Effective data science work starts with obtaining and preparing the data. This chapter focuses on the practical steps required to gather information from various origins and refine it into a usable format for analysis.

You will learn techniques for accessing data stored in SQL databases and data warehouses, retrieving information from web APIs, and extracting structured content from websites using web scraping methods. We will also cover advanced strategies for data cleaning, handling missing values beyond simple imputation, applying necessary transformations like scaling $x_{scaled} = (x - \min(x)) / (\max(x) - \min(x))$ and normalization, and integrating data from multiple sources through merging and joining operations. By the end of this chapter, you will have practiced applying these methods to prepare datasets for the subsequent stages of feature engineering and model building.