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Mihaela Danci
Understanding and presenting complex information is easier with data visualization. In this course, you’ll learn how to use Matplotlib to create and customize line, bar, and scatter plots.
Read more
Understanding and presenting complex information is easier with data visualization. In this course, you’ll learn how to use Matplotlib to create and customize line, bar, and scatter plots.
Read more
Understanding and presenting complex information is easier with data visualization. In this course, you’ll learn how to use Matplotlib to create and customize line, bar, and scatter plots.
Data visualization represents a great way to explore and understand data. It also helps in effectivity communicating insights to a variety of audiences and facilitating the decision-making process. In this course, Getting Started with Matplotlib, you’ll learn to create basic chart types. First, you’ll explore Matplotlib’s architecture. Next, you’ll discover what the components of a figure are. Finally, you’ll learn how to build and customize line, bar, and scatter plots. When you’re finished with this course, you’ll have the foundational knowledge needed to craft data visualizations using Matplotlib.
Register for this course and see more details by visiting:
OpenCourser.com/course/ixfrl1/getting
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What's inside
Syllabus
Course Overview
Understanding Matplotlib’s Architecture
Building Line Charts
Working with Scatter Plots
Read more
Syllabus
Course Overview
Understanding Matplotlib’s Architecture
Building Line Charts
Working with Scatter Plots
Read more
Creating Bar Charts
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Course teaches Matplotlib, a popular and industry standard data visualization library used by data analysts and scientists
Taught by Mihaela Danci, an experienced programmer and data visualization expert
Develops foundational knowledge in data visualization using Matplotlib, which is a core skill for data analysts and scientists
Explores Matplotlib's architecture, components of a figure, and building and customizing line, bar, and scatter plots
Suitable for beginners with no prior experience in data visualization
Save this course
Save Getting Started with Matplotlib to your list so you can find it easily later:
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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 Getting Started with Matplotlib with these
activities:
Review basic probability and statistics
Show steps
This course uses concepts from statistics and probability throughout. Reviewing these concepts will make the learning process more efficient.
Browse courses on
Probability
Show steps
-
Review notes from previous courses or textbooks on probability and statistics
-
Take practice problems on probability and statistics
Review Scatter Plots
Show steps
Review the fundamentals of representing data points on a 2D plane with the ability to define and label the axes and specify the parameters of the points.
Browse courses on
Scatter Plots
Show steps
-
revisit the concept and definition of scatter plots
-
review and practice plotting data points on a plane
-
explore real-world examples and applications of scatter plots
Read 'Data Visualization with Python and Matplotlib'
Show steps
Enhance your Matplotlib knowledge by exploring this comprehensive book that provides detailed explanations, code examples, and best practices for data visualization in Python.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
-
Read and understand the core concepts presented in the book
-
Work through the code examples and exercises
-
Experiment with different Matplotlib features and techniques
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Explore Matplotlib tutorials
Show steps
This course uses the Matplotlib library for data visualization. Following tutorials will help you become more familiar with the library.
Show steps
-
Find tutorials on Matplotlib's website or other online resources
-
Follow the tutorials and complete the exercises
Plot Custom Line, Bar, and Scatter Data
Show steps
Create multiple visualizations to solidify the understanding of Matplotlib's functions for creating line, bar, and scatter plots.
Show steps
-
follow and execute the instructions in the Matplotlib documentation
-
create at least one line, one bar, and one scatter plot
-
modify and customize the plots in terms of axis labels, titles, and colors
Create different types of plots using Matplotlib
Show steps
Creating different types of plots will help you understand the capabilities of Matplotlib and improve your data visualization skills.
Show steps
-
Choose a dataset and explore it
-
Create a line plot, bar plot, and scatter plot using Matplotlib
-
Customize the plots by changing colors, labels, and other properties
Discuss and Present Data Visualizations
Show steps
Join a peer group to exchange ideas, gain diverse perspectives, and receive feedback on your visualizations.
Show steps
-
find a study group or learning community focused on data visualization
-
actively participate in discussions and share your own visualizations
-
provide constructive feedback and suggestions to your peers
Write a blog post or article about data visualization using Matplotlib
Show steps
Writing a blog post or article will help you solidify your understanding of Matplotlib and data visualization concepts.
Show steps
-
Choose a topic related to Matplotlib or data visualization
-
Research the topic and gather information
-
Write the blog post or article
-
Publish your blog post or article online
Explore Matplotlib's Customization Options
Show steps
Expand your understanding and proficiency by working through comprehensive tutorials that provide step-by-step guidance on customizing Matplotlib plots.
Show steps
-
identify reputable tutorials or courses on Matplotlib customization
-
carefully follow the instructions and examples provided in the tutorial
-
experiment with different customization options and settings
Design a Visualization Dashboard
Show steps
Extend your skills by creating a compelling dashboard that combines various plots to present data insights in a visually engaging and informative manner.
