Workout Wednesday

2021 Week 19 | Tableau: Regional Sales Readout

Introduction Recently I’ve been spending more time working with Map Layers, so this week is dedicated to reimagining maps using this feature. You’re tasked with recreating a Regional Readout Report where Sales teams can see the performance of various cities within their Region.  They’ll be able to get more information by hovering over a city. …

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2021 Week 18: Can You Performance Tune This Workbook?

Introduction Learning complex techniques and beautiful design is super important to mastering visual analytics.  But, nothing will stop a user from regularly and joyfully using a dashboard than a poor performing one.  It doesn’t matter if it looks like Michaelangelo designed it or it has the cure for some crazy infectious disease – if it …

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Start Screen for the Cat or Croissant Game

2021 Week 18 | Power BI: DAX Trick – Cat or Croissant

Introduction The Power BI Workout Wednesday team has decided that May is made for DAX! Each challenge this month will require some DAX in the solution.  We are going to start our month of DAX with a fun game called Cat or Croissant. You might be familiar with the many versions of “animal or food” …

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2021 Week 16 | Tableau : Can you use Quick LODs to recreate this view?

Introduction With Tableau 2021.1 being released last month, I thought it was time for a challenge. This week we focus in on the new feature of Quick LODs.  Click to open in Tableau Public Requirements Dashboard Size: 800px by 700 px One Sheet Create a dual axis chart which shows the Sub-Category Average Sales by …

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2021 Week 15 | Tableau: Website Analytics

Introduction Lately I’ve been working with Google Analytics data, so I thought I’d pass on a data set to you!  Our data set was created by you the audience – it’s extracted directly from our website analytics. This week’s challenge focuses on a lot of little things coming together – dashboard design, formatting, and reference …

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2021 Week 14 | Tableau: Can You Recommend Profitable Return Customer Bundles?

Introduction Have you ever purchased something on Amazon and they show an extra random list of products in the “Customers that purchased X also purchased…” section and you think to yourself, “How in the world did they come up with that combination (e.g., sponges and Skittles)?”  They likely have high-powered recommender engines doing all of …

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