SAQA

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SOUTH AFRICAN QUALIFICATIONS AUTHORITY

REGISTERED QUALIFICATION:

Bachelor of Science in Business Analytics

SAQA QUAL ID	QUALIFICATION TITLE
111118	Bachelor of Science in Business Analytics
ORIGINATOR
North West University
PRIMARY OR DELEGATED QUALITY ASSURANCE FUNCTIONARY			NQF SUB-FRAMEWORK
-			HEQSF - Higher Education Qualifications Sub-framework
QUALIFICATION TYPE	FIELD		SUBFIELD
National First Degree	Field 10 - Physical, Mathematical, Computer and Life Sciences		Physical Sciences
ABET BAND	MINIMUM CREDITS	PRE-2009 NQF LEVEL	NQF LEVEL	QUAL CLASS
Undefined	360	Not Applicable	NQF Level 07	Regular-Provider-ELOAC
REGISTRATION STATUS		SAQA DECISION NUMBER	REGISTRATION START DATE	REGISTRATION END DATE
Registered		EXCO 0324/24	2024-07-01	2027-06-30
LAST DATE FOR ENROLMENT		LAST DATE FOR ACHIEVEMENT
2028-06-30		2033-06-30

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

The purpose of the Bachelor of Science in Business Analytics is to provide a well-rounded, broad education that equips graduates with the knowledge base, theory, and methodology of the Business Analytics discipline, and enables them to demonstrate initiative and responsibility in an academic or professional context. Through this qualification, graduates will be trained to become business analysts. Graduates will be provided with graduate-level knowledge, specific skills and applied competence in the Business Analytics discipline that provides opportunities for continued personal intellectual growth, gainful economic activity in the private, public and academic spheres and rewarding contributions to society. The Bachelor of Science in Business Analytics links to the general and vocational pathways leading to further education and occupations in the workplace and sectors. Typical occupations/professions in which the qualifying learner will operate include Data Scientist, Decision Support Analyst, Financial Statistician, and Data Mining Analyst. These jobs will mainly be in large companies in the financial services sector.

Rationale:
The rise of big data and digital transformation in finance has created a critical need for qualified business analysts in South Africa. These professionals combine skills in data analytics, machine learning, finance, and programming to extract insights from vast financial datasets. A formal qualification in financial data science ensures analysts are equipped to build predictive models, automate processes, detect fraud, and enhance decision-making through data-driven approaches. With sectors like banking, insurance, asset management, and fintech increasingly reliant on real-time data, qualified data science analysts are essential for delivering innovative financial solutions, improving customer experience, and maintaining a competitive edge in a rapidly evolving digital economy.

Economically, the integration of financial data science supports smarter, faster, and more efficient financial systems. These analysts enable businesses to optimise pricing strategies, personalise services, and manage risks more accurately, ultimately boosting productivity and profitability. Their work also helps detect financial crimes, enhance regulatory compliance, and improve macroeconomic forecasting. Developing this skill base locally creates high-value jobs, reduces dependency on foreign expertise, and cultivates a tech-savvy workforce ready to lead in Africa's digital financial future. As data becomes the foundation of financial innovation, business analysts will play a pivotal role in driving inclusive, technology-led economic growth in South Africa.

The rationale of the qualification is to provide graduates with the knowledge, specific skills and applied and theoretical competence in Business Analytics and also to provide opportunities for continued personal intellectual growth, to become economically active and deliver valuable contributions to society. The qualification will further provide South Africa (and other countries) with graduates in Business Analytics to ensure that the leadership base of innovative and knowledge-based economic and scholarly activity is widened. The third rationale of the qualification is to provide South Africa (and other countries) with graduates in this field of study who can perform a constructive role as intellectual leaders in their society.

Professional registration, membership or licensing as required by recognised professional bodies, and proof of collaboration with the relevant stakeholders are linked to three learning pathways: general, vocational, and professional. The general pathway links to further education, the vocational pathway leads to occupations in the workplace and sectors, and the professional pathway leads to registration with statutory bodies. The qualification in Business Analytics links to the general and vocational pathways, leading to further education and occupations in the workplace and sectors.

