Course
Visualizing Time Series Data in Python
IntermediateSkill Level
Updated 03/2022PythonData Visualization4 hr17 videos59 Exercises4,850 XP26,311Statement of Accomplishment
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Prerequisites
Introduction to Data Visualization with MatplotlibManipulating Time Series Data in Python1
Line Plots
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.
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.
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.
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.
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.
Visualizing Time Series Data in Python
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FAQs
What Python visualization libraries are used in this course?
The course primarily uses Matplotlib for creating time series plots, building on your existing knowledge of pandas for data manipulation and time series handling.
Does the course cover identifying seasonality and trends in time series data?
Yes. A full chapter is dedicated to decomposing time series into seasonality, trend, and noise components using autocorrelation and partial autocorrelation analysis.
What prior knowledge of time series data do I need?
You should have experience manipulating time series data in Python with pandas, plus basic Matplotlib skills and intermediate Python knowledge.
Will I learn how to annotate and customize time series plots?
Yes. The first chapter covers annotating plots, personalizing visual elements, and producing compelling static visualizations from any time-indexed dataset.
What summary statistics and diagnostic tools for time series are taught?
You will compute and plot aggregated views of your data, then use autocorrelation functions and spectral analysis to understand temporal patterns and data quality.
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