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Cleaning Data in PostgreSQL Databases

Learn to tame your raw, messy data stored in a PostgreSQL database to extract accurate insights.

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4 Hours15 Videos49 Exercises2,429 Learners4050 XP

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Course Description

If you surveyed a large number of data scientists and data analysts about which tasks are most common in their workday, cleaning data would likely be in almost all responses. This is the case because real-world data is messy. To help you tame messy data, this course teaches you how to clean data stored in a PostgreSQL database. You’ll learn how to solve common problems such as how to clean messy strings, deal with empty values, compare the similarity between strings, and much more. You’ll get hands-on practice with these tasks using interesting (but messy) datasets made available by New York City's Open Data program. Are you ready to whip that messy data into shape?

  1. 1

    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.

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    Introduction to data cleaning
    50 xp
    Developing a data cleaning mindset
    50 xp
    Applying functions for string cleaning
    100 xp
    Pattern matching
    50 xp
    Classifying parking violations by time of day
    100 xp
    Masking identifying information with regular expressions
    100 xp
    Matching similar strings
    50 xp
    Matching inconsistent color names
    100 xp
    Standardizing color names
    100 xp
    Standardizing multiple colors
    100 xp
    Formatting text for colleagues
    100 xp
  2. 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.

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  3. 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().

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Parking violations in NYCRestaurant inspections in NYCFilm permits in NYC


maggiematsuiMaggie Matsuiamy-4121b590-cc52-442a-9779-03eb58089e08Amy Peterson


Intermediate SQL
Darryl Reeves Ph.D Headshot

Darryl Reeves Ph.D

Industry Assistant Professor, NYU Tandon School of Engineering

Darryl is a computational scientist with expertise in utilizing data-driven approaches to solve complex problems in both academic and business settings. He worked for a number of years in a variety of technical roles including software development and technology-based client services mostly within start-up organizations in the finance and online advertising industries. He has a love for technology and education and enjoys solving interesting problems across diverse domains.
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