Python for Data Work

What you’ll learn

• Write practical Python

  Use the data types, comprehensions, and file handling that data work leans on daily.

• Work with dataframes

  Load, clean, filter, and aggregate tabular data with pandas without fighting the API.

• Visualise findings

  Produce charts that communicate a result rather than merely displaying numbers.

• Make it reproducible

  Manage environments and turn a notebook into something that runs the same way twice.

1. 1Python Foundations for DataThe subset of Python that data work actually uses, taught in the order you will need it.25m 1s

   1. Lesson 1.11.1Lists, dicts, and the types you will useFree preview
      8m 6s
   2. Lesson 1.21.2Comprehensions and iteration
      10m 20s
   3. Lesson 1.31.3Reading files and CSVs
      6m 35s
2. 2pandas EssentialsThe dataframe workflow: loading, cleaning, reshaping, and summarising tabular data.1h 13m

   1. Lesson 2.12.1Series, dataframes, and indexing
      30m 37s
   2. Lesson 2.22.2Cleaning messy data
      31m 54s
   3. Lesson 2.32.3groupby and aggregation
      11m 5s
3. 3Visualising DataTurning a result into a chart someone can read, and knowing which chart to reach for.1h 12m

   1. Lesson 3.13.1Plotting with matplotlib
      30m 51s
   2. Lesson 3.23.2Statistical plots with seaborn
      35m 56s
   3. Lesson 3.33.3Choosing the right chart
      6m 9s
4. 4Reproducible WorkflowsMaking the analysis run again next month, on someone else’s machine, with the same result.43m 52s

   1. Lesson 4.14.1Working effectively in notebooks
      23m 17s
   2. Lesson 4.24.2Virtual environments and dependencies
      9m 5s
   3. Lesson 4.34.3Automating a recurring data job
      11m 30s