Building Data Visualizations Using Matplotlib from Pluralsight

Matplotlib is one of the most popular visualization libraries used by data analysts and data scientists working in Python, but can often be intimidating to use. This course serves to make working with Matplotlib easy and simple.

This course will focus on making Matplotlib accessible and easily understandable to a Data Scientist or Business Analyst who needs to quickly and visually come to grips with relationships in a large dataset. In this course, Building Data Visualizations Using Matplotlib, you'll discover the basic components which make up a plot and see how you can tweak parameters and attributes to have the visualizations customized to exactly how you want it. First, you’ll grow to understand the basic APIs available in Matplotlib and where they are used and learn how to customize the display, colors, and other attributes of these plots which will have multiple axes. Next, you’ll build intermediate and advanced plots, drawing shapes and Bezier curves, using text and annotations to highlight plot elements, and normalizing the scales that are used on the x and y-axis. Lastly, you’ll use some real-world data to visualize statistical data such as mean, median mode, and outliers, cover box plots, violin plots, histograms, pie charts, stem and stack plots and autocorrelations graphs. By the end of this course you’ll not only have explored all the nitty gritty that Matplotlib has to offer; but you’ll also be capable of building production-ready visuals to embed with your UI or to display within reports and presentations.

What's inside

Syllabus

Course Overview

Working with the Matplotlib and Pyplot APIs

Building Basic, Intermediate, and Advanced Plots with Matplotlib

Visualizing Statistical Data with Matplotlib

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches tools and techniques for creating visualizations with Matplotlib, which is a valuable skill for both data analysis and data science

Covers essential components of Matplotlib, including basic APIs, customization options, and advanced plot types

Provides a clear understanding of statistical data visualization using Matplotlib

Introduces real-world examples to demonstrate the practical applications of Matplotlib

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Building Data Visualizations Using Matplotlib with these activities:

Review NumPy basics

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Review NumPy basics to ensure you have a solid foundation for working with Matplotlib.

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Go over NumPy documentation or tutorials to refresh your memory.
Practice using NumPy functions for data manipulation and array operations.

Follow Matplotlib tutorials

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Follow guided tutorials to gain a deeper understanding of specific Matplotlib features and techniques.

Browse courses on Matplotlib

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Identify specific areas of Matplotlib you want to improve upon.
Search for and select reputable Matplotlib tutorials that cover those areas.
Follow the tutorials step-by-step, taking notes and practicing the techniques.

Plot multiple graphs with different axes

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Practice creating plots with different axes to gain proficiency in customizing Matplotlib visualizations.

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Create a figure and multiple subplots
Set the x and y axes labels
Plot data on the subplots
Adjust the layout and spacing
Save the figure

Eight other activities

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Work through Matplotlib exercises

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Practice working with Matplotlib exercises to improve your understanding of its capabilities and reinforce your knowledge.

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Find a set of Matplotlib exercises online or in a textbook.
Work through the exercises, experimenting with different options and parameters.
Refer to the Matplotlib documentation or online resources for guidance as needed.

Join a Matplotlib study group

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Join a study group to discuss Matplotlib concepts, share knowledge, and collaborate on projects.

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Find or create a Matplotlib study group online or in your local area.
Attend regular study sessions and participate in discussions.
Share your knowledge and experience with others in the group.

Attend a Matplotlib workshop

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Attend a Matplotlib workshop to learn from experts, connect with others, and enhance your skills.

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Research and identify reputable Matplotlib workshops.
Register for a workshop that aligns with your learning goals.
Attend the workshop, take notes, and actively participate in discussions.
Follow up after the workshop by practicing what you learned and connecting with other attendees.

Create a visualization dashboard

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Build a data visualization dashboard using Matplotlib to solidify your understanding of the library.

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Gather data for your dashboard
Design the layout of your dashboard
Implement the visualizations using Matplotlib
Integrate the visualizations into your dashboard
Test and refine your dashboard

Participate in data visualization challenges

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Participate in data visualization challenges to test your skills, learn from others, and push your limits.

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Identify data visualization challenges that align with your interests and skill level.
Gather the necessary data and tools.
Develop and submit your visualizations.
Analyze the feedback and learn from your experience.

Write a blog post about Matplotlib tricks

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Share your knowledge and reinforce your understanding by writing a blog post about some lesser-known Matplotlib tricks.

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Identify useful but lesser-known Matplotlib techniques
Write a clear and concise blog post explaining the techniques
Illustrate the techniques with code examples
Proofread and edit your blog post
Publish your blog post on a relevant platform

Create a Matplotlib workbook

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Create a workbook that compiles your Matplotlib knowledge, including code snippets, examples, and explanations.

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Organize your Matplotlib knowledge into logical sections.
Write clear and concise explanations for each section.
Include code snippets and examples to illustrate the concepts.
Review and edit your workbook to ensure clarity and accuracy.

