IIE Postgraduate Diploma in Data Analytics - (Distance)

Admission requirements

Minimum Admission Requirements

• An appropriate HEQSF Level 7 Bachelor’s degree; OR
• An appropriate Advanced Diploma; OR
• An equivalent NQF Level 7 qualification
• International Applicants: A SAQA Evaluation Certificate with NQF L7 equivalence in an appropriate field.
• Notes: The qualification must include modules that provided the applicant with at least an introductory level background in programming, databases, and statistical or numerical methods. Alternatively, the candidate must have a minimum of 3 years’ experience working in a data analytics role; or they must first complete additional modules or short courses that will provide them with an introductory level background in programming, databases, and statistical or numerical methods. Example modules would include Programming 1A, Databases, and Mathematical Principles for Computer Science; or alternatively short courses such as Python or Java programming, database theory, and statistical or numerical methods.

For alternative admission options, please click here or reach out to student recruitment.

Please note, requirements for entry to this qualification are correct at the time of publication, however, these may change.

Curriculum

1 Year Structure:

Year 1 – Semester 1

• DANA8411: The purpose of this module is to introduce students to data analytics and big data (structured and unstructured). Students will be equipped with theoretical and practical knowledge to undertake basic data analytics for simple business problems.
• SMAA8411: This module introduces mathematical and statistical techniques, models, and algorithms that underly data analytics.
• PDAN8411: This module deepens and extends existing programming and development of knowledge and skills into specialist field of Data Analytics. Students use appropriate software tools to retrieve, prepare, explore, model data and present the result to solve business problems.
• RPDA8411: The purpose of this module is to expose students to the theoretical and practical knowledge in proposal development. Students are exposed to fundamental aspects such as philosophy of science, methodology and research planning.

Year 1 – Semester 2

• DANA8412: This module builds on the foundational knowledge of Data Analytics 1 module. The purpose of this module is to develop the knowledge, skills and practical application of machine learning, statistical methods, artificial intelligence and algorithmic processes to inform business processes and decisions.
• DASC8412: This module introduces students to the fundamental concepts, principles, tools and techniques of data science. Students will be able to identify various ways of preparing, managing, exploring and visualising data to provide insight into specific business problems.
• PDAN8412: The purpose of this module is to build on the knowledge and skills gained in Programming for Data Analytics 1 and to develop scalable solutions using data analytics to solve business problems using machine learning algorithms, models and methods.
• RPDA8412: The purpose of this module is to develop students’ skills in critical analysis, and academic discourse to complete a self-directed research project in data analytics, culminating in a research report.

2 Year Structure:

Year 1 – Semester 1

• DANA8411: The purpose of this module is to introduce students to data analytics and big data (structured and unstructured). Students will be equipped with theoretical and practical knowledge to undertake basic data analytics for simple business problems.
• SMAA8411: This module introduces mathematical and statistical techniques, models, and algorithms that underly data analytics.
• Year 1 – Semester 2
• DANA8412: This module builds on the foundational knowledge of Data Analytics 1 module. The purpose of this module is to develop the knowledge, skills and practical application of machine learning, statistical methods, artificial intelligence and algorithmic processes to inform business processes and decisions.
• DASC8412: This module introduces students to the fundamental concepts, principles, tools and techniques of data science. Students will be able to identify various ways of preparing, managing, exploring and visualising data to provide insight into specific business problems.

Year 2- Semester 3

• PDAN8411: This module deepens and extends existing programming and development of knowledge and skills into specialist field of Data Analytics. Students use appropriate software tools to retrieve, prepare, explore, model data and present the result to solve business problems.
• RPDA8411: The purpose of this module is to expose students to the theoretical and practical knowledge in proposal development. Students are exposed to fundamental aspects such as philosophy of science, methodology and research planning.

Year 2- Semester 4

• PDAN8412: The purpose of this module is to build on the knowledge and skills gained in Programming for Data Analytics 1 and to develop scalable solutions using data analytics to solve business problems using machine learning algorithms, models and methods.
• RPDA8412: The purpose of this module is to develop students’ skills in critical analysis, and academic discourse to complete a self-directed research project in data analytics, culminating in a research report.

Study further with our pathways

In the IIE Faculty of Science and Technology: School of Computer Science, graduates of the Postgraduate Diploma in Data Analytics can articulate into the Master of Strategic Information and Communication Technology Management.

Accreditation

The IIE Postgraduate Diploma in Data Analytics is accredited by the Higher Education Quality Council (HEQC) of the Council on Higher Education (CHE) and is registered by the South African Qualifications Authority (SAQA) on the National Qualifications Framework (NQF) as a 120-credit qualification on level 8 (SAQA ID: 117788).

Career opportunities

• Data analyst
• Data scientist
• Data engineer
• Big data analyst
• Business intelligence analyst
• Quantitative analyst
• Any field that requires the analysis of data to yield strategic value

Teaching and Learning strategy

At Emeris, we believe that effective teaching is about creating experiences that foster student growth. Our academic team design activities that challenge students and identify their strengths, allowing for real-time adaptation in the learning journey. By combining subject expertise with teaching insights and digital tools, we extend learning beyond the classroom. This approach ensures students not only keep up but thrive.

On-Campus written examination venue information

• For students based in South Africa, online students may select any campus of Emeris as their Examination Centre
• An alternative examination centre is only permissible for students studying abroad and those located in a province of South Africa where there is no Emeris campus. If there is no Emeris campus within reach, an alternative venue will need to be sourced by you. For non-Emeris venues, there is an alternative venue levy per module.

Device and connectivity specifications required for online study

• Reliable internet connection (we recommend 5-10 GB of data per month and an internet speed of at least 0.15 Mbps).
• Desktop PC or laptop with at least an i3 processor and 4GB RAM memory.
• Windows 8 or macOS 10.15 (Microsoft Office 365 is available as a free download for Emeris students), and Google Chrome, Microsoft Edge Chromium or Firefox.
• A working webcam and microphone.
• An uninterrupted power supply is also highly recommended. Exams are written at an exam centre and in some instances online. These details will be specified in your programme assessment schedule.
• Exams are written at an exam centre and in some instances online. These details will be specified in your programme assessment schedule.

Students will also receive access to a Virtual Machine to run required software – this is accessible from your own device as well or can be accessed via the campuses’ lab Computers.