- Basics of time series data
- Writing time series data
- Querying time series data
- Installing PostgreSQL
- Evaluating query performance
- Joining time series
- Denormalizing time series
- Indexing data
- Querying a partitioned table
- Functions for time series
- Calculating aggregates over windows
- Calculating moving averages
- Forecasting with linear regression
Skill Level Advanced
- [Dan] More and more data is being collected from sensors, user interactions with web applications and performance metrics. One thing all of these have in common is that they can be modeled as a series of events that happen over time. Analyzing time series data like this can be challenging, but SQL has evolved to include features that support just the kind of analysis we need. In this course, you'll learn about time series data, ways of working with is using abstractions like sliding windows and tumbling windows, and you'll also learn how to use SQL constructs, like over and partition by, to simplify analysis of time series data. You'll also learn about the benefits of denormalizing time series data to avoid joins and when to limit your use of denormalizing. By the end of the course, you'll be familiar with common analysis patterns, like moving averages, comparisons across periods, exponential smoothing, and forecasting with linear regression. My name is Dan Sullivan, and I'm a principal engineer and architect working on large scale time series and machine learning applications.
1. Introduction to Time Series Data
2. Installing Database and Tools
3. Querying Time Series Data
4. Modeling Time Series Data
5. Commonly Used Functions for Time Series
6. Time Series Analysis
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.Cancel
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.