Data Science
Data Science
Data science combines statistics, programming, domain knowledge and communication to turn raw information into evidence that supports decisions.
Tools and technologies
- Python
- pandas
- NumPy
- SQL
- Jupyter
- R
- Power BI / Tableau
- Matplotlib
Practical examples
- Analyse property listings to compare asking prices by district and property type.
- Track school attendance and identify patterns without exposing student identities.
- Summarise business sales and inventory to reduce stock-outs.
Application areas
- Business intelligence
- Forecasting
- Public-service planning
- Customer analytics
- Research
- Operations optimisation
Suggested hands-on project
Mini project: use a sample CSV of property listings, check missing values and duplicates, calculate median price by district, and publish a chart with a clear explanation of sample size and limitations.
A typical workflow
Ask a measurable question → obtain lawful data → clean and validate → explore and analyse → visualise findings → communicate uncertainty → enable a decision and monitor its effect.
How to get started
Choose one small, measurable problem, build a prototype with sample data, document assumptions, test common failure cases, and collect feedback before expanding it.
Responsible use: Correlation does not prove causation. Check data quality, representativeness and privacy; explain assumptions and avoid presenting estimates as certain facts.