Data Analytics

The Data Analytics Certification prepares learners to responsibly collect, manipulate, analyse, visualise, and communicate data to support informed decision-making. Learners will gain the knowledge and practical skills required to work with data, identify trends and patterns, perform analysis, create meaningful visualisations, and apply responsible data practices.

Through this certification, learners will develop analytical thinking, data literacy, problem-solving abilities, and communication skills needed to transform raw data into actionable insights that support business and organisational objectives.

Course Overview

Data analytics plays a critical role in helping organisations make evidence-based decisions, improve operations, and identify opportunities for growth. This programme is designed to provide learners with a strong foundation in data collection, preparation, analysis, visualisation, and responsible data management.

Learners will gain practical experience in data manipulation, data cleaning, exploratory analysis, statistical interpretation, reporting, and visual communication. They will also develop an understanding of data privacy, bias, ethics, and responsible analytics practices that are essential in modern data-driven environments.

Course Overview

Duration

The programme combines classroom, practical, and workplace learning. Modules can be scheduled flexibly to meet learner and workplace requirements.

Delivery Method

The course can be delivered on-site at the learner’s business, at a training provider facility, or virtually. Accredited providers will supervise practical and workplace-based learning to meet all assessment and credit requirements.

Who Is This Course For?

Aspiring data analysts
Business and operations professionals
Students pursuing careers in analytics
IT and technology professionals
Managers and decision-makers
Individuals interested in data-driven problem-solving

Cost

R4 800.00 Per Learner

Exam Objectives

Data Basics
1.1 Define the Concept of Data Science
1.2 Describe Basic Data Variable Types
1.3 Describe Basic Structures Used in Data Analytics
1.4 Gather Data and Categorise Data

Data Manipulation
2.1 Import, Store, and Export Data
2.2 Clean Data Using SQL, R, Python, and Excel
2.3 Organise Data Using SQL, R, Python, and Excel
2.4 Aggregate Data Using SQL, R, Python, and Excel

Data Analysis
3.1 Describe and Differentiate Between Types of Data Analysis
3.2 Describe and Differentiate Between Data Aggregation and Interpretation Metrics
3.3 Describe and Differentiate Between Exploratory Data Analysis Methods
3.4 Evaluate and Explain the Results of Data Analyses
3.5 Define and Describe the Role of Artificial Intelligence in Data Analysis

Data Visualisation and Communication
4.1 Report Data
4.2 Create Visualisations from Data
4.3 Derive Conclusions from a Data Visualisation

Responsible Analytics Practices
5.1 Describe Data Privacy Laws and Best Practices
5.2 Describe Best Practices for Responsible Data Handling
5.3 Describe Types of Bias That Affect Collection and Interpretation of Data