Typical occupations/professions in which the qualifying learner will operate include Data Scientist, Decision Support Analyst, Financial Statistician, and Data Mining Analyst. These jobs will mainly be in large companies in the financial services sector.

LEARNING ASSUMED TO BE IN PLACE AND RECOGNITION OF PRIOR LEARNING

Recognition of Prior Learning (RPL):
In accordance with the 2019 amended National Policy and Criteria for the Implementation of Recognition of Prior Learning:

RPL refers to the principles and processes through which the prior knowledge and skills of a person are made visible, mediated and rigorously assessed and moderated for the purposes of alternative access and admission, recognition and certification, or further learning and development.

RPL must be conducted in a valid, reliable, transparent and equitable way.

RPL decisions must be cognisant of statutory requirements and standard selection criteria in the case of professional qualifications.

RPL may be used to grant access to a qualification, or for purposes of advanced standing or exempting a candidate from a module(s) of a particular academic qualification on the grounds of the outcomes of the module(s) having been attained as evident from the RPL portfolio submitted by the candidate and evaluated by the panel of academic experts in the faculty concerned.

RPL will be applied according to the institution's General Academic Rules (2019:3-4), Rules 1.6 and 1.7 and Rules for Teaching, Learning and Assessment (2019), as follows:

Admission and advanced standing on the grounds of recognition of prior learning

An executive dean may, by means of the recognition of prior learning (RPL) in accordance with the university's Rules for Teaching, Learning and Assessment (2019), grant a learner who does not meet the minimum admission requirements admission to a qualification of a qualification, or grant advanced standing to a learner by exempting the learner from the recognised modules required for the completion of a particular qualification. The faculty must ensure that quality assurance processes that address the specificities of the RPL process (including applications, assessment, and reporting and management systems) are implemented; and that administrative and support systems, both prior and subsequent to RPL assessment, are in place.

Only proven informal or non-formal learning may be taken into consideration by means of RPL; the process of equivalence-setting between such learning and formal modules must be documented, and its outcome must be recorded on the official learner record.

Where a learner was granted an exemption for one or more modules as a consequence of RPL, the remaining HEMIS credits required for the qualification must be obtained by completing the relevant qualification. The maximum portion of a qualification from which a learner may be exempted by means of RPL is fifty per cent of the credits of the full qualification. Not more than 10% of a cohort of learners in an academic qualification can be admitted through an RPL process.

Credit recognition and transfer

Definition and application

Credit recognition and transfer entails a documented process by means of which a learner receives credit for specified prescribed modules or coursework components of a formal qualification offered by the university, allowing the adjustment of the minimum study period for the completion of a qualification offered by the university within the framework of General Academic Rules 2 to 5.

Credit recognition and transfer may only be granted for credits obtained during studies towards a formal qualification, including credits obtained for modules taken for non-degree purposes.
For the purposes of credit recognition and transfer, distinctions are maintained between qualifications leading to a qualification that has been completed and those that have not been completed and between credits received in qualifications offered by the university and credits received from other recognised higher education institutions.
Credit recognition and transfer apply to:

A learner of the university who is registered for, but has not completed a qualification, and who wishes to change to a different qualification linked to the same qualification;

A learner of the university who is registered for, but has not completed a qualification, and who wishes to change to a different qualification linked to a different qualification;
A learner registered at another recognised higher education institution who has not completed the qualification at that institution, and who wishes to register for the same or a different qualification at the university.

A learner who has completed a qualification at the university or at another recognised higher education institution who wishes to enrol for a qualification leading to another qualification at the university, and qualifications that consist entirely or partially of coursework modules or components, but not qualifications that consist purely of a research component

Credit recognition and transfer can only be granted to a learner who meets the minimum admission requirements of the qualification and qualification for which the learner wishes to register. The procedure and form of written application, and the conditions for credit recognition and transfer in formal academic qualifications and qualifications offered by a faculty must be provided for in the faculty rules. The executive dean of a faculty has the final discretion regarding the granting of credit recognition and transfer and is responsible for overseeing the procedure provided for in rule 1.7.3 and for ensuring appropriate record-keeping thereof.

