Course overview
The data-science pathway moves from analytical thinking and Python tools to data preparation, exploratory analysis, visualisation and introductory machine learning. Projects emphasise clear questions and defensible conclusions.
Learning outcomes
- Frame a useful analytical question
- Prepare and inspect datasets
- Analyse data with Python
- Create clear visualisations
- Build and evaluate introductory models
- Communicate limitations and findings
Curriculum
01 · Python for dataCore syntax, notebooks, arrays and tabular analysis.
02 · Data preparationCleaning, missing values, types, joins and transformations.
03 · ExplorationSummary measures, distributions, relationships and anomalies.
04 · VisualisationPurpose-led charts and explanatory presentation.
05 · Machine learningSupervised learning, validation, metrics and overfitting.
06 · Project communicationProblem statement, method, findings, limits and next steps.
Practical assignment
Example task
Frame a business question, inspect and clean a dataset, choose appropriate analysis and visualisation, evaluate a simple model where useful, and communicate findings with limitations.
Who this course suits
- Graduates with analytical interest
- Analysts upgrading technical skills
- Professionals working with business data
- Python learners moving into applied analysis
Learning format and prerequisites
Dataset exercises, notebooks, visual analysis, introductory model evaluation and a documented project.
Basic mathematics and computer confidence help. A Python foundation can be included where needed.
Before enrolling: confirm the syllabus, trainer, schedule, delivery mode, practical access, fee and support included in the current batch.