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Data Manipulation in SQL

120 reviews

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

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4 Hours15 Videos55 Exercises
188,169 Learners

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Course Description

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.
  1. 1

    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.

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    We'll take the CASE
    50 xp
    Basic CASE statements
    100 xp
    CASE statements comparing column values
    100 xp
    CASE statements comparing two column values part 2
    100 xp
    In CASE things get more complex
    50 xp
    In CASE of rivalry
    100 xp
    Filtering your CASE statement
    100 xp
    CASE WHEN with aggregate functions
    50 xp
    100 xp
    COUNT and CASE WHEN with multiple conditions
    100 xp
    Calculating percent with CASE and AVG
    100 xp
  2. 3

    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.

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In the following tracks

Data Analyst in SQLSQL Fundamentals


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Hillary Green-Lerman
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Sumedh Panchadhar


Joining Data in SQL
Mona Khalil HeadshotMona Khalil

Data Scientist, Greenhouse Software

As a senior data scientist at Greenhouse, Mona answers questions related to how the hiring process can be improved to find better candidates quicker and reduce bias in the hiring process. They previously worked in education, marketing, and local government. They also co-host Bad Methods, a podcast that brings a fun and interesting lens to critically evaluating science. You can find them on twitter at mona_kay_.
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Don’t just take our word for it

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  • Begzod S.
    17 days

    good course

  • Agnius M.
    19 days


  • Lisa H.
    20 days

    Loved the course - content explained well

  • Md. H.
    22 days

    It will contribute participant's skill immensely. Besides the dataset and example and exercises were too relevant.

  • Jherson C.
    22 days

    I recently finished an outstanding course that went above and beyond my expectations. The videos were clear, and the practical exercises perfectly complemented the lessons. This course is a valuable investment in your learning journey. Highly recommended!

"good course"

Begzod S.


Agnius M.

"Loved the course - content explained well"

Lisa H.


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