Entry Requirements:

National Senior Certificate, NQF Level 4, granting access to Bachelor studies.
Or

National Certificate Vocational, NQF Level 4, granting access to Bachelor studies.
Or

Senior Certificate, NQF Level 4.
Or

Higher Certificate in a cognate discipline.

RECOGNISE PREVIOUS LEARNING?

QUALIFICATION RULES

This qualification comprises compulsory and elective modules at National Qualifications Framework (NQF) Levels 5, 6 and 7, totalling 452 Credits.

Compulsory Modules, NQF Level 5, 120 Total Credits:

Introduction To Financial Mathematics, 12 Credits

Introduction To Computing and Programming, 12 Credits.

Introduction To Micro-Economics, 12 Credits.

Introductory Algebra and Calculus I, 12 Credits.

Descriptive Statistics and Inference, 12 Credits.

Introduction To Risk and Actuarial Science, 12 Credits.

Structured Programming, 12 Credits.

User Interface Programming I, 12 Credits.

Introductory Algebra and Calculus 2, 12 Credits.

Introductory Probability Theory, 12 Credits.

Elective Modules, NQF Level 5, 44 Credits (Learner must select three):

Financial Accountancy (Special), 16 Credits.

Financial Accounting, 16 Credits.

Academic Literacy Development - Afr, 12 Credits.

Academic Literacy Development - Eng, 12 Credits.

Financial Accounting (Special), 16 Credits.

Financial Accounting: Elementary Financial Reporting, Partner, 16 Credits.

Compulsory Modules, NQF Level 6, 132 Total Credits:

Object Oriented Programming, 16 Credits.

System Analysis and Design 1, 16 Credits.

Advanced Calculus 1, 8 Credits.

Linear Algebra 1, 8 Credits.

Probability And Sampling Theory, 16 Credits.

Numerical Methods, 8 Credits.

Data Structures and Algorithms, 8 Credits.

Linear Algebra 2, 8 Credits.

Statistical Inference and Data Analysis, 16 Credits.

Understanding The World of Economic and Management Science, 12 Credits

Basics Of Financial Mathematics, 16 Credits.

Compulsory Modules, NQF Level 7, 156 Total Credits:

Databases, 16 Credits.

Decision Support Systems 1, 16 Credits.

Artificial Intelligence, 16 Credits.

Linear Models I, 24 Credits.

Statistical Software and Applications I, 8 Credits.

Understanding The World of Business Ethics, 12 Credits.

Advanced Databases, 16 Credits.

Decision Supports Systems 2, 16 Credits.

Analysis Of Dependent Data, 16 Credits.

Statistical Software and Application 2, 16 Credits.

EXIT LEVEL OUTCOMES

1. Analyse and interpret financial statements.
2. Solve theoretical business analytics problems.
3. Solve basic problems using basic probability and statistical theory; use R/S-Plus and SAS and statistical (Software packages to run statistical procedures; apply optimisation techniques like least squares, Lagrangian multipliers, and constrained optimisation in general.
4. Use formal methods of statistical inference, apply techniques such as multiple regression, analysis of variance, sampling, and generalised linear models, apply and use Taylor's theorem, translate mathematical and statistical formulae into computer programming.
5. Code in Visual Basic and MS Excel, design and develop decision support systems using artificial intelligence technology, use MS Excel functionality to solve financial problems.
6. Translate mathematical and statistical formulae into computer programming code in JAVA, construct and analyse data structures, design and analyse computer systems and design database systems and to use SQL to extract information from database systems.

