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Exploratory Data Analysis in SQL

IntermediateSkill Level
4.7+
4,215 reviews
Updated 01/2026
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 hr16 videos57 Exercises4,650 XP170K+Statement of Accomplishment

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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.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.

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What you'll learn

  • Assess date and timestamp fields through extraction, truncation, interval arithmetic, and generate_series to construct comprehensive temporal analyses
  • Evaluate numeric variables with aggregate, variance, correlation, and binning functions to summarize distributions and detect anomalies
  • Identify key database tables, relationships, and data types required for exploratory analysis in PostgreSQL
  • Identify and apply PostgreSQL data capabilities including core/complex data types, full-text search, and extensibility via functions, types, and extensions
  • Clean and summarize categorical and unstructured text using string operations, pattern matching, and temporary tables

Prerequisites

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.
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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

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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4

Working with Dates and Timestamps

Exploratory Data Analysis in SQL
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FAQs

Will I receive a certificate at the end of the course?

Yes, you will receive a DataCamp course certificate after completing all the course lessons.

Who will benefit from this course?

Exploratory Data Analysis in SQL is essential for anyone who works with data, but especially relevant to those in data science, data engineering, analytics, and business intelligence.

What types of data will be explored in the course?

The course introduces you to numeric, character, and date/time data types, using data from Stack Overflow, Fortune 500 companies, and 311 help requests from Evanston, IL.

What types of functions will be used in the course?

You will learn how to use numeric data functions such as min, max, average, variance, correlation, and percentile. As well as look at text or character data functions such as case, spacing, and delimiters.

What strategies can be used to deal with messy data?

You will learn to look for common problems in data and strategies to clean up messy data. These include identifying missing values, counting the number of observations, joining tables to understand relationships, coalescing and casting data, and using temporary tables to recode messy categorical data.

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