-projects, extended essays on specialized topics, and individual and group presentations.

Similarities:

The University of Exeter (UoE) and the South African (SA) qualifications are offered for one-year full-time.

Both UE qualification and SA qualification allow learners who completed Bachelor of Science Honours Postgraduate Diploma in Data Science and/or related field.

Both the UE and SA qualifications are designed to consider part-time study and thereby address the great demand for qualified data scientists.

Both qualifications articulate vertically into a master's degree in data science or equivalent qualifications.

Differences:
The UoE qualification differs from the SA qualification, it offers compulsory and elective modules, while the SA qualification has no coursework, but it has a Dissertation.

Country: United Kingdom
Institution: Middlesex University London
Qualification Title: Master of Science in Data Science
NQF Level: 7
Credits: 180
Duration: One-year full time

Entry Requirements

A 2:2 honours degree in a related subject, such as those providing significant exposure to information technology.

Applicants with degrees in other fields who can demonstrate relevant industrial experience may also be considered.

Purpose/Rationale:
All industries now utilise data and Data-Science and Data-Analytics are increasingly identified as key industrial activities. The position of Data Scientist is rapidly becoming a required post for any company that wishes to take full advantage of the data that they collect. The qualification is unique in its fusion of machine-learning, visual analytics, and corporate data governance. Learners will explore theoretical and practical aspects with industry-recognised skills and apply machine learning and visual analytics to any data source.

This qualification has been designed to offer those with familiarity in math, science or computing an opportunity to develop a key set of skills for future employment in a way that builds on the existing knowledge and skill base. Upon completing the course, learners will be ready to fulfil the requirements of a Data Scientist. Learners will focus on the intertwining areas of machine learning, visual analytics, and data governance, and be able to strike a balance between theoretical underpinnings, practical hands-on experience, and acquisition of industrially relevant languages and packages. They will also be exposed to cutting-edge contemporary research activities within data science that will equip learners with the potential to pursue a research-based career and further PhD studies.

Exit Level Outcomes:
On completion of this qualification the successful learner will be able to:

Appraise the ideas and concepts underlying a selected set of advanced topics in data science.

Apply appropriate data science techniques to a given problem.

Analyse, reason about and implement complex data science systems.

Appraise the professional, legal, and ethical framework within which a data science professional must operate.

Plan and apply appropriate techniques for the solution of problems in data science.

Utilise a range of modelling and abstraction techniques for the specification and design of data science systems.

Critically evaluate a range of data science methodologies.

Plan and execute a challenging and substantial data science project by the application of appropriate research methods.

Effectively and independently acquire new knowledge and skills for the purpose of continuing professional development.

Analyse complex problems systematically and implement effective solutions.

Communicate effectively in writing, verbally and by presentation.

Effectively manage time and other resources.

Reflect critically on her, or his, own work and that of colleagues.

Display effective team working skills to make a positive contribution, as a member or leader, to the work of a group.

Qualification structure:
The qualification consists of the following compulsory modules.

Compulsory Modules, 180 Credits:

Modelling, Regression and Machine Learning, 30 Credits.

Visual Data Analysis, 30 Credits.

Applied Data Analytics: Tools, Practical Big Data Handling, Cloud Distribution, 30 Credits.

Legal, Ethical and Security Aspects of Data Management, 30 credits.

Individual Data Science Project, 60 credits.

Assessment:
The technical skills will be assessed throughout the year in a series of formative and summative coursework. Every week, learners will be given lab tasks designed to match the content covered in the lecture. These tasks are expected to be completed during the lab and learners will receive timely feedback assessment.

Summative coursework is planned for every two months, after the completion of each major module component. The type of work will depend on the module finished so it could range from development work after a technical component or research/report after a non-technical component such as design and evaluation. These works require considerably more effort than the formative coursework and can give learners a clear indication of their performance on each major module component.

Similarities:

The Middlesex University London (MUL) qualification is comparable to the South African (SA) qualification since both qualifications are offered over a period of one-year full-time study.

Both qualifications carry a weighting of 180 credit points.

Both the MUL qualification and the SA qualification allows learners who completed an Honours degree in the related field.

Both qualifications are designed to give learners the skills to step into a career as a Data Scientist in a wide range of industries and companies.

The assessment strategy for both the MUL and SA qualifications is underpinned by integrated assessment strategies which are reflective and continuous and include formative and summative assessment methods.

Both qualifications articulate to Doctoral degree in data science and related fields.

