SAQA

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SOUTH AFRICAN QUALIFICATIONS AUTHORITY

REGISTERED QUALIFICATION:

Master of Applied Data Science

SAQA QUAL ID	QUALIFICATION TITLE
119505	Master of Applied 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		Information Technology and Computer 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 Applied Data Science is to advance professionals in various fields related to competitive intelligence, with the competences to critically analyse and interpret data and information for tactical and strategic business decision making. Furthermore, it will provide the learner with advanced knowledge and skills to enable businesses to create a competitive advantage using data while meeting the challenges of the fourth industrial revolution (4IR). The learner will study a wide range of modules bringing together the knowledge areas of Information and Knowledge Management, Applied Information Systems and Marketing Management which will affords them the mastery of fundamental concepts in data science. Learners will also work on projects that require use of data sets to apply knowledge and skills.

Upon completion of this qualification, learner should be able to:

Analyse organisational data and make recommendations for tactical and strategic decisions

Determine information gaps and the best use of data for decisions to create a completive advantage for an organisation

Meet the challenges of the 4th Industrial Revolution by effectively processing and interpreting big data

Apply appropriate analytics models and techniques to obtain customer insight and market trends

Conduct research on a specific topic by following the correct methodology and produce a research report.

Rationale:
The Master of Applied Data Science, inter-disciplinary in nature, necessitated the new demands of the fourth industrial revolution (4IR). The qualification aims to ensure the development of a new generation of knowledge workers conversant with the best practices in competitive intelligence that leverage data science and the benefits of applied information systems.

Provide details of the reasoning that led to identifying the need for the qualification.
Developments in the 4IR have given rise to high demand for skills in data science. This is mainly because data has become a significant asset that is enabling 4IR innovations that are benefiting both the public and private sectors, including innovations associated with artificial intelligence and internet of things (IoT). The rapid rise in demand for workers skilled in data science has resulted in a global shortfall in supply and has placed a great premium on such skills. Addressing this shortage in supply is necessary to enhance the capacity of industries and economies to leverage fully the benefits of 4IR innovations.

Wide consultations with industry partners including members of the School of Consumer Intelligence and Information Systems Industry Advisory Board were held in developing this qualification. The consultations were aimed at ensuring that the qualification meets the knowledge and skills needs of industry. During the consultations, a growing demand for data scientists was identified across all industries in South Africa, the continent and beyond. The specific need for skilled workers capable of using data for competitive intelligence was highlighted. The Master of Applied Data Science has been developed to among other objectives meet this specific need.

Identify the range of typical learner and indicate the occupations, jobs, or areas of activity in which the qualifying learner will operate.
The typical range of targeted learner would be professionals in various fields related to competitive intelligence. The qualification will help provide learners with the career development and upskilling opportunities needed in the age of the 4IR. This qualification will also offer the opportunity for learner to further develop advanced conceptual thinking skills, and the problem finding and problem-solving skills, with which to innovatively address complex issues within organisations.

The qualification will provide benefits to society and the economy by contributing to the development of scarce skills relevant for continued competitiveness of industry and the country in this age for the 4IR and beyond. The qualification will also help position the institution as a hub of 4IR relevant knowledge creation and sharing.

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 may be applied for access, credits from modules and credits for or towards the qualification.

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.

RPL for exemption from modules

Learners may apply for RPL to be exempted from modules that form part of the qualification. For a learner to be exempted from a module, the learner needs to provide sufficient evidence in the form of a portfolio that demonstrates that competency was achieved for the learning outcomes that are equivalent to the learning outcomes of the module.

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 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 and/or elective modules at National Qualifications Framework Level 8 and 9, totalling 180 Credits.

Compulsory Modules Level 8, 30 Credits:

Data Exploration, 15 Credits.

Predictive Analytics, 15 Credits.

Compulsory Modules, Level 9, 120 Credits.

Marketing Analytics, Credit 15,

Marketing decision making models, 15 Credits.

Competitive Intelligence,15 Credits.

Research Methodology for Applied Data Science, 15 Credits.

Limited Scope Research Project: Applied Data Science, 60 Credits.

Elective Modules, Level 9, 30 Credits (Choose two of the following options):

Advanced Data Analytics, 15 Credits.