Show steps
-
determine the purpose and objective of the dashboard
-
gather relevant data and prepare it for visualization
-
select appropriate plot types and customize their appearance
-
arrange the plots in a logical and visually appealing layout
-
add interactivity or user controls to enhance the user experience
Create a data visualization dashboard using Matplotlib
Show steps
Create a comprehensive data visualization dashboard using Matplotlib to showcase your proficiency in data visualization and storytelling.
Browse courses on
Interactive Data Visualization
Show steps
-
Gather and prepare your data
-
Design the layout and structure of your dashboard
-
Create interactive visualizations using Matplotlib
-
Add interactivity and navigation features to your dashboard
-
Deploy your dashboard and share it with others
Contribute to Matplotlib
Show steps
Make significant contributions to the Matplotlib community by reporting bugs, suggesting improvements, or even contributing code.
Show steps
-
familiarize yourself with the Matplotlib codebase and development process
-
identify areas where you can contribute, such as bug fixes or feature enhancements
-
submit a pull request with your proposed changes
-
engage with the Matplotlib community and respond to feedback
Review basic probability and statistics
Show steps
This course uses concepts from statistics and probability throughout. Reviewing these concepts will make the learning process more efficient.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review notes from previous courses or textbooks on probability and statistics
- Take practice problems on probability and statistics
Review Scatter Plots
Show steps
Review the fundamentals of representing data points on a 2D plane with the ability to define and label the axes and specify the parameters of the points.
Browse courses on
Scatter Plots
Show steps
Show steps
Show steps
- revisit the concept and definition of scatter plots
- review and practice plotting data points on a plane
- explore real-world examples and applications of scatter plots
Read 'Data Visualization with Python and Matplotlib'
Show steps
Enhance your Matplotlib knowledge by exploring this comprehensive book that provides detailed explanations, code examples, and best practices for data visualization in Python.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
Show steps
Show steps
- Read and understand the core concepts presented in the book
- Work through the code examples and exercises
- Experiment with different Matplotlib features and techniques
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Explore Matplotlib tutorials
Show steps
This course uses the Matplotlib library for data visualization. Following tutorials will help you become more familiar with the library.
Show steps
Show steps
Show steps
- Find tutorials on Matplotlib's website or other online resources
- Follow the tutorials and complete the exercises
Plot Custom Line, Bar, and Scatter Data
Show steps
Create multiple visualizations to solidify the understanding of Matplotlib's functions for creating line, bar, and scatter plots.
Show steps
Show steps
Show steps
- follow and execute the instructions in the Matplotlib documentation
- create at least one line, one bar, and one scatter plot
- modify and customize the plots in terms of axis labels, titles, and colors
Create different types of plots using Matplotlib
Show steps
Creating different types of plots will help you understand the capabilities of Matplotlib and improve your data visualization skills.
Show steps
Show steps
Show steps
- Choose a dataset and explore it
- Create a line plot, bar plot, and scatter plot using Matplotlib
- Customize the plots by changing colors, labels, and other properties
Discuss and Present Data Visualizations
Show steps
Join a peer group to exchange ideas, gain diverse perspectives, and receive feedback on your visualizations.
Show steps
Show steps
Show steps
- find a study group or learning community focused on data visualization
- actively participate in discussions and share your own visualizations
- provide constructive feedback and suggestions to your peers
Write a blog post or article about data visualization using Matplotlib
Show steps
Writing a blog post or article will help you solidify your understanding of Matplotlib and data visualization concepts.
Show steps
Show steps
Show steps
- Choose a topic related to Matplotlib or data visualization
- Research the topic and gather information
- Write the blog post or article
- Publish your blog post or article online
Explore Matplotlib's Customization Options
Show steps
Expand your understanding and proficiency by working through comprehensive tutorials that provide step-by-step guidance on customizing Matplotlib plots.
Show steps
Show steps
Show steps
- identify reputable tutorials or courses on Matplotlib customization
- carefully follow the instructions and examples provided in the tutorial
- experiment with different customization options and settings
Design a Visualization Dashboard
Show steps
Extend your skills by creating a compelling dashboard that combines various plots to present data insights in a visually engaging and informative manner.
Show steps
Show steps
Show steps
- determine the purpose and objective of the dashboard
- gather relevant data and prepare it for visualization
- select appropriate plot types and customize their appearance
- arrange the plots in a logical and visually appealing layout
- add interactivity or user controls to enhance the user experience
Create a data visualization dashboard using Matplotlib
Show steps
Create a comprehensive data visualization dashboard using Matplotlib to showcase your proficiency in data visualization and storytelling.
Browse courses on
Interactive Data Visualization
Show steps
Show steps
Show steps
- Gather and prepare your data
- Design the layout and structure of your dashboard
- Create interactive visualizations using Matplotlib
- Add interactivity and navigation features to your dashboard
- Deploy your dashboard and share it with others
Contribute to Matplotlib
Show steps
Make significant contributions to the Matplotlib community by reporting bugs, suggesting improvements, or even contributing code.