Differences:

The MUL qualification is registered at EQF Level 7 whereas the SA qualification is registered at NQF Level 9.

The MUL qualification consists of both compulsory coursework and a research project while the SA qualification consists of only a Dissertation and no coursework.

Country: United States of America
Institution: South Dakota State University
Qualification Title: Master of Science in Data Science
Duration: One year Full-time
Credits: 30

Entry Requirements:
Applicants for graduate study for the M.S. in Data Science must have:

Baccalaureate degree from an institution of higher education with full regional accreditation for that degree.

The applicant must have an undergraduate grade point average of at least 3.0 on a 4.0 scale.

Purpose/Rationale:
The South Dakota State University (SDSU) qualification is a one-year program that provides graduates with the statistical, mathematical, and computational skills needed to meet the large-scale data science challenges of today's professional world. The curriculum incorporates current techniques in statistics, operations research, predictive modelling, data mining, forecasting, big data programming and management, and data visualization. The qualification focuses on the application and interpretation of modern data analysis techniques of known value in both the private and public sectors.

Exit Learning Outcomes:
On completion of the qualification, qualifying learners will be able to:

Analyse the foundations of data science, with a specific focus on the interplay between computational complexity and statistical efficiency.

Explore and understand the ethical implications of using data and statistical models for making decisions.

Perform exploratory data analysis and statistical inference in appropriate application areas.

Apply the methods in artificial intelligence, machine learning, or pattern recognition to real data.

Proficiently use at least one statistical software among R, SAS, PYTHON, STATA, JMP, or SQL.

Appropriately communicate the results of their analysis to various audiences.

Qualification structure:
The qualification consists of the following compulsory modules.

Compulsory Modules, 18 Credits:

Data Warehousing and Data Mining, 3 Credits.

Programming for Data Analytics, 3 Credits.

Big Data Analytics, 3 Credits.

Statistical Programming, 3 Credits.

Modern Applied Statistics I, 3 Credits.

Modern Applied Statistics II, 3 Credits.

Operations Research (COM) 3 Credits.

Nonparametric Statistics (COM) 3 Credits.

Predictive Analytics I Credits: 3 Credits.

Time Series Analysis (COM) 3 Credits.

Annual Evaluation:
A formal review of the progress of degree completion, including performance in coursework and completion of thesis goals, should take place annually and be standard in format and timing for all learners within a program. The review will include a written evaluation portion, including one opportunity for the learner to rebut, followed by a meeting for a discussion. The written portion should take place using a program-standard format and include a synopsis of progress made the previous year, as well as guidance for the upcoming year.

Examinations:
Master's programs require the completion of a capstone component. The capstone component must be conducted under the supervision of no fewer than two graduate faculty and approved through normal curriculum processes. Capstone components must be associated with graduate coursework. The capstone component for option A is the final oral exam. The capstone component for options B, C, and D may include a research paper, oral exam, portfolio of the learner's work or other suitable exercises.

Similarities:

The South Dakota State University (SDSU) and the South African (SA) qualifications prepare learners to use the power of data to help organizations of all kinds, and society makes better decisions.

Both the SDSU and SA qualifications take one-year full-time to complete.

Both qualifications articulate to Doctoral degree in Data Science and related field.
Differences:

The SDSU qualification consists of coursework whereas the SA qualification is only research-based and has no coursework.

The SDSU qualification carries a weighting of 30 credit points while the SA qualification has 180 credits.

Country: United States of America
Institution: University of the Pacific
Qualification Title: Master of Science in Data Science
Credits: 32
Duration: Two years full time

Entry requirements:
Prerequisite entry requirements include:

Bachelor's degree.

GPA of 2.65 or above.

Educational qualifications and/or work experience in:

Statistics.

Linear Algebra.

Computer programming (any language, although Python and R are preferred).

Basic calculus (derivatives).

In addition, international learners must also have:

The US equivalent of a GPA of 2.65 or above.

TOEFL (or equivalent) English language proficiency. A minimum score of 90 or a score of at least 550 (213 on the computer-based test) is required.

Official, course-by-course evaluation of their transcripts with an overall U.S. GPA equivalent from one of the agencies accepted by the University.

Purpose/Rationale:
The qualification prepares graduates for careers in data analytics and related fields. This is a science (as opposed to business) based program that is focused on developing learners' math foundation in statistics and linear algebra, and computer programming to prepare them for coursework in topics like machine learning, time series analysis, customer analytics, and data visualization. This STEM-designated program uses a hybrid approach to learning with most courses requiring attendance in both in-person and online class sessions. It consists of four semesters spread over two academic years, during which a minimum of 32 units must be completed for degree conferral.

