This module introduces techniques for acquiring data from a wide range of sources, with a focus on web scraping and text processing. You'll begin by exploring how data flows into analysis pipelines and gain hands-on experience using tools like Pandas and Beautiful Soup to extract, clean, and structure data. You'll apply text preprocessing methods to handle missing values and parse HTML. Plus, you’ll consider the ethical implications of scraping data from the web.

This module focuses on acquiring data using APIs, as well as applying numerical cleaning techniques. You’ll learn how to retrieve data from web-based APIs, handle authentication securely, and transform raw JSON responses into usable dataframes. The module also covers techniques for cleaning and preparing numerical data, including scaling, binning, normalization, and outlier handling.

This module introduces the fundamentals of data storage and retrieval using databases and SQL. You’ll learn how data is structured in relational systems; explore core concepts like entities, relationships, and schemas; and gain hands-on experience writing SQL queries. You’ll also explore how to query databases from a Python notebook, as well as how generative AI tools can support SQL-based tasks.

In this module, you’ll expand your SQL skills into data preprocessing, validation, and joins (combining tables). You’ll learn how to use SQL for filtering, conditional logic, and handling missing values, and apply validation techniques using aggregation and grouping. The module also explores different types of joins and demonstrates how to use them to combine and analyze data across multiple tables—especially in real-world scenarios like analyzing sports performance data.