Expert Systems and Applications, 15 Credits.

Consumer analytics and big data, 15 Credits.

Strategic Information Management, 15 Credits.

EXIT LEVEL OUTCOMES

1. Analyze organizational data and make recommendations for tactical and strategic decisions.
2. Determine information gaps and the best use of data for decisions to create a completive advantage for an organization.
3. Meet the challenges of the 4th industrial revolution by effectively processing and interpreting big data.
4. Apply appropriate analytics models and techniques to obtain customer insight and market trends.
5. Conduct research on a specific topic by following the correct methodology and produce a research report.

ASSOCIATED ASSESSMENT CRITERIA

Associated Assessment Criteria for Exit Level Outcome 1:

Conduct customer analytics and trend analysis by using appropriate analytics techniques.

Implement predictive analysis to forecast market demand.

Correctly use analytic outcomes to allocate resources of the organisation.

Analyse efficiency of digital marketing strategies can be accurately to determine the Return on Investment (ROI).

Associated Assessment Criteria for Exit Level Outcome 2:

Identify data gaps in a scenario and comprehensively present suggestions to overcome these gaps.

Apply factor analysis and Search Engine Marketing (SEM) on data on relevant tasks.

Assess and apply the solution model using cross-validation, Return on Capital (ROC), Mean squared error (MSE) against organizational needs.

Apply various competitive intelligence tools and techniques.

Associated Assessment Criteria for Exit Level Outcome 3:

Accurately identify strategic areas within an organization that may benefit from data mining and processing.

Create and implement constrained optimization models to solve challenging business problems.

Clearly argue importance of competitive intelligence for an organization in the 4th industrial revolution.

Accurately construct artificial neural network (ANN), that is a multilayer perceptron (MLP) from given data.

Associated Assessment Criteria for Exit Level Outcome 4:

Conduct customer and trend analysis using appropriate analytics techniques.

Accurately apply models for decision making to address a business problem and reflect on the applications of these models.

Correctly apply clustering techniques to segment consumer and industrial markets.

Use cluster analysis models can be to review a segmented market.

Associated Assessment Criteria for Exit Level Outcome 5:

Write, present, and defend a complete research proposal based on a specific topic/context.

Use appropriate methods or techniques to develop solutions to problems.

Draw research conclusions that are clearly supported by findings.

Produce a comprehensive and well-structured industry research project report or research article based on a specific topic.

INTERNATIONAL COMPARABILITY

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

Country: United States of America
Institution: University of Michigan
Qualification Title: Master of Science in Data Science
Duration: One-year full time

Entry Requirements:
Applicants complete a bachelor's degree from a United States (U.S.) college or university accredited by a regional accrediting association; or complete an international degree that is equivalent to a U.S. bachelor's degree from a college or university recognized and approved by the Ministry of Education or Commission responsible for higher education in the country where the degree is earned.

Purpose/Rationale:
The demand for statisticians is at an all-time high. Statistics and data science are necessary components in all applied sciences, businesses, medicine, and even many everyday tools and tasks. With the huge amounts of data collected in the world every second, statisticians at all levels are needed to help make sense of this data, quantify the uncertainty, develop new tools and methodologies, and analyze their properties. The Master's in Data Science is designed to require every learner to receive balanced training in statistical skills and computational skills, combining the educational strengths of the four departments. Graduates of this qualification are expected to understand data representation and analysis at an advanced level.

Data Science is often viewed as the confluence of Computer and Information Sciences, Statistical Sciences, and Domain Expertise. These three pillars are not symmetric: the first two together represent the core methodologies and the techniques used in Data Science, while the third pillar is the application domain to which this methodology is applied.

With the Master of Science in Data Science qualifying learners will be able to:

Identify relevant datasets.

Apply the appropriate statistical and computational tools to the dataset to answer questions posed by individuals, organizations, or governmental agencies.

Design and evaluate analytical procedures appropriate to the data.

Implement these efficiently over large heterogeneous data sets in a multi-computer environment.

Qualification structure:
The UM qualification requirements include at least 10 courses for a total of 30 credit hours. It consists of the following compulsory and elective modules.

Compulsory Modules:

Statistical Learning, I: Regression.

