Overview
Curriculum
In this module, we’ll focus on data exploration, visualization, and feature engineering—essential steps in preparing data for analysis. You’ll learn how to use techniques like summary statistics and visual tools to understand the structure of your data, spot patterns, and identify issues such as missing values or outliers. We’ll also cover how to transform and create new features that make your data more useful for modeling. By the end, you’ll be able to uncover insights, clean your data, and shape it for better analysis and predictive performance.
What You'll Learn
- Explore the idea behind exploratory data analysis using Python
- Understand the importance of feature engineering in building effective machine-learning models
- Apply summary statistics to interpret data
- Analyze the importance of segmentation in data analysis and recognize pitfalls of data analysis
- Understand how domain knowledge helps us to create informative and relevant features for a particular problem
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