Data Preparation, ETL and Warehousing

Why this module belongs in the course

Preprocessing changes the evidence available to a model. Imputation, scaling, feature selection and warehouse design are therefore modelling decisions, not harmless housekeeping.

Before you begin

The concepts and project evidence from Module 2. You should also be able to create a Python virtual environment and keep private or employer data out of the exercise.

Four lessons, one connected result

1. Lesson 1Treat Missing Values, Noise and Outliers Without Hiding Them75 min · Beginner to intermediate
2. Lesson 2Scale, Discretize and Reduce Features with Evidence85 min · Intermediate
3. Lesson 3ETL, Dimensional Modelling and OLAP for Data Mining90 min · Intermediate
4. Lesson 4Project: Build a Clean Analytical Dataset and Star Schema135 min · Intermediate

How to know you are ready to continue

Complete the checkpoint without copying the worked example. Keep the code, output and a short decision note. Your note should explain one choice, one failure you observed and one limitation a reviewer should know.

Primary references for this module

The lessons explain the ideas in original wording. Use these primary or official sources when a library interface, standard or research claim needs verification.

• pandas missing data
• scikit-learn imputation
• scikit-learn outlier detection
• scikit-learn preprocessing
• scikit-learn decomposition
• scikit-learn feature selection
• PostgreSQL table constraints
• PostgreSQL GROUPING SETS