DSEAfrica

Python

Python analytics projects

Clean, explore and analyse real datasets with pandas and NumPy, then submit your notebook and write up what you found.

Browse all projects

Where your code runs

The Python lab runs pandas, NumPy, Matplotlib and scikit-learn inside your browser, with the project dataset already loaded, so nothing needs installing. Graded exercises are marked again on our server. For the full brief you can also work in Jupyter, VS Code or Colab and submit the notebook.
1

Open the lab

Pick a project in the Python lab and its dataset is loaded into data/ for pandas.

2

Solve the exercises

Each project has graded exercises with hints. Submit a cell and it is marked on the server for points.

3

Analyse

Clean the data, explore it, and produce a defensible figure for each business question, in the lab or your own notebook.

4

Submit

Download your notebook as .ipynb, or link your GitHub repository, and write your summary.

What you will practise

Core libraries

  • pandas
  • NumPy
  • Matplotlib
  • Seaborn

Analysis

  • Data cleaning
  • Missing values
  • Exploratory data analysis
  • Grouping and aggregation
  • Joins and merges
  • Statistical analysis
  • Time series

Working with data

  • CSV
  • Excel
  • JSON
  • Parquet
  • API extraction
  • Writing analysis reports

What you submit

  • A Jupyter notebook (.ipynb) or Python script (.py)
  • A GitHub repository link, if you use one
  • Charts saved as images, or rendered in the notebook
  • A written summary of your findings and recommendations

Resources

Python projects(4)

Current section: /python