Time Series Analysis in PostgreSQL
Learn how to use PostgreSQL to handle time series analysis effectively and apply these techniques to real-world data.
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Course Description
This course teaches you how to leverage PostgreSQL to handle date and time data. You'll learn about functions and calls to help you parse through and manipulate this data, make calculations, and use window functions.
You’ll learn about various date and time data types and how to convert between them, manipulate their granularity, and perform calculations, including aggregations, partitioning, and running averages. These insights will help you add value to existing time series data.
You'll apply these techniques to real-world data to analyze temperatures, look at train schedules, and review how the popularity of news articles can change over time.
Work with time series data
You’ll learn about various date and time data types and how to convert between them, manipulate their granularity, and perform calculations, including aggregations, partitioning, and running averages. These insights will help you add value to existing time series data.
Apply time series analysis to real-world data
You'll apply these techniques to real-world data to analyze temperatures, look at train schedules, and review how the popularity of news articles can change over time.
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.- 1
Introduction to Date and Time Data in PostgreSQL
FreeIn this chapter, you’ll be introduced to date and time data types. You’ll learn how to convert text and numeric data to date and time format—and how to convert the other way around too!
Introduction to date and time data types50 xpPostgreSQL data types50 xpINSERT time INTO table100 xpWorking with time zone information50 xpFind the time zones100 xpConvert to a different time zone100 xpConverting between date, time, and text50 xpCasting dates100 xpConverting dates100 xpConverting date and times100 xp - 2
Working with Time Series
It’s time to get granular. In this chapter, you’ll learn how to set the granularity of your time series reports. You’ll then get to grips with adding, subtracting, and aggregating as you discover how to analyze time series data.
Manipulating the granularity of time series data50 xpTemperatures by century100 xpBusiest hour of day100 xpAdding and subtracting date and time data50 xpCalculate the age50 xpCalculate arrival time in minutes100 xpCalculating intervals100 xpAggregating time series data50 xpCount the number of data per time series100 xpAggregating temperature data100 xpCalculate yearly average temperature100 xpApplying statistical aggregates to time series data50 xpCalculating monthly temperature swings100 xpAnalyze data distribution100 xpSensor data analysis100 xp - 3
Using Window Functions to Analyze Time Series Data
In this chapter, you’ll work with window functions. You'll begin learning about partitions and partitioning and how they work with window functions. You'll be able to find the top items when ranking your data.
Partitioning and window functions50 xpPartitioned table100 xpPartition by100 xpTop items with window functions50 xpFind the three highest values per partition100 xpMost active hours of day per news article100 xpRanking functions50 xpRanking cold weather100 xpRanking hot weather100 xpTemperature percentile100 xp - 4
Calculating Running Totals and Moving Averages
In the final chapter, you’ll level up your skills by calculating the running total, running average, and even moving average to enhance your time series analysis.
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prerequisites
Joining Data in SQL
Jasmin LudolfData Science Content Developer, DataCamp
Jasmin is a Content Developer at DataCamp. After ten years as a global marketing manager in the music industry, she recently changed careers to follow her curiosity for data. Her passion is value exchange and making data science accessible to all.
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