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Visualizing Time Series Data in Python

Visualize seasonality, trends and other patterns in your time series data.

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4 Hours17 Videos59 Exercises19,682 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

    Line Plots

    Free

    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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    Welcome to the course!
    50 xp
    Load your time series data
    100 xp
    Test whether your data is of the correct type
    100 xp
    Plot your first time series
    50 xp
    Your first plot!
    100 xp
    Specify plot styles
    100 xp
    Display and label plots
    100 xp
    Customize your time series plot
    50 xp
    Subset time series data
    100 xp
    Add vertical and horizontal markers
    100 xp
    Add shaded regions to your plot
    100 xp
  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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    Clean your time series data
    50 xp
    Find missing values
    100 xp
    Handle missing values
    100 xp
    Plot aggregates of your data
    50 xp
    Display rolling averages
    100 xp
    Display aggregated values
    100 xp
    Summarize the values in your time series data
    50 xp
    Compute numerical summaries
    100 xp
    Boxplots and Histograms
    100 xp
    Density plots
    100 xp
  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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    Autocorrelation and Partial autocorrelation
    50 xp
    Autocorrelation in time series data
    100 xp
    Interpret autocorrelation plots
    50 xp
    Partial autocorrelation in time series data
    100 xp
    Interpret partial autocorrelation plots
    50 xp
    Seasonality, trend and noise in time series data
    50 xp
    Time series decomposition
    100 xp
    Plot individual components
    100 xp
    A quick review
    50 xp
    Visualize the airline dataset
    100 xp
    Analyze the airline dataset
    100 xp
    Time series decomposition of the airline dataset
    100 xp
  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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    Working with more than one time series
    50 xp
    Load multiple time series
    100 xp
    Visualize multiple time series
    100 xp
    Statistical summaries of multiple time series
    50 xp
    Plot multiple time series
    50 xp
    Define the color palette of your plots
    100 xp
    Add summary statistics to your time series plot
    100 xp
    Plot your time series on individual plots
    100 xp
    Find relationships between multiple time series
    50 xp
    Compute correlations between time series
    100 xp
    Visualize correlation matrices
    100 xp
    Clustered heatmaps
    100 xp
  5. 5

    Case Study: Unemployment Rate

    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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    Apply your knowledge to a new dataset
    50 xp
    Explore the Jobs dataset
    100 xp
    Describe time series data with boxplots
    100 xp
    Beyond summary statistics
    50 xp
    Plot all the time series in your dataset
    100 xp
    Annotate significant events in time series data
    100 xp
    Plot monthly and yearly trends
    100 xp
    Decompose time series data
    50 xp
    Apply time series decomposition to your dataset
    100 xp
    Visualize the seasonality of multiple time series
    100 xp
    Compute correlations between time series
    50 xp
    Correlations between multiple time series
    100 xp
    Interpret correlations
    50 xp
    Congratulations!
    50 xp

In the following tracks

Time Series with Python

Datasets

Inventions and Scientific DiscoveriesCO2 levels in Mauai HawaiiAirline passengersProduction of meat in USAUnemployment rate in USA

Collaborators

Lore Dirick
Sumedh Panchadhar

Prerequisites

Introduction to Data Visualization with MatplotlibManipulating Time Series Data in Python
Thomas Vincent HeadshotThomas 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 tlfvincent.github.io.
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