A practical, thinking-first approach to data analytics for real-world decision-making.
This repository provides a structured learning path for analytics.
It is designed to help you:
think with data, not just work with it
The focus is not only on techniques, but on:
- framing the right problems
- interpreting results meaningfully
- supporting decisions and actions
- evaluating whether decisions actually work
Most analytics resources focus on:
- tools
- coding
- models
This repository focuses on:
Thinking → Interpretation → Decision → Impact
You will learn:
- how to ask the right questions
- how to make sense of data
- how to connect analysis to real-world outcomes
- how to evaluate whether decisions actually make a difference
Learn how to think with data and apply core analytical techniques.
- Introduction to Data Thinking
- Problem Framing
- Data Understanding
- Regression
- Classification
- Clustering
- Association
- Time Series
- Natural Language Processing
- Real-World Analytics Systems (understanding how analytics systems work)
Extend your thinking beyond models into structure, decision-making, and evaluation.
- Social Network Analytics (Relationships & Influence)
- Prescriptive Analytics (Decision & Optimisation Thinking)
- Experimentation & Causal Thinking (Evaluation & Impact)
Each module includes:
- Lesson → concepts and intuition
- Exercise → guided thinking
- Lab → hands-on application
- Solutions → reference
This course follows the CRISP-DM framework:
- Business Understanding
- Data Understanding
- Data Preparation
- Modelling
- Evaluation
- Deployment
Different modules emphasise different parts of the process, but the framework remains consistent.
Start from:
1-Introduction/
Proceed sequentially through the core modules.
Extension modules deepen your analytical thinking and connect analysis to decision-making and evaluation.
- beginners learning analytics
- professionals working with data
- non-technical users who want to think more clearly with data
- Python (recommended)
- R (optional)
Analytics is not about models.
It is about:
making better decisions — and knowing whether those decisions work
Adam Wong Cheng Hun
CC BY-NC 4.0