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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?
Starting with Cleaning DataFree
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.Introduction to Cleaning Data50 xpUnifying flight formats I100 xpUnifying flight formats II100 xpCleaning messy strings50 xpTrimming strings I100 xpTrimming strings II100 xpUnifying strings100 xpComparing the similarity between strings50 xpSOUNDEX() and DIFFERENCE()50 xpComparing names with SOUNDEX()100 xpComparing names with DIFFERENCE()100 xp
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.Dealing with missing data50 xpRemoving missing values100 xpRemoving blank spaces100 xpFilling missing values using ISNULL()100 xpFilling missing values using COALESCE()100 xpAvoiding duplicate data50 xpDiagnosing duplicates50 xpTreating duplicates100 xpDealing with different date formats50 xpUsing CONVERT()100 xpUsing FORMAT()100 xpCONVERT() vs FORMAT()50 xp
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.Out of range values and inaccurate data50 xpOut of range values or inaccurate data?50 xpDetecting out of range values100 xpExcluding out of range values100 xpDetecting and excluding inaccurate data100 xpConverting data with different types50 xpUsing CAST() and CONVERT()100 xpThe series with most episodes50 xpPattern matching50 xpCharacters to specify a patterns50 xpMatching urls100 xpChecking phone numbers100 xp
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.Combining data of some columns into one column50 xpCombining cities and states using +100 xpConcatenating cities and states100 xpWorking with DATEFROMPARTS()100 xpSplitting data of one column into more columns50 xpUsing SUBSTRING() and CHARINDEX()100 xpUsing RIGHT() , LEFT() and REVERSE()100 xpSUBSTRING() or CHARINDEX()?50 xpTransforming rows into columns and vice versa50 xpPIVOT or UNPIVOT?50 xpTurning rows into columns100 xpTurning columns into rows100 xpCongratulations!50 xp
PrerequisitesIntermediate SQL Server
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.