Introduction to SQL

Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.
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4 Hours1 Video41 Exercises745,046 Learners
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Course Description

The role of a data scientist is to turn raw data into actionable insights. Much of the world's raw data—from electronic medical records to customer transaction histories—lives in organized collections of tables called relational databases. To be an effective data scientist, you must know how to wrangle and extract data from these databases using a language called SQL . This course teaches syntax in SQL shared by many types of databases, such as PostgreSQL, MySQL, SQL Server, and Oracle. This course teaches you everything you need to know to begin working with databases today!

  1. 1

    Selecting columns

    Free
    This chapter provides a brief introduction to working with relational databases. You'll learn about their structure, how to talk about them using database lingo, and how to begin an analysis using simple SQL commands to select and summarize columns from database tables.
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  2. 2

    Filtering rows

    This chapter builds on the first by teaching you how to filter tables for rows satisfying some criteria of interest. You'll learn how to use basic comparison operators, combine multiple criteria, match patterns in text, and much more.
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  3. 3

    Aggregate Functions

    This chapter teaches you how to use aggregate functions to summarize data and gain useful insights. You'll also learn about arithmetic in SQL and how to use aliases to make your results more readable.
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  4. 4

    Sorting and grouping

    This chapter provides a brief introduction to sorting and grouping your results.
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In the following tracks
Data Science for EveryoneData Analyst Data Analyst SQL Fundamentals
Collaborators
Filip SchouwenaarsColin Ricardo
Nick Carchedi Headshot

Nick Carchedi

Product Manager at DataCamp
Nick is a Product Manager at DataCamp. Prior to joining DataCamp, he earned his master's degree at Johns Hopkins Biostatistics and worked as a data scientist for McKinsey. Nick's passion for teaching data science began in graduate school, where he was heavily involved in tutoring fellow students, developing the Johns Hopkins Data Science Specialization, and building the swirl R package.
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