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Patrick Ester
Code and run your first Python script with Matplotlib in minutes without installing anything!
Read more
Code and run your first Python script with Matplotlib in minutes without installing anything!
Read more
Code and run your first Python script with Matplotlib in minutes without installing anything!
This course is designed for learners with some Python experience, and provides a crash course in Matplotlib. This enables the learners to delve into core data visualization topics that can be transferred to other languages. In this course, you will learn how to use Jupyter, generate and choose the best graphs to represent your data.
To allow for a truly hands-on, self-paced learning experience, this course is video-free.
Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours.
Register for this course and see more details by visiting:
OpenCourser.com/course/4dwro1/select
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What's inside
Syllabus
Intro to Matplotlib
Welcome to Week 1 of the Select Topics in Python: Matplotlib course. These assignments cover the basics of Jupyter notebooks and Matplotlib, customizing a visualization, as well as box plots and histograms. The module ends with graded coding exercises.
Read more
Syllabus
Intro to Matplotlib
Welcome to Week 1 of the Select Topics in Python: Matplotlib course. These assignments cover the basics of Jupyter notebooks and Matplotlib, customizing a visualization, as well as box plots and histograms. The module ends with graded coding exercises.
Read more
Intermediate Matplotlib
Welcome to Week 2 of the Select Topics in Python: Matplotlib course. These assignments extend the previous module by introducing line charts, area charts, scatter plots, bubble charts, pie charts, and donut charts. The module ends with graded coding exercises.
Advanced Matplotlib
Welcome to Week 3 of the Select Topics in Python: Matplotlib course. These assignments cover specialized visualizations, configuring layout and presentation, and adding animations. The module ends with graded coding exercises.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Focuses on data visualization, which is a standard tool in many industries
Teaches Matplotlib, which is highly relevant for data visualization
Provides hands-on exercises for experiential learning
Builds a foundation for Python users to expand their data visualization skills
Assumes some Python experience and may not be suitable for complete beginners
Emphasizes self-paced learning, which may not be ideal for learners who prefer structured guidance
Save this course
Save Select Topics in Python: 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 Select Topics in Python: Matplotlib with these
activities:
Create a Matplotlib cheat sheet
Show steps
Have a quick reference guide for matplotlib functions and syntax.
Browse courses on
Matplotlib
Show steps
-
Gather information about Matplotlib functions and syntax from the documentation and other resources.
-
Organize the information into a logical structure.
-
Create a cheat sheet using a tool like Google Sheets, Microsoft Word, or Markdown.
-
Share your cheat sheet with other learners.
-
Update your cheat sheet as you learn more about Matplotlib.
Read Fluent Python
Show steps
Gain a deeper understanding of Python's idioms and best practices.
View
Fluent Python
on Amazon
Show steps
-
Purchase or borrow a copy of Fluent Python.
-
Read the book at your own pace, taking notes as you go.
-
Apply what you learn to your own Python projects.
-
Share your insights with other learners.
-
Complete the exercises at the end of each chapter to test your understanding.
Form a study group with other learners
Show steps
Improve your learning through collaboration and discussion.
Browse courses on
Matplotlib
Show steps
-
Find other learners who are interested in learning Matplotlib.
-
Create a study schedule and agree on meeting times.
-
Meet regularly to discuss the material, work on projects, and quiz each other.
-
Help each other out when you get stuck.
-
Celebrate your successes together.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete coding exercises
Show steps
Improve your coding skills by practicing regularly.
Browse courses on
Coding
Show steps
-
Find a coding exercise or project that interests you.
-
Read through the instructions carefully.
-
Start coding!
-
Debug your code until it runs without errors.
-
Test your code to make sure it produces the correct results.
Create visualizations of data sets
Show steps
Improve your understanding of visualizations by creating them rather than just viewing them.
Browse courses on
Data Visualization
Show steps
-
Select a data set that interests you.
