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Data Analysis and Visualization with Python / Module 9

Module 9

Exploratory Data Analysis and Data Quality

Learners can run structured EDA, profile data quality, inspect distributions and relationships, compare segments, investigate missingness and outliers, and write careful findings with evidence and limits.

Downloadable-first courseRead the lesson online, then use the downloadable pack to run the practice and worked notebooks locally. A browser lab can be added later after full visualization runtime validation.
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Module sequence

  1. LessonRead the classroom explanation
  2. Practice activityUse the supplied notebook and dataset
  3. Module checkConfirm the skill before moving on

Notebook path

After downloading the pack, open modules/module-09/notebooks/module-09-practice.ipynb in JupyterLab.