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Exploratory Data Analysis in SQL

Learn how to explore what's available in a database: the tables, relationships between them, and data stored in them.

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4 Hours16 Videos58 Exercises46,744 Learners
4750 XP

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Course Description

You have access to a database. Now what do you do? Building on your existing skills joining tables, using basic functions, grouping data, and using subqueries, the next step in your SQL journey is learning how to explore a database and the data in it. Using data from Stack Overflow, Fortune 500 companies, and 311 help requests from Evanston, IL, you'll get familiar with numeric, character, and date/time data types. You'll use functions to aggregate, summarize, and analyze data without leaving the database. Errors and inconsistencies in the data won't stop you! You'll learn common problems to look for and strategies to clean up messy data. By the end of this course, you'll be ready to start exploring your own PostgreSQL databases and analyzing the data in them.

  1. 1

    What's in the database?


    Start exploring a database by identifying the tables and the foreign keys that link them. Look for missing values, count the number of observations, and join tables to understand how they're related. Learn about coalescing and casting data along the way.

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    What's in the database?
    50 xp
    Explore table sizes
    50 xp
    Count missing values
    100 xp
    Join tables
    100 xp
    The keys to the database
    50 xp
    Foreign keys
    50 xp
    Read an entity relationship diagram
    100 xp
    100 xp
    Coalesce with a self-join
    100 xp
    Column types and constraints
    50 xp
    Effects of casting
    100 xp
    Summarize the distribution of numeric values
    100 xp
  2. 2

    Summarizing and aggregating numeric data

    You'll build on functions like min and max to summarize numeric data in new ways. Add average, variance, correlation, and percentile functions to your toolkit, and learn how to truncate and round numeric values too. Build complex queries and save your results by creating temporary tables.

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  3. 3

    Exploring categorical data and unstructured text

    Text, or character, data can get messy, but you'll learn how to deal with inconsistencies in case, spacing, and delimiters. Learn how to use a temporary table to recode messy categorical data to standardized values you can count and aggregate. Extract new variables from unstructured text as you explore help requests submitted to the city of Evanston, IL.

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In the following tracks

SQL for Business Analysts


Chester IsmayAdrián SotoMona Khalil


Intermediate SQL
Christina Maimone Headshot

Christina Maimone

Data Scientist, Northwestern University

Christina Maimone leads Research Data Services at Northwestern University with the IT Research Computing Services group. She enables innovative research by providing data science, programming, and software development support for researchers. Through consultations, project collaborations, user groups, and workshops, the Research Data Services team ensures researchers have the resources, services, and skills they need to overcome challenges in their work. Christina regularly uses R, Python, and SQL but enjoys the challenge of using a wide range of programs and languages in her work. She has a PhD in political science and an MS in statistics from Stanford.
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