Course
Cleaning Data in PostgreSQL Databases
IntermediateSkill Level
Updated 09/2022SQLData Preparation4 hr15 videos49 Exercises4,050 XP13,950Statement of Accomplishment
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Prerequisites
Data Manipulation in SQL1
Data Cleaning Basics
In this chapter, you’ll gain an understanding of data cleaning approaches when working with PostgreSQL databases and learn the value of cleaning data as early as possible in the pipeline. You’ll also learn basic string editing approaches such as removing unnecessary spaces as well as more involved topics such as pattern matching and string similarity to identify string values in need of cleaning.
2
Missing, Duplicate, and Invalid Data
You’ll learn how to write queries to solve common problems of missing, duplicate, and invalid data in the context of PostgreSQL database tables. Through hands-on exercises, you’ll use the COALESCE() function, SELECT query, and WHERE clause to clean messy data.
3
Converting Data
Sometimes you need to convert data stored in a PostgreSQL database from one data type to another. In this chapter, you’ll explore the expressions you need to convert text to numeric types and how to format strings for temporal data.
4
Transforming Data
In the final chapter, you’ll learn how to transform your data and construct pivot tables. Working with real-world postal data, you’ll discover how to combine and split addresses into city, state, and zip codes using a multitude of powerful functions including CONCAT(), SUBSTRING(), and REGEXP_SPLIT_TO_TABLE().
Cleaning Data in PostgreSQL Databases
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FAQs
What PostgreSQL functions will I learn for cleaning messy data?
You learn COALESCE for missing data, pattern matching and string similarity functions, CAST for type conversion, and CONCAT, SUBSTRING, and REGEXP_SPLIT_TO_TABLE for transforming data.
What real-world datasets are used in the exercises?
You work with datasets from New York City's Open Data program, including postal data that you split into city, state, and zip code components in the final chapter.
Does the course cover handling missing and duplicate data?
Yes. Chapter 2 is dedicated to solving problems with missing, duplicate, and invalid data using techniques like COALESCE, targeted SELECT queries, and WHERE clause filtering.
Will I learn to convert data types in PostgreSQL?
Yes. Chapter 3 covers converting text to numeric types and formatting strings as temporal data, which are common tasks when cleaning data stored in PostgreSQL databases.
What SQL background do I need for this course?
You need Introduction to SQL, Intermediate SQL, Data Manipulation in SQL, and Joining Data in SQL. This intermediate course builds on solid SQL foundations.
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