This course will cover the more complex concepts that become involved when working beyond simple datasets. Exploring the connection between visual aspects and data understanding, we will examine how those concepts work together through data storytelling. After reviewing key points on how to avoid problematic visualizations and data misrepresentation, you will continue working in Tableau performing multivariate descriptive analysis of the S&P 500 stock sectors.
Data Storytelling
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Data Storytelling
This course is part of Use Tableau for Your Data Science Workflow Specialization
Instructors: Julie Pai
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8 hours to complete
3 weeks at 2 hours a week
Flexible schedule
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Assessments
4 assignments
Taught in English
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This course is part of the Use Tableau for Your Data Science Workflow Specialization
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There are 4 modules in this course
For all but the simplest datasets, complex analytics requires a multivariate understanding of the data being studied. Visual interactivity with the data is a key component of multivariate analytics and makes finding higher dimensional relationships in complex datasets more intuitive. In this module, we’ll take a look at various chart types and visualizations used to express comparisons. You will also have the opportunity to practice correlations in Tableau.
What's included
2 videos4 readings1 assignment1 discussion prompt
How important do you think interaction is for general audiences to gain an understanding of complex data sets? In this module, we’ll explore how visual interactivity with the data makes higher dimensional relationships in complex datasets more intuitive and debate whether interactivity in visualization is a hindrance or help for larger audiences.
What's included
4 videos3 readings1 assignment1 discussion prompt
Why does storytelling matter when delivering data to your audiences? What are the principles of storytelling that you should implement into your visuals? In this module, we’ll examine aspects of storytelling and how to structure your story to effectively communicate the right insights to your key stakeholders.
What's included
1 video4 readings1 assignment1 discussion prompt
Any time we create a visualization, we create an abstract model of the data. As designers, we must ensure that the data is represented as truthfully and objectively as possible because they have the ability to mislead, deceive, or confuse. In this module, we’ll take a look at some problematic visualizations and how to avoid misrepresenting data. You’ll also put your knowledge to use and perform multivariate visualization methods in Tableau.
What's included
4 readings1 assignment1 peer review1 discussion prompt
Offered by
Recommended if you're interested in Data Analysis
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University of Illinois Urbana-Champaign
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University of California, Davis
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