Contribute to Matplotlib

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Contribute to the Matplotlib open-source project to deepen your understanding, gain practical experience, and support the community.

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Explore the Matplotlib GitHub repository and identify areas where you can contribute.
Fork the repository and create a branch for your changes.
Implement your changes and write clear documentation.
Submit a pull request and work with the maintainers to get your changes merged.

Career center

Learners who complete Building Data Visualizations Using Matplotlib will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist is a professional who uses their skills in data analysis, machine learning, and statistics to solve business problems. Data Scientists are in high demand, and this course can help you build the skills you need to launch a successful career in this field. By taking this course, you'll learn how to use Matplotlib to create visualizations that can help you identify trends, patterns, and outliers in your data.

See salaries and explore the career path for Data Scientist

Data Analyst

A Data Analyst plays a vital role in any organization that collects data. They use their skills in data analysis, data mining, and data visualization to help businesses make informed decisions. This course, Building Data Visualizations using Matplotlib, can help you build a strong foundation in data visualization, which is a key skill for any Data Analyst. By taking this course, you'll learn how to create clear and concise visualizations that can help you communicate your findings to stakeholders.

See salaries and explore the career path for Data Analyst

Business Analyst

A Business Analyst is responsible for understanding the business needs of an organization and translating those needs into technical requirements. Business Analysts often work with Data Analysts and Data Scientists to help them understand the business context of their work. This course can help you build the skills you need to be a successful Business Analyst by teaching you how to use Matplotlib to create visualizations that can communicate complex business concepts in a clear and concise way.

See salaries and explore the career path for Business Analyst

Market Researcher

A Market Researcher is responsible for collecting, analyzing, and interpreting data about markets and customers. They use this information to help businesses make informed decisions about product development, marketing, and sales. This course can help you build the skills you need to be a successful Market Researcher by teaching you how to use Matplotlib to create visualizations that can help you communicate your findings to stakeholders.

See salaries and explore the career path for Market Researcher

Financial Analyst

A Financial Analyst uses their skills in data analysis and financial modeling to help businesses make informed investment decisions. This course can help you build the skills you need to be a successful Financial Analyst by teaching you how to use Matplotlib to create visualizations that can help you analyze financial data and identify trends.

See salaries and explore the career path for Financial Analyst

Quantitative Analyst

A Quantitative Analyst is a financial professional who uses their skills in mathematics, statistics, and computer programming to develop and implement trading strategies. This course can help you build the skills you need to be a successful Quantitative Analyst by teaching you how to use Matplotlib to create visualizations that can help you analyze financial data and identify trends.

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Biostatistician

A Biostatistician is a statistician who specializes in the application of statistical methods to biological and medical data. This course can help you build the skills you need to be a successful Biostatistician by teaching you how to use Matplotlib to create visualizations that can help you analyze medical data and identify trends.

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Epidemiologist

An Epidemiologist is a public health professional who studies the distribution and determinants of disease in populations. This course can help you build the skills you need to be a successful Epidemiologist by teaching you how to use Matplotlib to create visualizations that can help you analyze public health data and identify trends.

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Software Engineer

A Software Engineer is responsible for designing, developing, and maintaining software applications. This course may be useful for Software Engineers who want to learn how to use Matplotlib to create visualizations that can help them debug their code and identify performance bottlenecks.

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Data Engineer

A Data Engineer is responsible for designing, building, and maintaining the infrastructure that supports data analysis and data science. This course can help you build the skills you need to be a successful Data Engineer by teaching you how to use Matplotlib to create visualizations that can help you monitor the performance of your data pipelines.

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Sales Manager

A Sales Manager is responsible for managing a sales team and achieving sales goals. This course may be useful for Sales Managers who want to learn how to use Matplotlib to create visualizations that can help them track the performance of their team and identify areas for improvement.

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Product Manager

A Product Manager is responsible for managing the development and launch of new products. This course may be useful for Product Managers who want to learn how to use Matplotlib to create visualizations that can help them track the progress of their projects and communicate their findings to stakeholders.

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Marketing Manager

A Marketing Manager is responsible for developing and executing marketing campaigns. This course may be useful for Marketing Managers who want to learn how to use Matplotlib to create visualizations that can help them track the performance of their campaigns and identify areas for improvement.

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Project Manager

A Project Manager is responsible for planning, executing, and closing projects. This course may be useful for Project Managers who want to learn how to use Matplotlib to create visualizations that can help them track the progress of their projects and identify areas for improvement.

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Operations Manager

An Operations Manager is responsible for managing the day-to-day operations of a business. This course may be useful for Operations Managers who want to learn how to use Matplotlib to create visualizations that can help them track the performance of their operations and identify areas for improvement.

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