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Cleaning Data in SQL Server Databases

Develop the skills you need to clean raw data and transform it into accurate insights.

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4 Hours13 Videos48 Exercises6,339 Learners3750 XP

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Course Description

Did you know that data scientists and data analysts spend a large amount of time cleaning data before they can analyze it? This is because real-world data is messy. To help you navigate messy data this course teaches you how to clean data stored in an SQL Server 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 with all these tasks using a wide range of interesting and messy datasets, including monthly airline flights by airport, TV series and paper shop sales. Are you ready to get your hands messy?

  1. 1

    Starting with Cleaning Data


    To begin the course, you will learn why cleaning data is important. You will solve simple problems such as leading and trailing spaces in strings, unifying formats for flight registrations, combining strings and more.

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    Introduction to Cleaning Data
    50 xp
    Unifying flight formats I
    100 xp
    Unifying flight formats II
    100 xp
    Cleaning messy strings
    50 xp
    Trimming strings I
    100 xp
    Trimming strings II
    100 xp
    Unifying strings
    100 xp
    Comparing the similarity between strings
    50 xp
    50 xp
    Comparing names with SOUNDEX()
    100 xp
    Comparing names with DIFFERENCE()
    100 xp
  2. 2

    Dealing with missing data, duplicate data, and different date formats

    In this chapter, you will deepen your data cleaning knowledge. You will learn how to deal with missing data, avoid duplicate data in your datasets, and work with different formats of dates.

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  3. 3

    Dealing with out of range values, different data types, and pattern matching

    In this chapter, you will deal with out of range values and inaccurate data. You will also practice converting data with different types. Finally, you will work on matching patterns to your data to find outliers.

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Flight statistics dataset and series ratings dataset


amy-4121b590-cc52-442a-9779-03eb58089e08Amy Peterson
Miriam Antona Headshot

Miriam Antona

Software Engineer

Miriam Antona has 10+ years of experience working as a Software Engineer. She is focused on analyzing, designing, and developing software applications for the Justice Administration. Miriam loves programming and experimenting with different technologies. She is passionate about databases and enjoys playing with data. She holds a Master of Science Degree in Computer Engineering.
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