Data scientists are in high demand. Every industry realizes that data is the key to future success, and organizations are looking for data scientists with deep knowledge of analytics. The qualification will prepare learners to be the data scientists of tomorrow by developing learners' math foundation in statistics and linear algebra, and learning skills in the areas of data preparation, data modelling, predictive modelling, and a variety of data science / analytic solutions areas such as customer analytics, fraud detection and healthcare analytics.

Upon the completion of the qualification, graduates will be able to:

Extract value from data to assist organizations in understanding past performance, predicting future events, and optimizing processes.

Apply the methods of data wrangling, analytic programming, data mining, quantitative methods, and modelling, to prepare very large data sets for analysis.

Design and develop practical data-oriented solutions using modern analytic techniques such as machine learning, time series analysis, and clustering.

Apply the scientific method to develop and test hypotheses using mathematical and statistical principles.

Conduct compelling communications through informative visualizations and effective presentation skills.

Analyze various forms of data (e.g., numerical, categorical, textual, objects, etc.) using appropriate mathematical and/or machine learning techniques.

Apply modern programming and data engineering skills, extract data from files, databases, or online resources, and transform it for appropriate analysis.

Effectively communicate results in a format that is appropriate to the audience, via written, oral, and graphical media.

Qualification structure:
Learners must complete a minimum of 32 units with a Pacific cumulative grade point average of 3.0 to earn the Master of Science in data science degree. The qualification consists of the following compulsory and elective modules.

Compulsory Modules, 29 Credits:

Linear Algebra for Data Science, 2 Credits.

Frequentist Statistics, 1 Credit.

Bayesian Statistics, 1 Credit.

Research Methods for Data Science, 1 Credit.

Software Methods for Data Science, 1 Credit.

Analytics Computing for Data Science, 2 Credits.

Data Engineering for Data Science, 2 Credit.

Machine Learning for Data Science, 2 Credits.

Data Wrangling, 1 Credit.

Introduction to Data Visualization, 1 Credit.

Dynamic Visualization, 1 Credit.

Analytics Storytelling for Data Science, 1 Credit.

Relational Databases, 1 Credit.

NoSQL Databases, 1 Credit.

Healthcare Case Studies, 1 Credit.

Emphasis Case Studies, 1 Credit.

Capstone Project, 6 Credits.

Weekly Hot Topics, 3 Credits.

Elective Modules, 3 Credits (Select three of the following options):

Consumer Analytics, 1 Credit.

Sentiment Analysis and Opinion Mining, 1 Credit.

Time Series Analysis, 1 Credit.

Advanced Machine Learning, 1 Credit.

Fraud Detection, 1 Credit.

Customer Analytics, 1 Credit.

Text Mining, 1 Credit.

Similarities:

The University of the Pacific (UoP) and the South African (SA) qualifications prepare learners for the knowledge and skills required to become data scientists.

Both qualifications articulate vertically to a Doctoral degree in data science or related fields.

Differences:

The UoP qualification requires applicants who hold a bachelor's degree in Data Science whereas the SA qualifications require applicants who hold Honour's degree in data science.

The UoP qualification consists of both coursework and a research project whereas the SA qualification consists of only a dissertation and no coursework.

The SA qualification will span over one year if taken full-time and 18 months if taken part-time whereas the UoP qualification takes two years of full-time study.

The SA qualification carries 180 credits while the UoP qualification carries 32 credit units.

Country: Finland
Institution: University of Helsinki
Qualification Title: Master of Science in Data Science
Duration: Two years Full-time
Credits: 120 ECTS Credits

Entry Requirements:
The qualification will accept applicants who:

Have a first-cycle degree in computer science, statistics or mathematics, a first-cycle degree in an applied field of data science or another applicable degree.

Have completed at least 10 credits in programming, algorithms, and data structures, at least 5 credits in probability and statistics and at least 10 credits in linear algebra, differential calculus, integral calculus or other studies in mathematics.

Purpose/Rationale:
Data science combines computer science and statistics to solve exciting data-intensive problems in industry and many fields of science. As data is collected and analysed in all areas of society, the demand for professional data scientists is high and will grow even further. In the interdisciplinary master's in Data Science, learners are trained to work in data-intensive areas of industry and science, with the skills and knowledge needed to construct solutions to complex data analysis problems.

