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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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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?
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  1. 1

    Starting with Cleaning Data

    Free

    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.

    Play Chapter Now
    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
    SOUNDEX() and DIFFERENCE()
    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.

    Play Chapter Now
    Dealing with missing data
    50 xp
    Removing missing values
    100 xp
    Removing blank spaces
    100 xp
    Filling missing values using ISNULL()
    100 xp
    Filling missing values using COALESCE()
    100 xp
    Avoiding duplicate data
    50 xp
    Diagnosing duplicates
    50 xp
    Treating duplicates
    100 xp
    Dealing with different date formats
    50 xp
    Using CONVERT()
    100 xp
    Using FORMAT()
    100 xp
    CONVERT() vs FORMAT()
    50 xp
  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.

    Play Chapter Now
    Out of range values and inaccurate data
    50 xp
    Out of range values or inaccurate data?
    50 xp
    Detecting out of range values
    100 xp
    Excluding out of range values
    100 xp
    Detecting and excluding inaccurate data
    100 xp
    Converting data with different types
    50 xp
    Using CAST() and CONVERT()
    100 xp
    The series with most episodes
    50 xp
    Pattern matching
    50 xp
    Characters to specify a patterns
    50 xp
    Matching urls
    100 xp
    Checking phone numbers
    100 xp
  4. 4

    Combining, splitting, and transforming data

    In this final chapter, you will learn how to combine and aggregate data of some columns into one, split data of one column into more columns, and transform rows into columns and vice versa.

    Play Chapter Now
    Combining data of some columns into one column
    50 xp
    Combining cities and states using +
    100 xp
    Concatenating cities and states
    100 xp
    Working with DATEFROMPARTS()
    100 xp
    Splitting data of one column into more columns
    50 xp
    Using SUBSTRING() and CHARINDEX()
    100 xp
    Using RIGHT() , LEFT() and REVERSE()
    100 xp
    SUBSTRING() or CHARINDEX()?
    50 xp
    Transforming rows into columns and vice versa
    50 xp
    PIVOT or UNPIVOT?
    50 xp
    Turning rows into columns
    100 xp
    Turning columns into rows
    100 xp
    Congratulations!
    50 xp
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datasets

Flight statistics dataset and series ratings dataset

collaborators

Collaborator's avatar
Amy Peterson

prerequisites

Intermediate SQL Server
Miriam Antona HeadshotMiriam Antona

Software Engineer

Miriam is a freelance Software Engineer with 15+ years of experience. She is focused on analyzing, designing, and developing software applications. She also collaborates with the UOC University supervising Bachelor theses. 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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