Part 2: Downstream Data Cleaning – Consuming Structured Data

This part delves into the processes required for cleaning and preparing structured data for analysis, focusing on handling common data challenges that occur in more refined datasets. It provides practical techniques for managing missing values and outliers, ensuring data consistency through normalization and standardization, and effectively processing categorical features. Additionally, it introduces specialized methods for working with time series data, a common yet complex data type. By mastering these downstream cleaning and preparation techniques, readers will be well-equipped to turn structured data into actionable insights for advanced analytics.

This part has the following chapters:

• Chapter 8, Detecting and Handling Missing Values and Outliers
• Chapter 9, Normalization and Standardization
• Chapter 10, Handling Categorical Features
• Chapter 11, Consuming Time Series Data

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