Data Manipulation in SQL

Master the complex SQL queries necessary to answer a wide variety of data science questions and prepare robust data sets for analysis in PostgreSQL.

So you've learned how to aggregate and join data from tables in your database—now what? How do you manipulate, transform, and make the most sense of your data? This intermediate-level course will teach you several key functions necessary to wrangle, filter, and categorize information in a relational database, expand your SQL toolkit, and answer complex questions. You will learn the robust use of CASE statements, subqueries, and window functions—all while discovering some interesting facts about soccer using the European Soccer Database.

• We'll take the CASE
• In this chapter, you will learn how to use the CASE WHEN statement to create categorical variables, aggregate data into a single column with multiple filtering conditions, and calculate counts and percentages.
• Short and Simple Subqueries
• In this chapter, you will learn about subqueries in the SELECT, FROM, and WHERE clauses. You will gain an understanding of when subqueries are necessary to construct your dataset and where to best include them in your queries.
• Correlated Queries, Nested Queries, and Common Table Expressions
• In this chapter, you will learn how to use nested and correlated subqueries to extract more complex data from a relational database. You will also learn about common table expressions and how to best construct queries using multiple common table expressions.
• Window Functions
• You will learn about window functions and how to pass aggregate functions along a dataset. You will also learn how to calculate running totals and partitioned averages.