Challenge #170: 2019 Grand Prix US - Heat 1

[JimmyG]

A solution to last week's challenge can be found here!

This week's challenge will look familiar for those that attended Inspire this year. And for those that competed or were pit crew, you'll know this one well.

For those that have never attended Inspire, we have a contest among the fastest Alteryx users on stage in front of the entire Inspire audience. This year, roughly 5000 attendees watched as the 5 finalists competed to solve each Heat.

Although you won't have the aid of a pit crew to help you along the way, feel free to time yourself as you try complete this workflow. Give yourself about 2-3 minutes to read and understand the question, then start the clock. At the 11-minute mark, you may open up the tool contained named "Turbo Boost" which will reveal roughly 75% of a completed solution. Use this however you want.

For those that plan to attend Inspire Europe, keep your eyes peeled on the Analytics Blog to register for the Grand Prix Europe!

[NicoleJohnson]

I fear I cannot adequately express my love for the Grand Prix, and for solving these problems within ridiculous time limits. (Spoiler: THE LOVE IS DEEP.)

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But my favorite graphic is this one - 8 minutes, baby!!! 🙂

Huge props to all 5 contestants at the GP this year!! 

Cheers,

NJ

[wdavis]

Managed to complete the challenge, having seen some other solutions I think I could have made my life a bit easier! I wasted a bit of time when adding the (now apparently unnecessary) 0 to the empty sessions as type Double so my average figure for those years was slightly different due to the precision. Taken me about 16 minutes to complete

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[Adam_Dooley]

Messy but it gets there.  

[RolandSchubert]

My solution

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[patrick_digan]

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Just like @Kenda did on stage, my natural instinct is to join the data back as opposed to filling the gaps using the multi-row tool. It takes longer to build unfortunately.

[echuong]

Thought I'd remember the answer from the Grand Prix, but I had to think through this one! Great challenge

[bnelly1987]

It was a lot of fun watching this happen at #Inspire19

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[JORGE4900]

I wonder why they decided to use a ETS model to predict the quantity of participants, since the data looks like a linear regression. To be honest, I would have used a linear regression, since the data does not seem to have big seasonal up or downs because it is summarized by year.

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[adley31]

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