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Mo Rebaie
In this two-hour long project-based course, you will learn Statistical Data Visualization techniques in Python. By the end of this project, you will have applied basic statistics and created statistical plots and charts using Seaborn, Plotly, and...
Read more
In this two-hour long project-based course, you will learn Statistical Data Visualization techniques in Python. By the end of this project, you will have applied basic statistics and created statistical plots and charts using Seaborn, Plotly, and...
Read more
In this two-hour long project-based course, you will learn Statistical Data Visualization techniques in Python. By the end of this project, you will have applied basic statistics and created statistical plots and charts using Seaborn, Plotly, and Matplotlib.
This class is for learners who want to use Python for Statistical Data Visualization and Plots, and for learners who are currently taking a basic machine learning course or have already finished a machine learning course and are searching for a practical statistical data visualization project course. Also, this project provides learners with basic knowledge about statistical visualization techniques and libraries in Python and improves their skills in creating various types of charts and plots which helps them in fulfilling their career goals by adding this project to their portfolios.
Register for this course and see more details by visiting:
OpenCourser.com/course/l1skzd/statistical
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possible dealbreakers
Develops basic understanding of statistical visualization techniques and libraries in Python
Suitable for learners with basic machine learning knowledge
Provides practical experience with statistical data visualization
Useful for adding statistical visualization skills to portfolios
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Reviews summary
Challenging but gratifying data visualization course
In general, learners enjoyed this course but found that the instructor's speech made it difficult for them to follow along. Learners who took the time to work through the assignments and exercises found the content valuable and rewarding. However, learners hoping for more passive experience may want to look elsewhere.
Practical Python Skills
"By the end of this project, you will have applied basic statistics and created statistical plots and charts using Seaborn, Plotly, and Matplotlib"
Rewarding Course Projects
"The course was awesome"
Lack of Instruction
"no explanation at all, poor audio quality , even the transcription was wrong! just some code typed."
"Examples of visualizations using Seaborn Plotly Matplotlib, but no explanations are given of what the parameters mean or intuition about their architecture."
Audio Quality Issues
"Very poor audio quality and the explanation could have be lot better."
"This course presented lots of examples. My problem is that the voice of the instructor is not clear and his background is noisy."
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 Statistical Data Visualization in Python with these
activities:
Review Seaborn and Plotly
Show steps
Review your existing knowledge of Seaborn and Plotly for statistical visualizations to refresh your memory and strengthen your foundation.
Browse courses on
Seaborn
Show steps
-
Revisit the Seaborn documentation
-
Go through Plotly tutorials
-
Practice creating basic plots using Seaborn and Plotly
Review python programming concepts
Show steps
Brush up on Python programming concepts to ensure a solid foundation for the course.
Browse courses on
Python Programming
Show steps
-
Review Python data types, variables, and operators
-
Practice writing simple Python programs
-
Review Python libraries for data manipulation
Review Basic Statistics Fundamentals
Show steps
Refresh basic statistical concepts to strengthen understanding of advanced topics in the course.
Browse courses on
Statistics Fundamentals
Show steps
-
Revisit Probability Theorems
-
Review Sampling Distributions
-
Practice Applying Hypothesis Testing
18 other activities
Expand to see all activities and additional details
Show all 21 activities
Read 'Data Visualization: A Practical Introduction'
Show steps
Gain fundamental concepts and practical insights into statistical visualization.
View
Data Visualization: A Successful Design Process...
on Amazon
Show steps
-
Read Chapters on Visualization Principles
-
Review Case Studies and Examples
Practice creating different types of statistical plots
Show steps
Practice creating different types of statistical plots, such as histograms, scatterplots, and boxplots, to improve your understanding of how to represent data visually.
Show steps
-
Find a dataset that you are interested in
-
Choose a type of statistical plot that is appropriate for the data
-
Create the plot using Python and the libraries covered in the course
Explore Seaborn and Plotly tutorials
Show steps
Familiarize yourself with Seaborn and Plotly through guided tutorials to enhance your understanding of data visualization techniques.
Browse courses on
Seaborn
Show steps
-
Follow online tutorials on Seaborn and Plotly
-
Experiment with different examples provided in the tutorials
Solve Guided Statistical Visualization Problems
Show steps
Reinforce understanding by solving guided problems covering various statistical visualization techniques.
Browse courses on
Data Exploration
Show steps
-
Explore Different Types of Statistical Charts
-
Practice Data Analysis Using Visualization
-
Interpret Findings and Draw Conclusions
Guided Tutorials to Create Visualizations
Show steps
Follow guided tutorials and practice creating various types of statistical visualizations to enhance your proficiency.
