Data I/O and Preprocessing with Python and SQL

Overview

Most real-world data isn’t clean, it’s messy, incomplete, and spread across sources like websites, APIs, and databases. In this course, you’ll learn how to collect that data, clean it, and prepare it for analysis using Python and SQL. You’ll start by extracting data from webpages using tools like Pandas and Beautiful Soup, while also learning how to handle unstructured text and apply ethical scraping practices. Next, you’ll access real-time data through APIs, parse JSON files, and clean numerical data using techniques like normalization and binning. You’ll also learn how to manage authentication with API keys and store them securely. Finally, you’ll work with databases: Querying and joining tables using SQL, validating results, and understanding when to use SQL versus Python for different preprocessing tasks. By the end of the course, you’ll be able to turn raw, real-world data into reliable, analysis-ready inputs—a core skill for any data professional.

Syllabus

Web scraping & text preprocessing

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.

APIs & numerical cleaning

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.

Databases

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.

Preprocessing, validation, and joins with SQL

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.