Statistical Learning II: Multivariate Analysis.

Principles and Practices in Effective Statistical Consulting.

Probability and Distribution Theory.

Statistical Inference.

Elective Modules (Select five modules from the following options):

Computational Methods in Statistics and Data Science.

Applied Probability.

Introduction to Bayesian Data Analysis.

Applied Statistics II.

Computational Methods and Tools in Statistics.

Data Science and Analytics using Python.

Statistics for Financial Data.

Discrete State Stochastic Processes.

Modelling and Analysis of Time Series Data.

Reliability.

Probabilistic modelling in Bioinformatics.

Bayesian modelling and Computation.

Introduction to Nonparametric Statistics.

Design of Experiments.

Methods and Theory of Sample Design.

Statistical Computing.

Capstone:

Principles and Practices in Effective Statistical Consulting.

Directed Reading.

Special Topics.

Directed Study.

Independent Study.

Big Data Analytics

Reading in Biostatistics.

Modern Statistical Methods in Epidemiologic Studies.

Analysis of Biostatistical Investigations.

Similarities:

The University of Michigan (UM) and the South African (SA) qualifications are offered over a period of one-year full time.

Both the UM and SA qualifications will help learners to attain the necessary skills and knowledge to:

> Identify relevant datasets, apply the appropriate statistical and computational tools to the data set to answer questions posed by individuals, organizations, or governmental agencies.

> Design and evaluate analytical procedures appropriate to the data and implement these efficiently over large heterogeneous data sets in a multi-computer environment.

Both the UM and SA qualifications consist of compulsory and elective modules.

Both qualifications consist of a compulsory research project.

Differences:

The UM qualification requires prospective applicants to have completed at least 25 units of graduate-level coursework in the Data Science program. Of these 25, 18 must be at the advanced graduate level. Students cumulative Grade Point Average (GPA) must be 3.00 whereas the SA qualification requires applicants who completed the Honour's degree in Data Science.

The UM qualification carries a weighting of 30 unit of credits whereas the SA qualification has 192 credits.

Country: Netherlands
Institution: University of Amsterdam
Qualification: Master's in information science: Data Science Track.
Credits: 60 European Credits Transfer System (ECTS)
Duration: 12 months

Entry Requirements:
The qualification requires applicants with an academic bachelor's degree with data and technology.
In addition, the UA qualification requires applicants to have an overall grade point average (GPA) equivalent to at least:

3.0 (American system).

2.1 (a second class upper/division one degree in the British system).

C (ECTS-system).
The GPA is the average of the bachelor's course grades weighed by course/study load.

Purpose/Rationale:
In a data rich world, data science is gaining a central position with an increasing potential value for businesses, science, and society. Data scientists are needed to give meaning to the sea of data that surrounds us.

The qualification is intended for learners who:

Are interested in the ways people interact with (new) technology and media, and how they are supported, hampered, and influenced by them.

Want to analyse systems for the supply, storage, and communication of information.

Want to make connections between the people responsible for developing technological solutions and corporate management.

Want to translate user demands into innovative solutions.

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

Compulsory Modules

Fundamentals of Data Science, 6 EC.

Statistics, Simulation, and Optimisation, 6 EC.

Applied Machine Learning, 6 EC.

Big Data, 6 EC.

Data Systems Project, 6 EC.

Master's thesis, 18 EC.

Elective Modules (Select any two from the following options):

Dynamics in Business and IT, 6 EC.

Knowledge Engineering, 6 EC.

System Dynamics, 6 EC.

Technology for Games, 6 EC.

Data, Sensors, and Complex Services, 6 EC.

Data-Driven Business Innovation and Entrepreneurship, 6 EC.

Decision-making in a Complex Adaptive System Setting, 6 EC.

Information Retrieval, 6 EC.

Information Visualization, 6 EC.

Perspectives on Information and Society, 6 EC.

Policy Making and Rule Governance, 6 EC.

Similarities:
The University of Amsterdam (UA) qualification is comparable to the South African (SA) qualification in the following criteria.

Both the UA and SA qualifications are offered over a period of one-year full time.

