Home | Power BI | Excel | Python | SQL | Visualising Data | Generative AI
Python Course Notes
Why learn Python?
- The most popular language on the planet
- De-facto for data analysis, machine learning, generative AI
- A well-designed, consistent language – good for learning
- A versatile language – can use objected oriented and functional styles
- Python has libraries for just about everything
- Open source and free
Python Foundation Course - “Hidden” Agenda
- For people new to any computer language, concepts in coding
- Editors : Colab, or GitHub CodeSpaces, notebooks
- Working with AI “Copilots” and ChatGPT
- The universe of Python packages
Four ways to run Python
Virtually, within a browser, no local set up needed
- Google Colab (easiest to get started, free)
- GitHub CodeSpaces (slightly nicer than Colab but only 120 hours free then $4/month, need a free GitHub account
Locally installed
- VS Code (popular code editor) with Python: most flexible, best for serious coding
- Excel in Python (coming soon): some pros and cons but perfect for Excel hacks who want to sprinkle some Python into their spreadsheet
- Data and calculations: numpy
- Data analysis: e.g. pandas
- Visualisation: e.g. seaborn
- Machine learning examples: e.g. keras
- Advanced Language e.g. Functional Programming, Object Oriented Programming styles
- AI Coding Assistance (with GitHub Copilot and large language models)
End-to-end pandas examples – load, reshape, stack, group, sum,….
- Football match to league table (Load, reshape, calculate, summarise, ) -reference
- Time series (daily FX rates, merge USD and EUR) jason
- Titanic (clean data)
Visualisation examples (seaborn) covid infection rate
Language
- functional programming (lambdas, list comprehensions)
- building classes (and methods and properties)
- Other inc datetime, matt
AI-related
- Using Chat GPT from Python using the OpenAI API
- Image recognition using neural net