Show steps
-
Find online tutorials on statistical visualization with Seaborn and Plotly
-
Follow the tutorials step-by-step and create visualizations
-
Experiment with different parameters and options to customize your visualizations
Attend a Statistical Visualization Study Group
Show steps
Engage with peers and learn from shared discussions and perspectives.
Show steps
-
Find or Join a Study Group
-
Participate in Discussions and Share Insights
-
Review and Synthesize Different Approaches
Peer Review and Discussion
Show steps
Engage with peers to exchange knowledge, provide feedback, and learn from diverse perspectives on statistical visualization techniques.
Show steps
-
Join or create an online forum or study group
-
Share your visualizations and insights with others
-
Provide constructive feedback and engage in discussions
Solve Python coding exercises
Show steps
Reinforce your understanding of Python coding and statistical analysis techniques through practice drills.
Browse courses on
Python Coding
Show steps
-
Solve coding exercises on data manipulation using Python
-
Practice implementing statistical analysis algorithms in Python
Follow tutorials on advanced data visualization techniques
Show steps
Follow tutorials on advanced data visualization techniques, such as interactive visualizations, dashboards, and geospatial visualizations, to expand your knowledge and skills.
Show steps
-
Find tutorials on advanced data visualization techniques that interest you
-
Follow the tutorials step-by-step
-
Experiment with the techniques and apply them to your own data
Create a Statistical Data Visualization Dashboard
Show steps
Gain hands-on experience by building a data visualization dashboard to apply learned techniques.
Show steps
-
Select a Dataset and Define Metrics
-
Create Visualizations Using Libraries
-
Organize Visualizations into a Dashboard
Create a data visualization portfolio
Show steps
Create a portfolio of your data visualizations to showcase your skills and improve your understanding of the concepts covered in the course.
Browse courses on
Statistical Data Visualization
Show steps
-
Gather your best data visualizations
-
Create a website or online portfolio to display your work
-
Write brief descriptions of each visualization, explaining the purpose and methodology
Attend a Statistical Visualization Workshop
Show steps
Attend a workshop led by experienced professionals to gain hands-on experience and delve deeper into advanced visualization techniques.
Show steps
-
Research and find relevant workshops
-
Register and attend the workshop
-
Actively participate in discussions and hands-on exercises
Write a Blog Post on Statistical Visualization
Show steps
Solidify understanding and improve communication skills by sharing knowledge through writing.
Show steps
-
Identify a Topic and Define the Scope
-
Research and Gather Information
-
Write and Structure the Post
Attend a Statistical Visualization Workshop
Show steps
Deepen understanding and connect with experts through a dedicated workshop.
Show steps
-
Research and Select a Relevant Workshop
-
Attend and Engage in Workshop Sessions
-
Network and Connect with Experts
Create a Statistical Visualization Dashboard
Show steps
Apply your acquired skills to create an interactive data visualization dashboard that effectively communicates statistical insights.
Show steps
-
Choose a dataset that aligns with your interests or a specific industry
-
Explore the data and identify key insights and trends
-
Design and create interactive visualizations using Seaborn and Plotly
-
Integrate the visualizations into a cohesive dashboard
Develop a data visualization project
Show steps
Apply your knowledge by creating a data visualization project that demonstrates your understanding of statistical modeling and machine learning concepts.
Browse courses on
Statistical Modeling
Show steps
-
Choose a dataset and define the problem statement
-
Explore and preprocess the data
-
Develop data visualization models using Seaborn and Plotly
-
Interpret and present the results
Create a Statistical Visualization Blog Post
Show steps
Share your knowledge and insights by writing a blog post on a specific statistical visualization technique or a case study, reinforcing your understanding and contributing to the community.
Show steps
-
Choose a topic and conduct research
-
Write and edit the blog post
-
Publish and promote your blog post
Learn Advanced Statistical Visualization Techniques
Show steps
Expand knowledge by exploring tutorials on advanced statistical visualization techniques.
Browse courses on
Interactive Data Visualizations
Show steps
-
Identify and Select Relevant Tutorials
-
Follow Tutorials and Apply Techniques
-
Experiment with Different Approaches
Review Seaborn and Plotly
Show steps
Review your existing knowledge of Seaborn and Plotly for statistical visualizations to refresh your memory and strengthen your foundation.