-
Decide what type of visualizations would be most appropriate for the data set.
-
Create the visualizations using Matplotlib.
-
Analyze the visualizations and draw conclusions from the data.
-
Share your visualizations with others and get feedback.
Attend a Python workshop
Show steps
Learn from experts and get hands-on experience with matplotlib.
Browse courses on
Python
Show steps
-
Find a Python workshop that fits your schedule and interests.
-
Register for the workshop.
-
Attend the workshop and participate actively.
-
Ask questions and take notes.
-
Apply what you learned in your own projects.
Write a blog post about your experience learning Matplotlib
Show steps
Deepen your understanding of Matplotlib by teaching it to others.
Browse courses on
Matplotlib
Show steps
-
Choose a topic that you are passionate about and that you have experience with.
-
Do some research to gather information and organize your thoughts.
-
Write a rough draft of your blog post.
-
Revise and edit your blog post to make it clear and concise.
-
Publish your blog post and share it with others.
Contribute to an open source Matplotlib project
Show steps
Get involved in the Matplotlib community and make a difference.
Browse courses on
Matplotlib
Show steps
-
Find an open source Matplotlib project that interests you.
-
Read the project's documentation and contribute in a way that aligns with your skills and interests.
-
Submit a pull request with your changes.
-
Collaborate with other contributors to improve the project.
-
Celebrate your contributions and the impact you have made.
Create a Matplotlib cheat sheet
Show steps
Have a quick reference guide for matplotlib functions and syntax.
Browse courses on
Matplotlib
Show steps
Show steps
Show steps
- Gather information about Matplotlib functions and syntax from the documentation and other resources.
- Organize the information into a logical structure.
- Create a cheat sheet using a tool like Google Sheets, Microsoft Word, or Markdown.
- Share your cheat sheet with other learners.
- Update your cheat sheet as you learn more about Matplotlib.
Read Fluent Python
Show steps
Gain a deeper understanding of Python's idioms and best practices.
View
Fluent Python
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow a copy of Fluent Python.
- Read the book at your own pace, taking notes as you go.
- Apply what you learn to your own Python projects.
- Share your insights with other learners.
- Complete the exercises at the end of each chapter to test your understanding.
Form a study group with other learners
Show steps
Improve your learning through collaboration and discussion.
Browse courses on
Matplotlib
Show steps
Show steps
Show steps
- Find other learners who are interested in learning Matplotlib.
- Create a study schedule and agree on meeting times.
- Meet regularly to discuss the material, work on projects, and quiz each other.
- Help each other out when you get stuck.
- Celebrate your successes together.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete coding exercises
Show steps
Improve your coding skills by practicing regularly.
Browse courses on
Coding
Show steps
Show steps
Show steps
- Find a coding exercise or project that interests you.
- Read through the instructions carefully.
- Start coding!
- Debug your code until it runs without errors.
- Test your code to make sure it produces the correct results.
Create visualizations of data sets
Show steps
Improve your understanding of visualizations by creating them rather than just viewing them.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Select a data set that interests you.
- Decide what type of visualizations would be most appropriate for the data set.
- Create the visualizations using Matplotlib.
- Analyze the visualizations and draw conclusions from the data.
- Share your visualizations with others and get feedback.
Attend a Python workshop
Show steps
Learn from experts and get hands-on experience with matplotlib.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find a Python workshop that fits your schedule and interests.
- Register for the workshop.
- Attend the workshop and participate actively.
- Ask questions and take notes.
- Apply what you learned in your own projects.
Write a blog post about your experience learning Matplotlib
Show steps
Deepen your understanding of Matplotlib by teaching it to others.
Browse courses on
Matplotlib
Show steps
Show steps
Show steps
- Choose a topic that you are passionate about and that you have experience with.
- Do some research to gather information and organize your thoughts.
- Write a rough draft of your blog post.
- Revise and edit your blog post to make it clear and concise.
- Publish your blog post and share it with others.
