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Overview For the case study, you will work with data from the Great British Baking Show (a series I highly recommend for cozy & inspiring viewing). Even if you haven’t seen the series, I expect you will find the dimensions and measures interesting. Using the following data files, create a dashboard and/or story in Tableau with at least three visuals. You can choose to summarize (by season, demographic, theme, etc.) and/or highlight interesting features and trends. Your dashboard should have at least two filters that work for all relevant worksheets. Late submissions will not be accepted. Please submit whatever you have by 8:30 PM Central. Ready, get set… viz! Data files Bakers.csv, ChallengeBakes.csv, Episodes.csv, Outcomes.csv, and Seasons.csv Also see the data dictionary, which includes a flow chart that can be used to model your data source connections. Specifics When you are finished, upload a .twbx file (Tableau workbook file with extracts). Like the bakers in the series, I recommend putting thought into your design, and consider visual appeal. I won’t grade your submissions by comparisons with classmates, but I will be the judge of how much effort you put in to make your visual stand out. Tips: I suggest starting with a storyboard, though it is not required If you choose to narrow in on a particular season(s), I recommend using either Season or Netflix Collection as dimensions, and perhaps as a filter consider aggregating on COUNT instead of SUM/AVG if you would like to use custom icons, Wikimedia Commons (hint: search baking icon) and flaticon.com are allowlisted have fun — even if you aren’t familiar with the series, you should be able to come up with some compelling visuals I apologize for spoilers, especially if you use the fields Eliminated, Winner, or Star Baker!   Resources Don’t forget that the Tableau documentation is available for consulting.

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Which principle of accessibility suggests that keyboard shortcuts should be available wherever possible?

What is one tip you learned for effective storytelling?

How can we make a prediction using linear regression?

What American statistician was mentioned in class, known for multiple books on visualizing data?

Which Gestalt principle suggests people tend to see shapes as whole, even if they are not truly complete?

What concept describes the process of handling data and preparing it for analysis?

Which Gestalt principle tells us that we tend to assume objects are uninterrupted when there is an intersection with another object?