Programme content
Problem framing and the data-science lifecycle
Guided explanation, practical examples and an applied task focused on problem framing and the data-science lifecycle.
Python for analytical work
Guided explanation, practical examples and an applied task focused on python for analytical work.
Data cleaning and quality decisions
Guided explanation, practical examples and an applied task focused on data cleaning and quality decisions.
Exploratory data analysis
Guided explanation, practical examples and an applied task focused on exploratory data analysis.
Statistics for practical modelling
Guided explanation, practical examples and an applied task focused on statistics for practical modelling.
Feature engineering and model preparation
Guided explanation, practical examples and an applied task focused on feature engineering and model preparation.
Supervised machine learning
Guided explanation, practical examples and an applied task focused on supervised machine learning.
Model evaluation and avoiding misleading results
Guided explanation, practical examples and an applied task focused on model evaluation and avoiding misleading results.
Visualisation, interpretation and communication
Guided explanation, practical examples and an applied task focused on visualisation, interpretation and communication.
Capstone: evidence-driven data-science project
Guided explanation, practical examples and an applied task focused on capstone: evidence-driven data-science project.
Learning outcomes
- Explain the core technical concepts accurately.
- Apply the relevant methods in guided practical work.
- Test and improve outputs systematically.
- Complete an applied challenge or capstone.
- Present the final work and justify key decisions.