Course Objective & Scope
Analyses that rely on Excel and manual reporting become slow and error-prone as data grows. This programme introduces the Python data science ecosystem from scratch so participants can produce reproducible, version-controlled analyses.
Python syntax, NumPy arrays, pandas DataFrame operations (filtering, groupby, merge, pivot), working with dates and text, and visualisation with Seaborn are covered in hands-on labs. Work is done on a realistic, anonymised dataset from the organisation, and the course ends with a data preparation notebook template the team can share internally.
Who Should Attend?
- Data analysts working with Excel and SQL who want to move to Python
- Developers joining data science teams
- Finance, marketing and operations specialists looking to automate reporting
- Prerequisite: familiarity with basic programming logic helps but is not mandatory
Key Takeaways
- Set up a clean analysis environment with Jupyter and virtual environments (venv/conda)
- Load and combine data from CSV, Excel and SQL sources with pandas
- Fix missing values, type conversions and duplicate records
- Build summary tables with groupby, pivot and window operations
- Produce clear charts with Matplotlib and Seaborn
- Deliver a clean feature table ready for a scikit-learn pipeline
Curriculum
Python Basics and the Analysis Environment
- Data types, loops, functions
- Jupyter, venv/conda and package management
Vectorised Computing with NumPy
- Arrays, broadcasting and vectorised operations
- Avoiding loops for performance
Data Preparation with pandas
- Reading, filtering, merge and pivot
- Missing values, dates and text operations
Visualisation
- Chart types with Matplotlib and Seaborn
- Choosing the right chart and telling the story
Preparing Data for Modelling
- Encoding, scaling and feature creation
- Introduction to scikit-learn pipelines
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