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SQL for Data Analysis

SQL for Data Analysis

Learn to use Structured Query Language (SQL) to extract and analyze data stored in databases
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In this course, you’ll learn to use Structured Query Language (SQL) to extract and analyze data stored in databases. You’ll first learn to extract data, join tables together, and perform aggregations. Then you’ll learn to do more complex analysis and manipulations using subqueries, temp tables, and window functions. By the end of the course, you’ll be able to write efficient SQL queries to successfully handle a variety of data analysis tasks.

Expected Learning

SQL is the most commonly used data analysis tool for data analysts and data scientists. The majority of the world’s data is stored in databases, and learning SQL will enable you to access and analyze this data with ease. SQL is a valuable tool for a wide array of roles across diverse industries—digital marketers, engineers, product managers, and more, use SQL every day. The ability to do important data work yourself with SQL enhances the value you'll bring to any organization.


Lesson 1: SQL Basics
  • Learn to write common SQL commands including SELECT, FROM, and WHERE
  • Learn to use logical operators in SQL ## Lesson 2: SQL Joins
  • Learn to write INNER JOINs to combine data from multiple tables
  • Learn to write LEFT JOINs to combine data from multiple tables ## Lesson 3: SQL Aggregations
  • Learn to write common aggregations in SQL including COUNT, SUM, MIN, and MAX
  • Learn to write CASE and DATE functions, as well as work with NULL values ## Lesson 4: Subqueries and Temp Tables
  • Learn to write subqueries to run multiple queries together
  • Learn to use temp tables to access a table with more than one query ## Lesson 5: SQL Data Cleaning
  • Learn how to perform data cleaning using SQL ## Lesson 6: Window Functions
  • Learn to use window functions to tackle more analysis tasks ## Lesson 7: Advanced Joins and Performance Tuning
  • Learn to use advanced joins
  • Learn to write queries that run quickly across giant datasets

Required Knowledge

Students should have general familiarity working with data in spreadsheets.

Derek Steer
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