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Grzegorz Gąsiewski avatar

Grzegorz Gąsiewski has completed

Time Series Analysis in SQL Server

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5 hours
5,150 XP
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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.
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  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.

    Play Chapter Now
    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
  2. 2

    Converting to Dates and Times

    Here, we'll be converting strings and other inputs to date and time data types.

    Play Chapter Now
    Building dates from parts
    50 xp
    Build dates from parts
    100 xp
    Build dates and times from parts
    100 xp
    Build dates and times with offsets from parts
    100 xp
    Translating date strings
    50 xp
    Cast strings to dates
    100 xp
    Convert strings to dates
    100 xp
    Parse strings to dates
    100 xp
    Working with offsets
    50 xp
    Changing a date's offset
    100 xp
    Using the time zone DMV to look up times
    100 xp
    Converting to a date offset
    100 xp
    Handling invalid dates
    50 xp
    Try out type-safe date functions
    100 xp
    Convert imported data to dates with time zones
    100 xp
    Test type-safe conversion function performance
    100 xp
  3. 3

    Aggregating Time Series Data

    In this chapter, we will learn techniques to aggregate data over time. We will briefly review aggregation functions and statistical aggregation functions. We will cover upsampling and downsampling of data. Finally, we will look at the grouping operators.

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    Basic aggregate functions
    50 xp
    Summarize data over a time frame
    100 xp
    Calculating distinct counts
    100 xp
    Calculating filtered aggregates
    100 xp
    Statistical aggregate functions
    50 xp
    Working with statistical aggregate functions
    100 xp
    Calculating median in SQL Server
    100 xp
    Downsampling and upsampling data
    50 xp
    Downsample to a daily grain
    100 xp
    Downsample to a weekly grain
    100 xp
    Downsample using a calendar table
    100 xp
    Grouping by ROLLUP, CUBE, and GROUPING SETS
    50 xp
    Generate a summary with ROLLUP
    100 xp
    View all aggregations with CUBE
    100 xp
    Generate custom groupings with GROUPING SETS
    100 xp
    Combine multiple aggregations in one query
    100 xp
  4. 4

    Answering Time Series Questions with Window Functions

    In this chapter, we will learn how to use window functions to perform calculations over time, including calculating running totals and moving averages, calculating intervals, and finding the maximum levels of overlap.

    Play Chapter Now
    Using aggregation functions over windows
    50 xp
    Contrasting ROW_NUMBER(), RANK(), and DENSE_RANK()
    100 xp
    Aggregate window functions
    100 xp
    Calculating running totals and moving averages
    50 xp
    Running totals with SUM()
    100 xp
    Investigating window frames
    100 xp
    Calculating moving averages
    100 xp
    Working with LAG() and LEAD()
    50 xp
    Seeing prior and future periods
    100 xp
    Seeing the prior three periods
    100 xp
    Calculating days elapsed between incidents
    100 xp
    Finding maximum levels of overlap
    50 xp
    Analyze client data for potential fraud
    100 xp
    Build a stream of events
    100 xp
    Complete the fraud analysis
    100 xp
    Wrapping up
    50 xp
For Business

GroupTraining 2 or more people?

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datasets

CalendarDay Spa RollupDay Spa VisitIncident RollupIncident TypeImported Time

collaborators

Collaborator's avatar
Chester Ismay
Collaborator's avatar
Mona Khalil

prerequisites

Intermediate SQL Server
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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