The live coding examples (created during the course) with added comments can be found in the day_1 and day_2 folders.
Data can be found in the /files folder There are two jupyter notebooks for pandas related exercises:
- Exercises: exercises.ipynb
- Solutions: exercise_solutions.ipynb
The notebook for visualization demonstrations are in the notebook visualization_guide.ipynb. If you would like to investigate other types of visualizations, you can find more in the Seaborn library documentation.
During the course, we cloned and investigated the examples in the following repository:
Found in Python_Training_Slides.pptx in this repo.
Recommended reading:
- Python Crash Course (3rd Edition) by Eric Matthes: A project-oriented book for Python beginners.
- Hands On Data Analysis With Pandas by Stefanie Molin: An analytics course for intermediate Python programmers.
Here are some other recommended resources:
- Leetcode: Python Exercises
- Leetcode: Pandas Exercises
- Choosing the Right Visualization
- Pandas Crash Course on YouTube
- Learn Data Analysis with Python
- Intro to Computer Science (Highly recommended, however more of a focus on a deeper understanding programming and algorithms rather than direct applicability)
Basic usage of git (and Github) is outlined in the file github_instructions.md. Becoming proficient in git (by understanding both the branching/merging behaviour as well as memorising the commands) can take time, but there are several helful resources, some of which are linked below:
A document called KPI_Dashboard_Demo.pdf shows examples of how to work with the AI coding assistant Cline to build a KPI dashboard. It shows the progression of coding manually towards using spec-driven programming with AI.