Duration: 8 Weeks (1 day per week)                                                                                                                                
Method of Delivery: Online (LMIS) or Classroom-Based Training
Approach: Staggered Learning (1 module per week)

Alignment: SAQA US:  

1. 9009-Apply basic knowledge of statistics and probability to influence the use of data and procedures in order to investigate life related problems

2. 14346: Process numerical and text data in a business environment

3. 110083: Process, analyse and communicate

Course Overview:

This course equips learners with fundamental data analysis skills to support decision-making, risk assessment, and strategic insights. It covers statistical concepts, data visualization, probability, forecasting, and data storytelling to enhance data-driven business operations.

Learners will gain practical knowledge in data handling, interpretation, and presentation while applying relevant analytical tools and methodologies.

Week

Module Title

Key Learning Outcomes & Theories

1

Decision-making under uncertainty

- Understanding decision theory in uncertain environments.
- Introduction to Bayesian reasoning and probability updates.
- Exploring expected value and decision trees for strategic planning.
- Introduction to heuristics and biases in decision-making.

2

Data visualization and descriptive statistics

- Understanding data distributions (normal, skewed, uniform).
- Exploring measures of central tendency (mean, median, mode) and variability.
- Applying histograms, box plots, and scatter plots for data interpretation.
- Principles of effective data visualization (color theory, perception, storytelling).

3

Quantifying risk through probability

- Understanding basic probability rules (independence, conditional probability).
- Applying Bayes' Theorem for predictive analysis.
- Working with probability distributions (normal, binomial, Poisson).
- Exploring risk assessment and Monte Carlo simulations.

4

Data integrity and statistical inference

- Understanding data quality principles (accuracy, consistency, completeness).
- Sampling techniques (random, stratified, systematic sampling).
- Applying hypothesis testing (null vs. alternative hypothesis, p-values).
- Understanding confidence intervals and margin of error.

5

Evidence-based decisions

- Applying scientific methods to business analytics.
- Using A/B testing and controlled experiments.
- Understanding correlation vs. causation in decision-making.
- Working with regression analysis for predicting outcomes.

6

Understanding the causes of things

- Applying causal inference techniques (difference-in-differences, instrumental variables).
- Identifying relationships using linear and logistic regression.
- Understanding confounding variables and omitted variable bias.
- Working with data modeling techniques (causal diagrams, decision trees).

7

Time series forecasting

- Understanding trend analysis and seasonality.
- Applying moving averages and exponential smoothing techniques.
- Using autoregressive models (ARIMA, Holt-Winters method).
- Forecasting using machine learning techniques (regression trees, neural networks).

8

Delivering insights through storytelling

- Principles of data storytelling (clarity, structure, engagement).
- Crafting narratives with data visualization.
- Using persuasive storytelling techniques (problem-solution framework).
- Presenting insights effectively through dashboards and reports.