Contribute to an open source Matplotlib project
Show steps
Get involved in the Matplotlib community and make a difference.
Browse courses on
Matplotlib
Show steps
Show steps
Show steps
- Find an open source Matplotlib project that interests you.
- Read the project's documentation and contribute in a way that aligns with your skills and interests.
- Submit a pull request with your changes.
- Collaborate with other contributors to improve the project.
- Celebrate your contributions and the impact you have made.
Career center
Learners who complete Select Topics in Python: Matplotlib will develop knowledge and skills
that may be useful to these careers:
Data Visualization Engineer
Data Visualization Engineer
Data Visualization Engineers specialize in transforming complex data into visually appealing and informative graphics. Matplotlib is a powerful tool for creating interactive and customizable visualizations. This course provides a comprehensive introduction to Matplotlib, empowering individuals to excel in this growing field and contribute to data-driven decision-making.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to draw conclusions. Matplotlib is a powerful tool for visualizing statistical data and identifying patterns. This course provides a comprehensive introduction to data visualization for statistics, empowering individuals to communicate statistical insights more effectively and make informed decisions based on data.
Data Scientist
Data Scientist
Data Scientists are responsible for designing and developing data-driven solutions to complex problems. The ability to visualize data is crucial for understanding patterns and trends, and Matplotlib is a widely used tool for this purpose. This course empowers individuals to create compelling visualizations that effectively convey data insights. By mastering Matplotlib, learners can enhance their skills as Data Scientists and contribute to data-driven decision-making.
Data Analyst
Data Analyst
Data Analysts transform raw data into valuable insights that drive decision-making. By visualizing data in a meaningful way, they can help organizations identify trends, patterns, and correlations. This course provides a solid foundation in data visualization with Matplotlib, a powerful Python library. With a deeper understanding of data visualization techniques, individuals can advance their careers as Data Analysts, enabling them to effectively communicate insights and support data-driven决策.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts provide insights into business performance by analyzing data and identifying trends. Data visualization is essential for communicating findings to stakeholders in a clear and concise manner. This course provides a solid foundation in Matplotlib, enabling individuals to create compelling visualizations that support informed decision-making.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand market trends and consumer behavior. Matplotlib is widely used to visualize market data and identify insights that drive marketing strategies. This course provides a strong foundation in data visualization for market research, empowering individuals to effectively communicate research findings and support data-driven marketing decisions.
Financial Analyst
Financial Analyst
Financial Analysts evaluate the financial performance of companies and make investment recommendations. Matplotlib is widely used to visualize financial data and identify trends. This course provides a strong foundation in data visualization for financial analysis, enabling individuals to analyze financial data more effectively and make informed investment decisions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data in order to make informed investment decisions. Matplotlib is commonly used in quantitative finance to visualize complex financial data and identify trading opportunities. This course provides a solid foundation in data visualization for quantitative analysis, helping individuals strengthen their skills in this highly specialized field.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. Matplotlib is a powerful tool for visualizing actuarial data and identifying patterns. This course provides a comprehensive introduction to data visualization for actuarial science, empowering individuals to communicate actuarial insights more effectively and make informed decisions based on data.
Science Researcher
Science Researcher
Science Researchers conduct scientific experiments and analyze data to add to the body of scientific knowledge. Matplotlib is widely used to visualize scientific data and communicate research findings. This course provides a solid foundation in data visualization for scientific research, enabling individuals to effectively communicate their findings and contribute to the advancement of scientific knowledge.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and implement machine learning algorithms to solve real-world problems. Data visualization is an essential tool for understanding the performance of machine learning models and identifying potential biases. With this course, learners can gain a deeper understanding of data visualization in the context of machine learning, enabling them to build and deploy more effective models.
Data Journalist
Data Journalist
Data Journalists use data to tell stories and uncover insights from complex datasets. Matplotlib is a powerful tool for creating visually engaging data-driven narratives. This course provides individuals with the necessary skills to effectively communicate data insights through compelling visualizations, enhancing their ability to inform and engage audiences.