Browse courses on
Seaborn
Show steps
Show steps
Show steps
- Revisit the Seaborn documentation
- Go through Plotly tutorials
- Practice creating basic plots using Seaborn and Plotly
Review python programming concepts
Show steps
Brush up on Python programming concepts to ensure a solid foundation for the course.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review Python data types, variables, and operators
- Practice writing simple Python programs
- Review Python libraries for data manipulation
Review Basic Statistics Fundamentals
Show steps
Refresh basic statistical concepts to strengthen understanding of advanced topics in the course.
Browse courses on
Statistics Fundamentals
Show steps
Show steps
Show steps
- Revisit Probability Theorems
- Review Sampling Distributions
- Practice Applying Hypothesis Testing
18 other activities
Expand to see all activities and additional details
Show all 21 activities
Read 'Data Visualization: A Practical Introduction'
Show steps
Gain fundamental concepts and practical insights into statistical visualization.
View
Data Visualization: A Successful Design Process...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters on Visualization Principles
- Review Case Studies and Examples
Practice creating different types of statistical plots
Show steps
Practice creating different types of statistical plots, such as histograms, scatterplots, and boxplots, to improve your understanding of how to represent data visually.
Show steps
Show steps
Show steps
- Find a dataset that you are interested in
- Choose a type of statistical plot that is appropriate for the data
- Create the plot using Python and the libraries covered in the course
Explore Seaborn and Plotly tutorials
Show steps
Familiarize yourself with Seaborn and Plotly through guided tutorials to enhance your understanding of data visualization techniques.
Browse courses on
Seaborn
Show steps
Show steps
Show steps
- Follow online tutorials on Seaborn and Plotly
- Experiment with different examples provided in the tutorials
Solve Guided Statistical Visualization Problems
Show steps
Reinforce understanding by solving guided problems covering various statistical visualization techniques.
Browse courses on
Data Exploration
Show steps
Show steps
Show steps
- Explore Different Types of Statistical Charts
- Practice Data Analysis Using Visualization
- Interpret Findings and Draw Conclusions
Guided Tutorials to Create Visualizations
Show steps
Follow guided tutorials and practice creating various types of statistical visualizations to enhance your proficiency.
Show steps
Show steps
Show steps
- Find online tutorials on statistical visualization with Seaborn and Plotly
- Follow the tutorials step-by-step and create visualizations
- Experiment with different parameters and options to customize your visualizations
Attend a Statistical Visualization Study Group
Show steps
Engage with peers and learn from shared discussions and perspectives.
Show steps
Show steps
Show steps
- Find or Join a Study Group
- Participate in Discussions and Share Insights
- Review and Synthesize Different Approaches
Peer Review and Discussion
Show steps
Engage with peers to exchange knowledge, provide feedback, and learn from diverse perspectives on statistical visualization techniques.
Show steps
Show steps
Show steps
- Join or create an online forum or study group
- Share your visualizations and insights with others
- Provide constructive feedback and engage in discussions
Solve Python coding exercises
Show steps
Reinforce your understanding of Python coding and statistical analysis techniques through practice drills.
Browse courses on
Python Coding
Show steps
Show steps
Show steps
- Solve coding exercises on data manipulation using Python
- Practice implementing statistical analysis algorithms in Python
Follow tutorials on advanced data visualization techniques
Show steps
Follow tutorials on advanced data visualization techniques, such as interactive visualizations, dashboards, and geospatial visualizations, to expand your knowledge and skills.
Show steps
Show steps
Show steps
- Find tutorials on advanced data visualization techniques that interest you
- Follow the tutorials step-by-step
- Experiment with the techniques and apply them to your own data
Create a Statistical Data Visualization Dashboard
Show steps
Gain hands-on experience by building a data visualization dashboard to apply learned techniques.
Show steps
Show steps
Show steps
- Select a Dataset and Define Metrics
- Create Visualizations Using Libraries
- Organize Visualizations into a Dashboard
Create a data visualization portfolio
Show steps
Create a portfolio of your data visualizations to showcase your skills and improve your understanding of the concepts covered in the course.
Browse courses on
Statistical Data Visualization
Show steps
Show steps
Show steps
- Gather your best data visualizations
- Create a website or online portfolio to display your work
- Write brief descriptions of each visualization, explaining the purpose and methodology
Attend a Statistical Visualization Workshop
Show steps
Attend a workshop led by experienced professionals to gain hands-on experience and delve deeper into advanced visualization techniques.
Show steps
Show steps
Show steps
- Research and find relevant workshops
- Register and attend the workshop
- Actively participate in discussions and hands-on exercises
Write a Blog Post on Statistical Visualization
Show steps
Solidify understanding and improve communication skills by sharing knowledge through writing.
