In 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 types

PostgreSQL data types

INSERT time INTO table

Working with time zone information

Find the time zones

Convert to a different time zone

Converting between date, time, and text

Casting dates

Converting dates

Converting date and times

Introduction to Date and Time Data in PostgreSQL

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 data

Temperatures by century

Busiest hour of day

Adding and subtracting date and time data

Calculate the age

Calculate arrival time in minutes

Calculating intervals

Aggregating time series data

Count the number of data per time series

Aggregating temperature data

Calculate yearly average temperature

Applying statistical aggregates to time series data

Calculating monthly temperature swings

Analyze data distribution

Sensor data analysis

Working with Time Series

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 functions

Partitioned table

Partition by

Top items with window functions

Find the three highest values per partition

Most active hours of day per news article

Ranking functions

Ranking cold weather

Ranking hot weather

Temperature percentile

Using Window Functions to Analyze Time Series Data

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.

Running total

Next station

Calculate a running total

Running total with lag

Running average

Single running average

Multi running average

Moving Average

Identifying trending news

Running average and moving average yearly temperature

Wrap-Up

Calculating Running Totals and Moving Averages

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 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.

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.
<br><br>
<h2>Work with time series data</h2>
<br><br>
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.
<br><br>
<h2>Apply time series analysis to real-world data</h2>
<br><br>
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.

Joining Data in SQL

Learn how to use PostgreSQL to handle time series analysis effectively and apply these techniques to real-world data.

Learn About Time Series Analysis in PostgreSQL

Time Series Analysis in PostgreSQL

Content Developer, DataCamp

What is a running total?

Calculate the running total

The dataset: a train schedule

Lead function

Using the lead function

Calculating duration of each stop

Lag function

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_30637/subtitles/course_30637_e1bed4bd42140488d8137e66d4741fb0.vtt

Calculating Running Totals and Moving Averages - Running total

Running total

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