Show steps
Show steps
Show steps
- familiarize yourself with the Matplotlib codebase and development process
- identify areas where you can contribute, such as bug fixes or feature enhancements
- submit a pull request with your proposed changes
- engage with the Matplotlib community and respond to feedback
Career center
Learners who complete Getting Started with Matplotlib will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses their expertise to analyze, interpret, and present data. They work with other professionals in the business to design and implement data collection, organization, and analysis. This course may help a Data Scientist build a foundation in data visualization, a key skill for communicating insights to others.
Data Analyst
Data Analyst
A Data Analyst helps organization make better decisions through data visualization. A good Data Analyst uses tools to organize and present data from a variety of sources in a way that communicates detailed information. This course may help a Data Analyst gain a foundation for visualizing data in powerful, compelling ways.
Business Analyst
Business Analyst
A Business Analyst is responsible for researching, understanding, and analyzing an organization's business processes. They may then create and implement solutions to business problems. This course may help a Business Analyst learn critical visualization techniques that help improve communication and decision-making.
Market Researcher
Market Researcher
A Market Researcher gathers, analyzes, and interprets data about consumer behavior and market trends. This course may be helpful for a Market Researcher by providing tools that can be used to visualize data for use in marketing and sales.
Product Manager
Product Manager
A Product Manager is responsible for developing and executing a product vision, including gathering requirements, defining features, and overseeing development. This course may be helpful for a Product Manager who wants to learn about creating clear and actionable visualizations to communicate their ideas and plans to stakeholders.
Software Engineer
Software Engineer
A Software Engineer applies computer science principles to the design, development, deployment, and maintenance of software systems. This course may be helpful for a Software Engineer who participates in the creation of data-driven software applications.
Financial Analyst
Financial Analyst
A Financial Analyst uses financial data to help organizations make investment decisions and maximize profitability. This course may be helpful for a Financial Analyst by providing the skills to create visualizations that enable them to make data-driven recommendations for improved financial outcomes.
Data Journalist
Data Journalist
A Data Journalist uses data to tell stories and inform the public. This course may be helpful for a Data Journalist who wants to learn about creating clear and concise visualizations to communicate data-driven insights to a wide audience.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. This course may be helpful for a Statistician who wants to learn about using data visualization to present statistical findings in a clear and concise way.
Data Engineer
Data Engineer
A Data Engineer designs and builds data systems for storing, managing, and processing large amounts of data. This course may be helpful for a Data Engineer who wants to learn about using data visualization to understand the performance of data systems and identify areas for improvement.
UX Designer
UX Designer
A UX Designer is responsible for designing user interfaces that are both functional and aesthetically pleasing. This course may be helpful for a UX Designer who wants to learn about using data visualization to understand user behavior and improve the user experience.
Management Consultant
Management Consultant
A Management Consultant provides expert advice to organizations on how to improve their performance. This course may be helpful for a Management Consultant by providing the ability to visualize and present complex data in a way that makes it easier to make effective recommendations.
Information Architect
Information Architect
An Information Architect designs and organizes the structure and content of websites, intranets, and other digital products. This course may be helpful for an Information Architect who wants to learn about using data visualization to create intuitive and user-friendly information architectures.
Quantitative Researcher
Quantitative Researcher
A Quantitative Researcher uses statistical methods to analyze financial data and make investment recommendations. This course may be helpful for a Quantitative Researcher by providing a foundation in data visualization, which can be used to identify trends and patterns in financial data.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes and spread of diseases. This course may be helpful for an Epidemiologist who wants to learn about using data visualization to track and map the spread of diseases and identify risk factors.
For more career information including salaries, visit:
OpenCourser.com/course/ixfrl1/getting
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Getting Started with Matplotlib.
Save
Is considered a classic in the field of data visualization and is highly recommended for this course's learners who seek advanced knowledge in this area.
Helps learners understand principles related to what type of chart to use for different data and can supplement this course.
This course provides foundational concepts in information visualization that can supplement the information provided in this course, particularly in understanding the process of designing effective data visualizations.
Provides a practical introduction to using D3.js for data visualization, offering an alternative perspective to Matplotlib.
Provides learners with an introduction to ggplot2, a popular data visualization library in R, which can provide a different perspective to Matplotlib.
Provides foundational knowledge in Python data analysis and can serve as a preparatory resource for this course.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ixfrl1/getting
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Time
69 hours
Level
Introductory
Via
Pluralsight
Instructor
Mihaela Danci
Language
English
Good to know
Course teaches Matplotlib, a popular and industry standard data visualization library used by data analysts and scientists
Taught by Mihaela Danci, an experienced programmer and data visualization expert
Develops foundational knowledge in data visualization using Matplotlib, which is a core skill for data analysts and scientists
Explores Matplotlib's architecture, components of a figure, and building and customizing line, bar, and scatter plots
Suitable for beginners with no prior experience in data visualization
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