Loved by learners at thousands of companies
Visualizing data in plots and figures exposes the underlying patterns in the data and provides insights. Good visualizations also help you communicate your data to others, and are useful to data analysts and other consumers of the data. In this course, you will learn how to use Matplotlib, a powerful Python data visualization library. Matplotlib provides the building blocks to create rich visualizations of many different kinds of datasets. You will learn how to create visualizations for different kinds of data and how to customize, automate, and share these visualizations.
Introduction to MatplotlibFree
This chapter introduces the Matplotlib visualization library and demonstrates how to use it with data.Introduction to data visualization with Matplotlib50 xpUsing the matplotlib.pyplot interface100 xpAdding data to an Axes object100 xpCustomizing your plots50 xpCustomizing data appearance100 xpCustomizing axis labels and adding titles100 xpSmall multiples50 xpCreating a grid of subplots50 xpCreating small multiples with plt.subplots100 xpSmall multiples with shared y axis100 xp
Time series data is data that is recorded. Visualizing this type of data helps clarify trends and illuminates relationships between data.Plotting time-series data50 xpRead data with a time index100 xpPlot time-series data100 xpUsing a time index to zoom in100 xpPlotting time-series with different variables50 xpPlotting two variables100 xpDefining a function that plots time-series data100 xpUsing a plotting function100 xpAnnotating time-series data50 xpAnnotating a plot of time-series data100 xpPlotting time-series: putting it all together100 xp
Quantitative comparisons and statistical visualizations
Visualizations can be used to compare data in a quantitative manner. This chapter explains several methods for quantitative visualizations.Quantitative comparisons: bar-charts50 xpBar chart100 xpStacked bar chart100 xpQuantitative comparisons: histograms50 xpCreating histograms100 xp"Step" histogram100 xpStatistical plotting50 xpAdding error-bars to a bar chart100 xpAdding error-bars to a plot100 xpCreating boxplots100 xpQuantitative comparisons: scatter plots50 xpSimple scatter plot100 xpEncoding time by color100 xp
Sharing visualizations with others
This chapter shows you how to share your visualizations with others: how to save your figures as files, how to adjust their look and feel, and how to automate their creation based on input data.Preparing your figures to share with others50 xpSelecting a style for printing50 xpSwitching between styles100 xpSaving your visualizations50 xpSaving a file several times100 xpSave a figure with different sizes100 xpAutomating figures from data50 xpUnique values of a column100 xpAutomate your visualization100 xpWhere to go next50 xp
PrerequisitesIntroduction to Python
Senior Data Scientist, University of Washington
Ariel Rokem is a Data Scientist at the University of Washington eScience Institute. He received a PhD in neuroscience from UC Berkeley, and postdoctoral training in computational neuroimaging at Stanford. In his work, he develops data science algorithms and tools, and applies them to analysis of neural data. He is also a contributor to multiple open-source software projects in the scientific Python ecosystem.
What do other learners have to say?
“I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyds Banking Group
“DataCamp is the top resource I recommend for learning data science.”
Harvard Business School
“DataCamp is by far my favorite website to learn from.”
Decision Science Analytics, USAA