Show steps
Show steps
Show steps
- Identify a Topic and Define the Scope
- Research and Gather Information
- Write and Structure the Post
Attend a Statistical Visualization Workshop
Show steps
Deepen understanding and connect with experts through a dedicated workshop.
Show steps
Show steps
Show steps
- Research and Select a Relevant Workshop
- Attend and Engage in Workshop Sessions
- Network and Connect with Experts
Create a Statistical Visualization Dashboard
Show steps
Apply your acquired skills to create an interactive data visualization dashboard that effectively communicates statistical insights.
Show steps
Show steps
Show steps
- Choose a dataset that aligns with your interests or a specific industry
- Explore the data and identify key insights and trends
- Design and create interactive visualizations using Seaborn and Plotly
- Integrate the visualizations into a cohesive dashboard
Develop a data visualization project
Show steps
Apply your knowledge by creating a data visualization project that demonstrates your understanding of statistical modeling and machine learning concepts.
Browse courses on
Statistical Modeling
Show steps
Show steps
Show steps
- Choose a dataset and define the problem statement
- Explore and preprocess the data
- Develop data visualization models using Seaborn and Plotly
- Interpret and present the results
Create a Statistical Visualization Blog Post
Show steps
Share your knowledge and insights by writing a blog post on a specific statistical visualization technique or a case study, reinforcing your understanding and contributing to the community.
Show steps
Show steps
Show steps
- Choose a topic and conduct research
- Write and edit the blog post
- Publish and promote your blog post
Learn Advanced Statistical Visualization Techniques
Show steps
Expand knowledge by exploring tutorials on advanced statistical visualization techniques.
Browse courses on
Interactive Data Visualizations
Show steps
Show steps
Show steps
- Identify and Select Relevant Tutorials
- Follow Tutorials and Apply Techniques
- Experiment with Different Approaches
Career center
Learners who complete Statistical Data Visualization in Python will develop knowledge and skills
that may be useful to these careers:
Data Visualization Engineer
Data Visualization Engineer
A Data Visualization Engineer specializes in creating visual representations of data to make it more easily understandable. This course in Statistical Data Visualization in Python can help build a foundation for this role by providing the skills needed to create clear and concise visualizations that can help businesses make better decisions. The course covers topics such as basic statistics, creating statistical plots and charts using Seaborn, Plotly, and Matplotlib, which are all essential skills for a Data Visualization Engineer.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical models to solve business problems. This course in Statistical Data Visualization in Python can help build a foundation for this role by providing the skills needed to create visualizations that can help communicate insights from data to stakeholders. The course covers topics such as basic statistics and creating statistical plots and charts using Seaborn, Plotly, and Matplotlib.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data to help businesses make decisions. This course in Statistical Data Visualization in Python can help build a foundation for this role by providing the skills needed to create visualizations that can help communicate insights from data to stakeholders. The course covers topics such as basic statistics, creating statistical plots and charts using Seaborn, Plotly, and Matplotlib, which are all essential skills for a Statistician.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data. This course in Statistical Data Visualization in Python can help build a foundation for this role by providing the skills needed to create visualizations that can help communicate insights from data to stakeholders. The course covers topics such as basic statistics and creating statistical plots and charts using Seaborn, Plotly, and Matplotlib.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help businesses make decisions. This course in Statistical Data Visualization in Python can be useful for a Data Analyst by providing the skills needed to create visualizations that can help communicate insights from data to stakeholders. The course covers topics such as basic statistics and creating statistical plots and charts using Seaborn, Plotly, and Matplotlib.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. This course in Statistical Data Visualization in Python can be useful for a Data Scientist by providing the skills needed to create visualizations that can help communicate insights from data to stakeholders. The course covers topics such as basic statistics and creating statistical plots and charts using Seaborn, Plotly, and Matplotlib.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make recommendations to investors and businesses. This course in Statistical Data Visualization in Python can help build a foundation for this role by providing the skills needed to create visualizations that can help communicate insights from data to stakeholders. The course covers topics such as basic statistics and creating statistical plots and charts using Seaborn, Plotly, and Matplotlib.
Business Analyst
Business Analyst
A Business Analyst uses data to analyze and improve business processes. This course in Statistical Data Visualization in Python can be useful for a Business Analyst by providing the skills needed to create visualizations that can help communicate insights from data to stakeholders. The course covers topics such as basic statistics and creating statistical plots and charts using Seaborn, Plotly, and Matplotlib.