ASSOCIATED ASSESSMENT CRITERIA

The following Associated Assessment Criteria are assessed in an integrated manner across all the Exit Level Outcomes:

1. Apply an intermediate knowledge and logical understanding of key terms, theories and principles in the field of Financial Management.
2. Calculate interest; time value of money; present and future values; nominal and effective interest rates; and annuities and loans.
3. Integrate knowledge and understanding of mathematical modelling and analysis of financial instruments.
4. Apply mathematical formulae to price simple derivative securities.
5. Manage a team in an unfamiliar context in order to solve a problem in the field of capital markets modelling and analysis, monitoring the progress of the team and taking responsibility for task outcomes and application of appropriate resources where necessary.
6. Integrate knowledge and understanding of statistical distributions for risk modelling, with and without risk sharing, including the assumptions underlying different statistical models.
7. Identify and apply the most suitable statistical methods for a broad range of general insurance problems.
8. Apply problem-solving skills by analysing previously encountered problems and new and unfamiliar problems and use the computer language SAS (PROC IML) to apply these concepts practically.
9. Apply fundamental knowledge of the main areas of the computer science discipline, including system areas and application areas;
10. Apply fundamental knowledge of the main areas of structured programming, including the basic structure, data types and functions.
11. Structured programming constructs in designing, coding, debugging, testing and execution of applications in a procedural programming language.
12. Apply knowledge of programming constructs to develop algorithms to solve programming problems.
13. Describe the phases of the system development life cycle and apply the tools and techniques used during the System Initiation, Analysis and Design phases to an IT project in a chosen scenario; Describe the activities involved in project management and apply these in a group context across the stated life-cycle activities while running the IT project;
14. Integrated knowledge and critical understanding of, as well as an ability to correctly evaluate running times and complexity of algorithms, integrated knowledge and clear understanding of, as well as an ability to correctly evaluate and apply manipulation algorithms for data.

Integrated Assessment
Learner Academic Access, Retention, Attainment and Success (ARAS) at the institution require heightened responsiveness in provisions for social justice and equal opportunity, inclusive of but not limited to admission requirements, academic content, assessment approaches and methods, modes of provision, learner academic development and support opportunities, and non-academic support to learners. Teaching, learning and assessment should promote competency-based pedagogical practices that emphasise the promotion of flexible learning and thinking, self-directed and lifelong learning, local and global knowledge, diverse learning opportunities and assessment criteria to promote the academic success of a diverse learner body.

Curriculum compilation implies a logical coherence and continuous constructive alignment of:

The qualification purpose,

The qualification outcomes and associated assessment criteria,

The chosen learning components (core, fundamental and elective modules) of the curriculum,

The learning outcomes and selected content of each module,

Effective teaching, learning and assessment strategies,

Appropriate teaching and learning activities, and

Available and appropriate teaching and learning resources to support learner attainment.

Methods used for integrated assessment include continuous, formative (informal and formal), and summative assessments. Opportunities for both continuous formative and summative assessments are embedded in the learning design of the qualification throughout the year of study, in the form of written and practical assignments, portfolios, practical assessments, written and practical examinations and a research report to align with the associated outcomes and assessment criteria and the graduate attributes. Assessment focuses particularly on a critical, creative, and interpretative perspective of the various disciplines. At least one opportunity for integrated assessment, where the main purpose and key outcomes of the qualification/internal qualification are assessed, is included in the assessment activities for this qualification. Oral presentations of smaller integrated research projects or seminars in individual, as well as group contexts, may also be employed.

The qualification incorporates an inclusive and integrated approach to assessment; assessment practices are open, transparent, fair, valid, and reliable to ensure that learners are not disadvantaged during the process.

The module mark for all coursework modules is based on a combination of a module participation mark and an examination mark in the ratio determined in the applicable faculty rules, except where the module mark is based on continuous assessment or where no participation mark is required. In the case of continuous assessment modules, the module mark is based on the weighted average of the assessments prescribed for the module, according to the weights prescribed for the different assessment tasks. Taking into account the institution's A-rules 1.3.3, 1.14, 1.17 and 1.19.3, an undergraduate qualification is obtained when final verification and audit confirmation are given that a learner has successfully completed all the modules prescribed in the applicable faculty rules for the qualification of the qualification concerned.

Formative Assessment:
Examples of methods by which informal and formal formative assessments are done include class and online tests, discussions, feedback and critique during contact and blended learning sessions and practical sessions.

