Python for Time Series Analysis

68 students

Time series analysis is used to model behavior and make predictions about the future. This workshop will provide you with an introduction to time series analysis. After completing this workshop, you will be able to use Python to perform time series analyses on time dependent datasets. Specifically, you’ll learn

  • Data characteristics, summary and usability
  • What is time series data trend and seasonality
  • Dickey-Fuller testing and estimating trend and seasonality
  • How to model time series data using ARIMA
  • How to use your model to make predictions

You will learn these tools within the context of solving real world data science problems. This workshop features:

  • Two (2) lesson modules with video lectures and interactive Jupyter notebooks
  • One (1) lesson quiz
  • Five (5) lesson exercises
  • One (1) end of workshop project that demonstrates time series analysis in the real world

This is a self-paced workshop that contains assignments and quizzes without specified due dates. You can progress through the workshop at your own pace or at the speed set by your instructor. This workshop is great for young and/or beginner data scientists and requires no prerequisite programming knowledge.

Prerequisites suggested: Python for Data Science, Python for Data Visualizations, Python for A/B Testing, and Python for Data Preprocessing


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