User Experience Researcher
User Experience Researcher
User Experience Researchers study the behavior and needs of users to improve the design and usability of products and services. Matplotlib can be used to visualize user data and identify patterns that inform UX decisions. This course provides a foundation in data visualization for UX research, enabling individuals to gain insights into user behavior and contribute to the development of more user-friendly products.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. Data visualization is becoming increasingly important in software development to gain insights into application performance and user behavior. Matplotlib is a popular tool for creating informative visualizations that can be integrated into software applications. This course provides a foundation in data visualization for software engineering, enabling individuals to contribute to the development of more user-friendly and efficient software.
Web Developer
Web Developer
Web Developers design and develop websites and web applications. Matplotlib can be used to embed interactive visualizations into web pages, enhancing user engagement and providing insights into website usage. This course provides a practical introduction to data visualization for web development, empowering individuals to create more informative and engaging web experiences.
For more career information including salaries, visit:
OpenCourser.com/course/4dwro1/select
Reading list
We've selected ten 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
Select Topics in Python: Matplotlib.
Save
Provides a comprehensive guide to data science using Python. It covers topics such as data cleaning, data analysis, and machine learning. It would be a valuable resource for students who want to learn more about data science and how to use Python for data analysis tasks.
Provides a comprehensive guide to data science using Python. It covers topics such as data cleaning, data analysis, and machine learning. It would be a valuable resource for students who want to learn more about data science and how to use Python for data analysis.
Provides a comprehensive guide to deep learning using Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It would be a valuable resource for students who want to learn more about deep learning and how to use Python for deep learning tasks.
Provides a comprehensive guide to data analysis using Python. It covers topics such as data cleaning, data exploration, and data visualization. It would be a valuable resource for students who want to learn more about data analysis and how to use Python for data analysis tasks.
Provides a comprehensive guide to machine learning using Python. It covers topics such as supervised learning, unsupervised learning, and natural language processing. It would be a valuable resource for students who want to learn more about machine learning and how to use Python for machine learning tasks.
Provides a comprehensive guide to machine learning using Python. It covers topics such as supervised learning, unsupervised learning, and natural language processing. It would be a valuable resource for students who want to learn more about machine learning and how to use Python for machine learning tasks.
Save
Provides a comprehensive guide to deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It would be a valuable resource for students who want to learn more about deep learning and how to use it to solve real-world problems.
Provides a comprehensive guide to data science. It covers topics such as data cleaning, data analysis, and machine learning. It would be a valuable resource for students who want to learn more about data science and how to use it to solve real-world problems.
Provides a comprehensive guide to machine learning using Python. It covers topics such as supervised learning, unsupervised learning, and deep learning. It would be a valuable resource for students who want to learn more about machine learning and how to use it to solve real-world problems.
Provides a gentle introduction to machine learning using Python. It covers topics such as supervised learning, unsupervised learning, and natural language processing. It would be a good choice for students who are new to machine learning and want to learn how to use Python for machine learning tasks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4dwro1/select
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Effort
3 weeks of study, 3-4 hours/week
Level
Intermediate
Via
Coursera
Institution
Codio
Instructor
Patrick Ester
Language
English
This course belongs to a
collection
called
Select Topics in Python. It's comprised of four courses:
- Select Topics in Python: Matplotlib (viewing)
- Select Topics in Python: Django
- Select Topics in Python: Packaging
- Select Topics in Python: Natural Language Processing
Good to know
Focuses on data visualization, which is a standard tool in many industries
Teaches Matplotlib, which is highly relevant for data visualization
Provides hands-on exercises for experiential learning
Builds a foundation for Python users to expand their data visualization skills
Assumes some Python experience and may not be suitable for complete beginners
Emphasizes self-paced learning, which may not be ideal for learners who prefer structured guidance
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