Examples of methods by which continuous assessment is done include portfolios, simulations, self- and group reflections, written assignments, written tests and online tests, case studies and case presentations, peer group evaluation, and face-to-face contact with learners.

Summative Assessment:
Examples of methods by which summative assessment is done include written exam papers, projects and portfolios of evidence during which all theoretical knowledge, problem-solving skills and practical skills are integrated and assessed.

INTERNATIONAL COMPARABILITY

Country 1: Singapore
Institution: Nanyang Technological University
Qualification Title: Bachelor of Business degree (Specialisation in BA)

The Bachelor of Business with a Specialisation in Business Analytics from Nanyang Technological University compares favourably with the South African qualification of the same name. Both qualifications share a similar purpose of integrating analytical, statistical, and technological skills to solve practical business problems using real-world data.

Admission to NTU requires a minimum mathematics requirement, reflecting the quantitative demands of the qualification. Similarly, the South African qualification expects a comparable mathematical foundation. Upon completion, both qualifications award a bachelor's degree and serve as a foundation for postgraduate study.

NTU's qualification is structured around 129 academic units and spans four years of full-time study, culminating in a direct honours degree. While the South African qualification may differ in credit system and duration, both degrees are undergraduate qualifications that equip learners for roles in business analytics, data science, finance, and operations.

Both curricula share a focus on applied analytics and practical implementation of theory. They integrate components from information systems, statistics, probability, and finance, and aim to produce graduates capable of interpreting complex data within business contexts. However, there are differences in modular structure, with NTU adopting a system that reflects Singapore's academic policies and institutional framework.

Although these qualifications align in outcomes and core content areas, the South African degree is distinctive in its contextual responsiveness. It is shaped by local industry needs, national policy frameworks, and regional economic challenges, making it uniquely relevant to the South African context while maintaining global comparability.

Country 2: England
Institution: Imperial College London
Qualification Title: BSc Economics, Finance and Data Science

The BSc in Economics, Finance and Data Science from Imperial College London compares closely with the South African Bachelor of Business degree with a Specialisation in Business Analytics in terms of thematic content, quantitative rigour, and academic level. Both qualifications are designed to develop learners' ability to apply mathematical and statistical knowledge to solve complex financial and business problems.

Entry into the Imperial qualification requires a strong background in mathematics, aligning with the minimum mathematics requirement also expected in the South African qualification. On successful completion, learners are awarded a bachelor's degree and become eligible for postgraduate study another shared feature between the qualifications.

The Imperial College London qualification is delivered over three years, whereas the South African counterpart may follow a different duration or credit model depending on national qualifications frameworks. Despite structural differences, both qualifications aim to prepare learners for the evolving demands of data-driven decision-making across business and financial sectors.

Curriculum themes are strongly aligned, particularly in their integration of information systems, statistics, probability, finance, and real-world data applications. However, the modular structure and specific institutional focuses differ. The Imperial qualification may include more emphasis on economic theory and modelling, while the South African qualification incorporates regionally relevant content and practical case applications aligned to local markets.

While both qualifications meet international academic standards and prepare graduates for further study and employment in data-centric fields, the South African qualification is unique in its responsiveness to the national economic landscape, workforce needs, and transformation goals, ensuring its relevance within a global yet distinctly South African context.

ARTICULATION OPTIONS

N/A

MODERATION OPTIONS

N/A

CRITERIA FOR THE REGISTRATION OF ASSESSORS

N/A

NOTES

This qualification allows possibilities for horizontal, vertical and diagonal articulation.

Horizontal Articulation:

Bachelor of Science in Quantitative Risk Management, NQF Level 7.

Bachelor of Business Science with Analytics, NQF Level 7.

Vertical Articulation:

Bachelor of Science Honours in Business Analytics, NQF Level 8.

Postgraduate Diploma in Data Analytics, NQF Level 8.

Diagonal Articulation:

Advanced Occupational Diploma: Chief Information Officer: Cybersecurity, NQF Level 7.

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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