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Josh Magee
Enroll in Udacity's Data Visualization course and learn how to programmatically build data visualizations, convey trends and communicate insights with Matplotlib and Seaborn.
Prerequisite details
Read more
Enroll in Udacity's Data Visualization course and learn how to programmatically build data visualizations, convey trends and communicate insights with Matplotlib and Seaborn.
Prerequisite details
Read more
Enroll in Udacity's Data Visualization course and learn how to programmatically build data visualizations, convey trends and communicate insights with Matplotlib and Seaborn.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Data analysis process
- Basic SQL
- Inferential statistics
- Elementary algebra
- Basic Python
- Basic descriptive statistics
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/2zqzpe/data
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What's inside
Syllabus
In this lesson, see the motivations for why data visualization is an important part of the data analysis process and where it fits in.
Learn about elements of visualization design, especially to avoid those elements that can cause a visualization to fail.
Read more
Syllabus
In this lesson, see the motivations for why data visualization is an important part of the data analysis process and where it fits in.
Learn about elements of visualization design, especially to avoid those elements that can cause a visualization to fail.
Read more
In this lesson, you will see how you can use matplotlib and seaborn to produce informative visualizations of single variables.
In this lesson, build up from your understanding of individual variables and learn how to use matplotlib and seaborn to look at relationships between two variables.
In this lesson, see how you can use matplotlib and seaborn to visualize relationships and interactions between three or more variables.
Previous lessons covered how you could use visualizations to learn about your data. In this lesson, see how to polish up those plots to convey your findings to others!
Choose a dataset, either your own or a Udacity-curated dataset, and perform an exploratory data analysis using Python. Then, create a presentation with explanatory plots that conveys your findings.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Relevant for learners from various backgrounds, including data analysts, scientists, and researchers
Provides hands-on experience with popular data visualization libraries, Matplotlib and Seaborn
Teaches essential concepts of data visualization design, helping learners create effective and informative visuals
Suitable for learners with some background in data analysis and statistics
Requires proficiency in basic Python programming
Save this course
Save Data Visualization with Matplotlib and Seaborn to your list so you can find it easily later:
Save
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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 Data Visualization with Matplotlib and Seaborn with these
activities:
Read 'Effective Data Visualization' by Stephanie Evergreen
Show steps
This book provides a comprehensive overview of data visualization principles and best practices.
View
Effective Data Visualization
on Amazon
Show steps
-
Read the book and take notes on key concepts.
-
Apply the principles to your own data visualization projects.
Review prerequisite knowledge
Show steps
Reviewing these topics will ensure you have a strong foundation for the course and be better prepared for success.
Browse courses on
Data Analysis Process
Show steps
-
Review your notes from previous courses or study materials on the prerequisite topics.
-
Do practice questions related to these topics.
Follow tutorials and videos on data visualization best practices
Show steps
Following tutorials will provide you with practical guidance and tips on creating effective and visually appealing data visualizations.
Browse courses on
Data Visualization
Show steps
-
Search for tutorials on data visualization best practices.
-
Watch video tutorials and follow along with the examples.
-
Apply the techniques you learn to your own visualizations.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice creating visualizations using Matplotlib and Seaborn
Show steps
Completing practice drills will help you improve your proficiency and confidence in using these libraries for data visualization.
Browse courses on
Matplotlib
Show steps
-
Find a dataset and explore it.
-
Create a scatter plot, a bar chart, and a histogram using Matplotlib or Seaborn.
-
Experiment with different visualization options to customize the appearance of your plots.
Participate in peer review sessions
Show steps
Peer review sessions allow you to provide and receive feedback on your visualizations, leading to improvement and a deeper understanding of the subject matter.
Browse courses on
Data Visualization
Show steps
-
Find a study group or online community where you can connect with other learners.
-
Share your visualizations and provide constructive criticism to others.
-
Incorporate feedback into your own work.
Create an infographic on data visualization concepts
Show steps
Creating an infographic will force you to synthesize and present key concepts in a concise and visually appealing manner.
Browse courses on
Data Visualization
Show steps
-
Identify the key concepts you want to cover.
-
Design the infographic using a tool like Canva or Visme.
-
Share your infographic with others.
Create a data visualization portfolio
Show steps
Creating a portfolio will showcase your skills and help you stand out in job applications or research projects.
Browse courses on
Data Visualization
Show steps
-
Gather your best data visualizations.
-
Create a website or online platform to display your portfolio.
-
Write a brief description for each visualization, highlighting its purpose and key insights.