Marketing Analyst
Marketing Analyst
A Marketing Analyst analyzes marketing data to help businesses make better decisions. This course in Statistical Data Visualization in Python can be useful for a Marketing Analyst by providing the skills needed to create visualizations that can help communicate insights from data to stakeholders. The course covers topics such as basic statistics and creating statistical plots and charts using Seaborn, Plotly, and Matplotlib.
Research Analyst
Research Analyst
A Research Analyst conducts research on a variety of topics to provide insights to businesses. This course in Statistical Data Visualization in Python can be useful for a Research Analyst by providing the skills needed to create visualizations that can help communicate insights from data to stakeholders. The course covers topics such as basic statistics and creating statistical plots and charts using Seaborn, Plotly, and Matplotlib.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models. This course in Statistical Data Visualization in Python may be useful for a Machine Learning Engineer by providing the skills needed to create visualizations that can help evaluate the performance of machine learning models. The course covers topics such as basic statistics and creating statistical plots and charts using Seaborn, Plotly, and Matplotlib.
Data Engineer
Data Engineer
A Data Engineer builds and maintains data pipelines to ensure that data is available to businesses for analysis. This course in Statistical Data Visualization in Python may be useful for a Data Engineer by providing the skills needed to create visualizations that can help communicate insights from data to stakeholders. The course covers topics such as basic statistics and creating statistical plots and charts using Seaborn, Plotly, and Matplotlib.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course in Statistical Data Visualization in Python may be useful for a Software Engineer by providing the skills needed to create visualizations that can help communicate insights from data to stakeholders. The course covers topics such as basic statistics and creating statistical plots and charts using Seaborn, Plotly, and Matplotlib.
Project Manager
Project Manager
A Project Manager plans and executes projects to achieve specific goals. This course in Statistical Data Visualization in Python may be useful for a Project Manager by providing the skills needed to create visualizations that can help communicate insights from data to stakeholders. The course covers topics such as basic statistics and creating statistical plots and charts using Seaborn, Plotly, and Matplotlib.
Product Manager
Product Manager
A Product Manager plans and develops products that meet the needs of customers. This course in Statistical Data Visualization in Python may be useful for a Product Manager by providing the skills needed to create visualizations that can help communicate insights from data to stakeholders. The course covers topics such as basic statistics and creating statistical plots and charts using Seaborn, Plotly, and Matplotlib.
For more career information including salaries, visit:
OpenCourser.com/course/l1skzd/statistical
Reading list
We've selected 11 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
Statistical Data Visualization in Python.
Save
This classic work on data visualization must-read for anyone interested in the field. Tufte provides a comprehensive overview of the principles of data visualization and offers practical advice on how to create effective visuals.
This comprehensive textbook on pattern recognition and machine learning valuable resource for anyone interested in using machine learning for data analysis and prediction. The book covers a wide range of topics, from supervised learning to unsupervised learning.
Save
This classic work on deep learning valuable resource for anyone interested in using deep learning for data analysis and prediction. The book covers a wide range of topics, from neural networks to convolutional neural networks.
This classic work on statistical learning valuable resource for anyone interested in using statistical methods for data analysis and prediction. The book covers a wide range of topics, from linear regression to support vector machines.
This comprehensive guide to data science with Python valuable resource for anyone interested in using Python for data analysis and visualization. The book covers a wide range of topics, from data preparation and transformation to machine learning and deep learning.
Provides a comprehensive introduction to the ggplot2 package, which is one of the most popular R packages for data visualization. The book covers a wide range of topics, from data preparation and transformation to chart creation and customization.
Provides a comprehensive introduction to D3, which popular JavaScript library for creating interactive data visualizations. The book covers a wide range of topics, from data preparation and transformation to chart creation and customization.
Provides a comprehensive overview of reinforcement learning, including both the theoretical foundations and practical applications. The book is written in a clear and concise style, and it is suitable for readers with little or no background in reinforcement learning.
This hands-on guide to data visualization with Python and JavaScript is written for readers with little or no experience in the field. The book covers the basics of data visualization with Python and JavaScript, including data preparation, chart selection, and best practices for presenting data.
Provides a comprehensive overview of Bayesian data analysis, including both the theoretical foundations and practical applications. The book is written in a clear and concise style, and it is suitable for readers with little or no background in Bayesian statistics.
Save
This practical guide to data visualization is written for readers with little or no experience in the field. The book covers the basics of data visualization, including data preparation, chart selection, and best practices for presenting data.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/l1skzd/statistical
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Mo Rebaie
Good to know
Develops basic understanding of statistical visualization techniques and libraries in Python
Suitable for learners with basic machine learning knowledge
Provides practical experience with statistical data visualization
Useful for adding statistical visualization skills to portfolios
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