After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
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.
Feels like what you want to learn?
Start Course for FreeWhat you'll learn
- Assess the necessity and effectiveness of dashboard elements to implement “less is more” practices that align with stakeholder objectives
- Distinguish between Power BI dashboards and paginated reports, including their design requirements and business applications
- Evaluate dashboards for cognitive load and apply chunking, negative space, and color principles to streamline information delivery
- Identify optimal Power BI visuals for presenting categorical, temporal, and hierarchical data to various professional audiences
- Recognize techniques for invoking appropriate emotional responses through data visualization choices in executive reporting
Prerequisites
Curriculum
Course outline
1
Introduction to Matplotlib
This chapter introduces the Matplotlib visualization library and demonstrates how to use it with data.
- Introduction to data visualization with Matplotlib50 XP
- Using the matplotlib.pyplot interface100 XP
- Adding data to an Axes object100 XP
- Customizing your plots50 XP
- Customizing data appearance100 XP
- Customizing axis labels and adding titles100 XP
- Small multiples50 XP
- Creating a grid of subplots50 XP
- Creating small multiples with plt.subplots100 XP
- Small multiples with shared y axis100 XP
2
Plotting time-series
Time series data is data that is recorded. Visualizing this type of data helps clarify trends and illuminates relationships between data.
3
Quantitative comparisons and statistical visualizations
Visualizations can be used to compare data in a quantitative manner. This chapter explains several methods for quantitative visualizations.
4
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.
Introduction to Data Visualization with Matplotlib
Course
Complete
