Occupational Certificate: Data Science Practitioner

SAQA ID: 118708, 185 Credits, Certificate issued by QCTO

OVERVIEW

The purpose of this qualification is to prepare a learner to operate as a Data Science Practitioner. Data Science Practitioners take custody of data and make the data available in a structured form for the Data Scientist to use. They support the data life cycle by collecting, transforming, and analysing data and communicating results to solve elementary business problems. They transform data into robust, comprehensive data sets, aligned with the problem identified in the statement of work and ready for storage.

KEY LEARNING OUTCOMES

  • Collect large amounts of structured and unstructured data from primary and secondary sources and extract and transform them into a usable format.
  • Apply data analysis techniques to uncover patterns and trends in datasets (resultant sets of data that can be viewed as tables or as a “spreadsheet of data”) to solve business-related problems.
  • Prepare and present descriptive analytic reports on patterns and trends using computer programming languages and explain those patterns and trends through e.g., visualisation, storytelling, etc., using data visualization tools.

Academic Requirements:

  • NQF Level 4 qualification. 

Language: Proficiency in English is required, as course materials and support are provided exclusively in English.

Tools Needed: A PC or laptop with a stable internet connection.

Non-Refundable Application Fee: R3500.00

Course fee: R36 500.00

Monthly instalment: R4 125.00 over 8 Months

Study duration: 12 Months Online/Full Time

  • Data Scientist
  • Data Analyst
  • Junior Data Scientist
  • Data Engineer
  • Business Intelligence Analyst
  • Data Visualization Specialist
  • Machine Learning Engineer
  • Research Analyst
  • Quantitative Analyst
  • Data Quality Analyst

modules

Knowledge Modules (66 Credits):

  • 251102-001-00-KM-01 Introduction to Data Science and Data Analysis, Level 4, 6 Credits.
  • 251102-001-00-KM-02 Logical Thinking and Basic Calculations: Refresher, Level 4, 4 Credits.
  • 251102-001-00-KM-03 Computers and Computing Systems, Level 4, 4 Credits.
  • 251102-001-00-KM-04 Computing Theory, Level 4, 2 Credits.
  • 251102-001-00-KM-05 Basic Statistics for Data Analytics, Level 4, 10 Credits.
  • 251102-001-00-KM-06 Statistics Essentials for Data Analytics, Level 5, 4 Credits.
  • 251102-001-00-KM-07 Data Science and Data Analysis, Level 5, 12 Credits.
  • 251102-001-00-KM-08 Data Analysis and Visualisation, Level 5, 16 Credits.
  • 251102-001-00-KM-09 Introduction to Governance, Legislation and Ethics, Level 4, 3 Credits.
  • 251102-001-00-KM-10 Fundamentals of Design Thinking and Innovation, Level 4, 4 Credits.
  • 251102-001-00-KM-11 4IR and Future Skills, Level 4, 1 Credit.

Practical Skill Modules (59 Credits):

  • 251102-001-00-PM-01 Apply Logical Thinking and Maths Refresher, Level 4, 3 Credits.
  • 251102-001-00-PM-02 Apply Code to Use a Software Toolkit/Platform in the Field of Study or Employment, Level 4, 4 Credits.
  • 251102-001-00-PM-03 Use Spreadsheets to Analyse and Visualise Data, Level 4, 3 Credits.
  • 251102-001-00-PM-04 Use a Visual Analytics Platform to Analyse and Visualise Data, Level 5, 4 Credits.
  • 251102-001-00-PM-05 Apply Statistical Tools and Techniques, Level 5, 4 Credits.
  • 251102-001-00-PM-06 Collect and Pre-Process Large Amounts of Unruly Data, Level 5, 12 Credits.
  • 251102-001-00-PM-07 Apply Data Analysis Techniques to Uncover Patterns and Trends in Datasets, Level 5, 12 Credits.
  • 251102-001-00-PM-08 Prepare and Present Descriptive Analytic Reports for Decision Making, Level 5, 12 Credits.
  • 251102-001-00-PM-09 Participate in a Design Thinking for Innovation Workshop, Level 5, 3 Credits.
  • 251102-001-00-PM-10 Collaborate Ethically and Effectively in the Workplace, Level 5, 2 Credits.

Work Experience Modules (60 Credits):

  • 251102-001-00-WM-01 Data Collection and Pre-processing Processes, Level 5, 16 Credits.
  • 251102-001-00-WM-02 Statistical Data Analysis Processes, Level 5, 16 Credits.
  • 251102-001-00-WM-03 Data visualisation and Reporting Processes, level 5, 16 Credits.
  • 251102-001-00-WM-04 Capstone Project Using an Appropriate Toolkit, Level 5, 12 Credits.

ts.

Exit Level Outcomes 

  • Collect large amounts of structured and unstructured data from primary and secondary sources and extract and transform them into a usable format.
  • Apply data analysis techniques to uncover patterns and trends in datasets (resultant sets of data that can be viewed as tables or as a “spreadsheet of data”) to solve business-related problems.
  • Prepare and present descriptive analytics reports on patterns and trends using computer programming languages and explain those patterns and trends through e.g., visualization and storytelling etc., using data visualisation tools. 

OVERVIEW

The Real-World Learning Programme at ICM Institute is designed to provide students with hands-on, practical experiences that bridge the gap between theory and real-world application. This unique programme ensures that students are not only equipped with knowledge but are also industry-ready by the time they complete their courses.

Key Inclusions of the Real-World Learning Programme

Industry-Focused Projects

Students will work on live projects that reflect current industry challenges. These projects allow students to apply what they’ve learned in class to solve real-world problems.

Case Studies

Throughout the course, learners will engage with case studies from their chosen industries, gaining insights into how businesses and organizations handle specific situations.

Collaborative Learning

The programme encourages collaboration with peers, mentors, and industry professionals, fostering teamwork and improving problem-solving skills.

Simulations and Role-Playing

The programme promotes collaboration with peers, mentors, and industry professionals through role-playing, mock interviews, and decision-making simulations, preparing students for real-world challenges.

MENTORSHIP

Students will have access to industry mentors who provide guidance, feedback, and insights into career paths, best practices, and emerging trends.

Internship Opportunities

Where applicable, students may have the chance to secure internships with partnering companies, allowing them to gain real workplace experience.

Benefits of the Real-World Learning Programme

Enhanced Employability

Students gain real-world experience, making them more appealing to employers who prioritize practical skill

Case Career Readiness

Graduates face industry challenges confidently, having already encountered similar demands during the programme.

Networking OpportunitieS

Collaboration with industry professionals and mentors helps students build valuable career networks.

Skill Mastery

Students will have access to industry mentors who provide guidance, The hands-on learning approach ensures students develop essential skills to excel in their specific fields., and insights into career paths, best practices, and emerging trends.

Real-Time Feedback

Students receive valuable feedback from academic staff and industry professionals to refine their skills.

Teamwork & Problem-Solving

Group projects and simulations foster collaboration and improve critical problem-solving abilities.

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