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Exploratory Data Analysis in SQL

MellanliggandeFärdighetsnivå

Uppdaterad 2026-01

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

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SQLExploratory Data Analysis4 timmar16 videos57 exercises4,650 XP170K+Uttalande om prestation

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Kursbeskrivning

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.The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos. The course glossary can be found on the right in the resources section. To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

Förkunskapskrav

Data Manipulation in SQL

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.

What's in the database?

Explore table sizes

Count missing values

Join tables

The keys to the database

Foreign keys

Read an entity relationship diagram

Column types and constraints

Effects of casting

Summarize the distribution of numeric values

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.

Numeric data types and summary functions

Explore with division

Summarize numeric columns

Summarize group statistics

Exploring distributions

Generate series

More summary functions

Correlation

Mean and Median

Creating temporary tables

Create a temp table

Create a temp table to simplify a query

Insert into a temp table

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.

Character data types and common issues

Count the categories

Spotting character data problems

Cases and spaces

Exploring unstructured text

Splitting and concatenating text

Concatenate strings

Split strings on a delimiter

Shorten long strings

Strategies for multiple transformations

Create an "other" category

Group and recode values

Create a table with indicator variables

4

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.

Date/time types and formats

Date comparisons

Date arithmetic

Completion time by category

Date/time components and aggregation

Variation by day of week

Date truncation

Aggregating with date/time series

Find missing dates

Custom aggregation periods

Monthly average with missing dates

Time between events

Longest gap

Exploratory Data Analysis in SQL

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