Visualizing Time Series Data in Python

Visualize seasonality, trends and other patterns in your time series data.
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4 Hours17 Videos59 Exercises13,478 Learners
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Course Description

Time series data is omnipresent in the field of Data Science. Whether it is analyzing business trends, forecasting company revenue or exploring customer behavior, every data scientist is likely to encounter time series data at some point during their work. To get you started on working with time series data, this course will provide practical knowledge on visualizing time series data using Python.

  1. 1


    You will learn how to leverage basic plottings tools in Python, and how to annotate and personalize your time series plots. By the end of this chapter, you will be able to take any static dataset and produce compelling plots of your data.
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  2. 2

    Summary Statistics and Diagnostics

    In this chapter, you will gain a deeper understanding of your time series data by computing summary statistics and plotting aggregated views of your data.
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  3. 3

    Seasonality, Trend and Noise

    You will go beyond summary statistics by learning about autocorrelation and partial autocorrelation plots. You will also learn how to automatically detect seasonality, trend and noise in your time series data.
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  4. 4

    Work with Multiple Time Series

    In the field of Data Science, it is common to be involved in projects where multiple time series need to be studied simultaneously. In this chapter, we will show you how to plot multiple time series at once, and how to discover and describe relationships between multiple time series.
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  5. 5

    Case Study

    This chapter will give you a chance to practice all the concepts covered in the course. You will visualize the unemployment rate in the US from 2000 to 2010.
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Time Series
Sumedh PanchadharLore Dirick
Thomas Vincent Headshot

Thomas Vincent

Head of Data Science at Getty Images
Thomas is an experienced statistician and programmer who is passionate about developing tools and pipelines to discover and retrieve underlying phenomenons and patterns in modern-day datasets. He enjoys applying his statistical skills to solve practical problems and blogs about his analyses at
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