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Time Series Analysis in SQL Server

Explore ways to work with date and time data in SQL Server for time series analysis

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5 Hours16 Videos60 Exercises
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Course Description

SQL Server has a robust set of tools to prepare, aggregate, and query time series data. This course will show you how to build and work with dates, parse dates from strings (and deal with invalid strings), and format dates for reporting. From there, you will see how SQL Server's built-in aggregation operators and window functions can solve important business problems like calculating running totals, finding moving averages, and displaying month-over-month differences using realistic sample data sets. You will also see how taking a different perspective on your data can solve difficult problems.
  1. 1

    Working with Dates and Times

    Free

    This chapter covers date and time functionality in SQL Server, including building dates from component parts, formatting dates for reporting, and working with calendar tables.

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    Building dates
    50 xp
    Break out a date into year, month, and day
    100 xp
    Break a date and time into component parts
    100 xp
    Date math and leap years
    100 xp
    Rounding dates
    100 xp
    Formatting dates for reporting
    50 xp
    Formatting dates with CAST() and CONVERT()
    100 xp
    Formatting dates with FORMAT()
    100 xp
    Working with calendar tables
    50 xp
    The benefits of calendar tables
    50 xp
    Try out a calendar table
    100 xp
    Joining to a calendar table
    100 xp

In the following tracks

SQL Server DeveloperSQL Server Fundamentals

Collaborators

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Mona Khalil
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Chester Ismay
Maham Khan HeadshotMaham Khan

Senior Data Scientist, YouView TV

Maham is a Data Scientist on a mission to make data skills accessible for everyone. She's worked on creating toolkits and exploring experimental applications of data science for urban analytics, disaster risk management, and climate change mitigation at the World Bank. She has a background in Experimental Psychology and Philosophy from the University of Oxford and Urban Data Science from NYU.
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