course
Time Series Analysis in Python
Intermediate
Updated 12/2024Start course for free
Included for FreePremium or Teams
PythonProbability & Statistics4 hours17 videos59 exercises4,850 XP61,918Statement of Accomplishment
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.Training 2 or more people?
Try DataCamp for BusinessLoved by learners at thousands of companies
Course Description
Learn How to Use Python for Time Series Analysis
From stock prices to climate data, you can find time series data in a wide variety of domains. Having the skills to work with such data effectively is an increasingly important skill for data scientists. This course will introduce you to time series analysis in Python.After learning what a time series is, you'll explore several time series models, ranging from autoregressive and moving average models to cointegration models. Along the way, you'll learn how to estimate, forecast, and simulate these models using statistical libraries in Python.
You'll see numerous examples of how these models are used, with a particular emphasis on applications in finance.
Discover How to Use Time Series Methods
You’ll start by covering the fundamentals of time series data, as well as simple linear regression. You’ll cover concepts of correlation and autocorrelation and how they apply to time series data before exploring some simple time series models, such as white noise and a random walk. Next, you’ll explore how autoregressive (AR) models are used for time series data to predict current values and how moving average models can combine with AR models to produce powerful ARMA models.Finally, you’ll look at how to use cointegration models to model two series jointly before looking at a real-life case study.
Explore Python Models and Libraries for Time Series Analysis By the end of this course, you’ll understand how time series analysis in Python works. You’ll know about some of the models, methods, and libraries that can assist you with the process and will know how to choose the appropriate ones for your own analysis.
This course is part of a wider Time Series with Python Track, which provides a set of five courses to help you master this data science skill.
Prerequisites
Manipulating Time Series Data in Python1
Correlation and Autocorrelation
2
Some Simple Time Series
3
Autoregressive (AR) Models
4
Moving Average (MA) and ARMA Models
5
Putting It All Together
Time Series Analysis in Python
Course Complete
Earn Statement of Accomplishment
Add this credential to your LinkedIn profile, resume, or CVShare it on social media and in your performance review
Included withPremium or Teams
Enroll nowFAQs
Join over 15 million learners and start Time Series Analysis in Python today!
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.