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.

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.

• 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.
• 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.
• 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.
• Working with Dates and Timestamps
• What time is it? In this chapter, you'll learn how to find out. You'll aggregate date/time data by hour, day, month, or year and practice both constructing time series and finding gaps in them.