The qualification allows for specialisations either in the core areas of data science- machine learning and algorithms, infrastructure, and statistics or in its applications. Learners can focus on the development of new models and methods in data science, supported by data science research.

Upon graduating from the qualification, learners will have a solid knowledge of the central concepts, theories, and research methods of data science as well as applied skills. Learners will be able to:

Analyse and apply general computational and probabilistic principles underlying modern machine learning and data mining algorithms.

Apply various computational and statistical methods to analyse scientific and business data.

Assess the suitability of each method for the purpose of data collection and use.

Implement state-of-the-art machine learning solutions efficiently using high-performance computing platforms.

Undertake creative work, making systematic use of investigation or experimentation, to discover new knowledge.

Report results in a clear and understandable manner.

Analyse scientific and industrial data to devise new applications and support decision-making.

Qualification structure:
The qualification consists of 120 credits divided into the following compulsory and elective data science modules, and other modules.

Compulsory Modules:

Introduction to Data Science.

Introduction to Machine Learning.

Bayesian Data Analysis.

Distributed Data Infrastructures.

Statistical Data Science (substitutes Bayesian Inference).

Data Science Project I, 5 Credits.

Academic Skills for Data Science.

Elective Modules: Specialisation studies:
Learners can specialise either in the core areas of data science - algorithms, infrastructure, and statistics or in its applications.

In addition to mainstream data science topics, the qualification offers two largely unique opportunities for specialisation: the data science computing environment and infrastructure, and data science in natural sciences, especially physics. Learners must select at least four and at most eleven elective courses from the list of data science specialization courses below (20-55 credits).

Machine Learning and Algorithms.

Statistical Data Science.

Data Science Infrastructures.

Computers and Cognition.

Interdisciplinary Data Science.

Other Studies:
In addition to the above-required courses, the learner can include other courses in the degree as well, for up to 35 credits. Other studies include computer science, statistics and mathematics, language technology, digital humanities, life science informatics and computational social sciences.

Thesis work, 30 credits.
Master's thesis, 30 credits.
Articulation:
After completing the qualification, learners can apply for doctoral studies.

Similarities:

The University of Helsinki (UoH) qualification and the South African (SA) qualifications will equip learners with solid knowledge of the central concepts, theories, and research methods of data science as well as applied skills.

Both the UoH and SA qualifications aim at training learners to work in data-intensive areas of industry and science, with the skills and knowledge needed to construct solutions to complex data analysis problems.

Learners graduating from both qualifications will develop the same graduate attributes as data scientists.

Both qualifications allow learners to progress into Doctoral degree in data science or related fields.

Differences:

The UoH qualification is offered over a period of two years full-time study while the SA qualification is completed full-time over one year.

The UoH carries 120 credits whereas the SA qualification has 180 credits.

To study the UoH qualification, learners must have a first cycle bachelor's degree in data science and related fields whereas the SA qualification requires applicants who hold an Honour's degree in data science or equivalent qualification.

The SA qualification is research-based only and has no coursework whereas the UoH qualification consists of coursework and a research project.

ARTICULATION OPTIONS

This qualification allows possibilities for both vertical and horizontal articulation.

Horizontal Articulation:

Master of Science in e-Science, NQF Level 9.

Master of Science in Statistics, NQF Level 9.

Master of Computing, NQF Level 9.

Master of Science in Computer Science, NQF Level 9.

Vertical Articulation:

Doctor of Science, NQF Level 10.

Doctor of Philosophy in Computer Science, NQF Level 10.

Doctor of Philosophy in Computer and Information Sciences, NQF Level 10.

Doctor of Philosophy in Statistics, NQF Level 10.

MODERATION OPTIONS

N/A

CRITERIA FOR THE REGISTRATION OF ASSESSORS

N/A

NOTES

N/A

LEARNING PROGRAMMES RECORDED AGAINST THIS QUALIFICATION:

NONE

PROVIDERS CURRENTLY ACCREDITED TO OFFER THIS QUALIFICATION:

This information shows the current accreditations (i.e. those not past their accreditation end dates), and is the most complete record available to SAQA as of today. Some Primary or Delegated Quality Assurance Functionaries have a lag in their recording systems for provider accreditation, in turn leading to a lag in notifying SAQA of all the providers that they have accredited to offer qualifications and unit standards, as well as any extensions to accreditation end dates. The relevant Primary or Delegated Quality Assurance Functionary should be notified if a record appears to be missing from here.

NONE