Mentor junior learners or participate in Q&A forums
Show steps
Teaching and explaining concepts to others will reinforce your understanding and identify areas where you need further improvement.
Browse courses on
Data Visualization
Show steps
-
Identify platforms or communities where you can connect with junior learners.
-
Provide guidance, answer questions, and share your knowledge.
Read 'Effective Data Visualization' by Stephanie Evergreen
Show steps
This book provides a comprehensive overview of data visualization principles and best practices.
View
Effective Data Visualization
on Amazon
Show steps
Show steps
Show steps
- Read the book and take notes on key concepts.
- Apply the principles to your own data visualization projects.
Review prerequisite knowledge
Show steps
Reviewing these topics will ensure you have a strong foundation for the course and be better prepared for success.
Browse courses on
Data Analysis Process
Show steps
Show steps
Show steps
- Review your notes from previous courses or study materials on the prerequisite topics.
- Do practice questions related to these topics.
Follow tutorials and videos on data visualization best practices
Show steps
Following tutorials will provide you with practical guidance and tips on creating effective and visually appealing data visualizations.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Search for tutorials on data visualization best practices.
- Watch video tutorials and follow along with the examples.
- Apply the techniques you learn to your own visualizations.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice creating visualizations using Matplotlib and Seaborn
Show steps
Completing practice drills will help you improve your proficiency and confidence in using these libraries for data visualization.
Browse courses on
Matplotlib
Show steps
Show steps
Show steps
- Find a dataset and explore it.
- Create a scatter plot, a bar chart, and a histogram using Matplotlib or Seaborn.
- Experiment with different visualization options to customize the appearance of your plots.
Participate in peer review sessions
Show steps
Peer review sessions allow you to provide and receive feedback on your visualizations, leading to improvement and a deeper understanding of the subject matter.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Find a study group or online community where you can connect with other learners.
- Share your visualizations and provide constructive criticism to others.
- Incorporate feedback into your own work.
Create an infographic on data visualization concepts
Show steps
Creating an infographic will force you to synthesize and present key concepts in a concise and visually appealing manner.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify the key concepts you want to cover.
- Design the infographic using a tool like Canva or Visme.
- Share your infographic with others.
Create a data visualization portfolio
Show steps
Creating a portfolio will showcase your skills and help you stand out in job applications or research projects.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather your best data visualizations.
- Create a website or online platform to display your portfolio.
- Write a brief description for each visualization, highlighting its purpose and key insights.
Mentor junior learners or participate in Q&A forums
Show steps
Teaching and explaining concepts to others will reinforce your understanding and identify areas where you need further improvement.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify platforms or communities where you can connect with junior learners.
- Provide guidance, answer questions, and share your knowledge.
Career center
Learners who complete Data Visualization with Matplotlib and Seaborn will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/2zqzpe/data
Reading list
We've selected seven 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
Data Visualization with Matplotlib and Seaborn.
Save
This classic work by Edward Tufte must-read for anyone interested in data visualization. It provides a deep understanding of the principles of visual communication and how to design effective visualizations.
Provides a comprehensive overview of the field of visualization analysis and design. It covers a wide range of topics, from the history of visualization to the latest research in the field.
Provides a practical guide to data visualization using Python and JavaScript. It covers a wide range of topics, from basic data visualization techniques to more advanced topics such as interactive visualizations and data storytelling.
Provides practical guidance on how to choose the right chart type for your data and how to design effective visualizations. It is particularly useful for readers who want to improve their skills in data visualization.
Save
Provides a comprehensive overview of data visualization techniques and best practices, covering both the theoretical foundations and practical applications. It is particularly useful for readers who are new to data visualization or who want to improve their skills in this area.
Provides a practical guide to creating interactive data visualizations for the web using JavaScript and D3.js. It covers a wide range of topics, from basic data visualization techniques to more advanced topics such as interactive visualizations and data storytelling.
Provides a beginner-friendly introduction to Tableau Desktop, a powerful data visualization software. It covers a wide range of topics, from basic data visualization techniques to more advanced topics such as interactive visualizations and data storytelling.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2zqzpe/data
Share
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Effort
4 weeks
Via
Udacity
Instructor
Josh Magee
Language
English
Good to know
Relevant for learners from various backgrounds, including data analysts, scientists, and researchers
Provides hands-on experience with popular data visualization libraries, Matplotlib and Seaborn
Teaches essential concepts of data visualization design, helping learners create effective and informative visuals
Suitable for learners with some background in data analysis and statistics
Requires proficiency in basic Python programming
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