The UA and SA have been designed for learners to become an all-round data scientist who knows how to go through all the steps in a data-driven project; from how to approach a business or societal problem from a data-analytical perspective to the final implementation of data science solutions, while also understanding the organisational and social implications these solutions may have.

Both the UA qualification and the SA qualification have compulsory and elective modules.

Both UA qualification and the SA qualification offer similar module that is Big Data, Applied Machine Learning, and research project.

Both qualifications progress into the Doctoral studies in cognate field.

Differences:

UA qualification requires applicants with an academic bachelor's degree with data and technology while the SA qualification requires applicants who hold the Honour's degree in Data Science.

The UA qualification has 60 ECTS whereas the SA qualification has 180 credits.

Country: United States of America (USA)
Institution: University of California, Berkeley
Qualification Title: Master of Information and Data Science
Duration: 12 months.
Credits: 27 Units

Entry Requirements:
To be eligible for the online master's programs, applicants must meet the following requirements:

A bachelor's degree. The recognized equivalent to a bachelor's degree is also accepted, if earned from an accredited institution.

Applicants should have a superior scholastic record, normally well above a 3.0 GPA.

Purpose/Rationale:
The qualification features a multidisciplinary curriculum that draws on insights from the social sciences, computer science, statistics, management, and law. Data Scientists develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software; apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from large structured and unstructured datasets; visualize, interpret, and report data findings and may create dynamic data reports

Graduates of the qualification will be able to:

Imagine new and valuable uses for large datasets.

Retrieve, organize, combine, clean, and store data from multiple sources.

Apply appropriate data mining, statistical analysis, and machine learning techniques to detect patterns and make predictions.

Design visualizations and effectively communicate findings; and

Understand the ethical and legal requirements of data privacy and security.

The career pathways within this discipline are wide-ranging and diverse. In addition to traditional titles such as data engineer, data analyst, and data scientist, those pursuing a career in data science may leverage their skills and expertise in the areas of marketing, finance, accounting, operations, or supply chain in specialized departmental analytics.

Qualification structure:
The University of California, Berkeley (UCB) curriculum includes research design and applications for data and analysis, statistics for data science, data engineering, applied machine learning, data visualization, and data ethics. The qualification consists of the following compulsory modules.

Compulsory Modules:

Python for Data Science.

Research Design and Application for Data and Analysis.

Statistics for Data Science.

Fundamentals of Data Engineering.

Applied Machine Learning.
Advanced Modules:

Experiments and Causal Inference.

Behind the Data: Humans and Values.

Deep Learning in the Cloud and at the Edge.

Capstone Project:
Learner will complete a capstone by executing a culminating project that integrates the core skills and concepts learned throughout the qualification. The capstone combines the technical, analytical, interpretive, and social dimensions required to design and execute a full data science project. Students will learn integral skills that prepare them for long-term professional success in the field.

Similarities:

The University of California, Berkeley (UCB) and the South African (SA) and qualifications are offered over a period of one-year full time.

Both the UCB and SA qualifications are designed to educate data science leaders. The professional qualifications prepare learners to derive insights from real-world data sets, use the latest tools and analytical methods, and interpret and communicate their findings in ways that change minds and behaviours.

The UCB and SA qualifications feature a project-based approach to learning and encourage the pragmatic application of a variety of different tools and methods to solve complex problems.

Differences:

The UCB qualification requires applicants who hols bachelor's degree or recognized equivalent to a bachelor's degree while the SA qualification requires applicants who completed Honour's degree.

The SA qualification carries a weighting of 180 credits whereas the UCB qualification has 27 units.

The UCB qualification is fully online while the SA qualification follows a blended learning approach.

The UCB qualification offers only compulsory qualification, while the SA qualification offers both compulsory and elective modules.

ARTICULATION OPTIONS

This qualification allows possibilities for both vertical and horizontal articulation.
Horizontal Articulation:

Master of Science in Computer Science, NQF Level 9.

Master of Science in Applied Mathematics, NQF Level 9.

Master of Science in Data Science, NQF Level 9.

Vertical Articulation:

Doctor of Philosophy in Applied Sciences, NQF Level 10.

Doctor of Philosophy in Applied Mathematics, NQF Level 10.

Doctor of Science in Computer Science, 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.

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