Alteryx Academy

This solution to last week's challenge can be found HERE.   A website log tracks every click visitors are making on a particular link on the site in order to measure interest in the link. However, web consumers are impatient and may click a link multiple times in short succession. The goal of this exercise is to flag "duplicate clicks" that is defined as the event fire date happening within 30 seconds of the previous event fire date for the same user (IDFA) and device (DeviceID) ... View more

  Hello Community members,   A solution to last week’s challenge can be found here.    This week’s challenge was submitted by Talia Sanders (@tsand22) . Thank you, Talia, for this incredible challenge!   Every year, parents and guardians need to purchase school supplies for their children at the beginning of the school year. This is a major cost that many families struggle to budget for.   In Part 1 of this challenge, your objective is to calculate the cost of school supplies for one child in each grade of elementary school (from kindergarten to sixth grade).   There are two datasets provided: School Supply List Text Input: A list of school supplies separated by grade. Cost of School Supplies Text Input: The cost of each unit from a list of school supplies.   Need a refresher? Review the following lessons in Academy to gear up: Changing Data Layouts Sorting Data   Ready? Good luck! The Academy Team   Download Start File   Data Source: https://www.in.gov/doe/it/data-center-and-reports/ ... View more

Strava is a popular app that serves a social network for all athletes. In particular, the app is popular with runner and cyclists. However, sometimes we want to see the data differently from what is served to us. Below is a GPX file containing a mountain bike ride I completed a few weeks ago. The challenge is to parse it and create report snippets like these: A speed and elevation dual-axis chart A map of the route Feel free to try anything else on top of these! I will not be providing a starting workflow since the results are pictured above, and connecting to the .gpx file is part of the challenge! ... View more

  Hello Community Members,   A solution to last week’s challenge can be found here.    This week’s challenge was created by Jennifer Fugita @Fugi. Thanks, Jennifer, for this fun challenge!   The Great British Baking Show is an iconic baking competition and has been a staple of TV entertainment for 14 seasons. Fans everywhere have been whisking up some burning questions. From age breakdowns to star bakers, handshakes, and more, there is a lot of data to uncover behind the flour, frosting, and fierce competition. There are five datasets for this challenge:   Bakers.csv includes information about the participants of each season. Episodes.csv details information about each episode, such as their themes; signature; technical and showstopper baking challenges; and timing to complete each challenge. The Standardize Themes Text Input tool reveals the baking challenges’ themes and theme categories. Outcomes.csv includes season episodes, bakers’ names, and how they performed in each episode. Seasons.csv outlines information about each season’s hosts; judges; winners and the year they won; and information about the network and streaming services where the show is available. So, grab your apron and your Designer data analytics tools to complete the following tasks:   Calculate the youngest, oldest, and average age of bakers for each gender. Count how many episodes were in each season and determine which season had the fewest episodes. Determine the top three baking theme categories across the regular episodes of the 14 seasons. Note that the last episode of each season is a special episode final bake-off. HINT: Exclude the Final theme from the Theme field. Combine the Outcomes and Seasons datasets to find out the name of the winner who had the most star baker and handshakes combined. HINT: Beware of duplicate values in the Seasons dataset!   Need a refresher? Review the following lessons in Academy to gear up: Joining Data Summarizing Data   Good luck!   The Academy Team   Download Start File   Download Solution File   Data Source: https://thegreatbritishbakeoff.co.uk/   Dataset: https://public.tableau.com/app/learn/sample-data ... View more

Here is this week’s challenge, I would like to thank everyone for playing along and for your feedback. The link to the solution for last challenge #10 is HERE.    The use case:   A manufacturing company receives customer complaint data on a daily basis from their call centers about the medical parts they distribute to their customers. The company monitors these comments to understand which parts and part groups have the highest complaint rate. This helps the company prioritize which parts to focus on from a development standpoint.   In this exercise, take the customer complaint data and identify which bucket the complaint falls within. The complaint can fall into multiple buckets and needs to be flagged as these complaints take highest priority. Create an aggregate view of which buckets or bucket pairings have the highest # of complaints.   This is only a subset of data so all records will not be assigned to buckets and can be ignored.   Update: As of 9/26/19, this challenge start file and solution were modified.  Your solution will not match those posted by other Community members prior to this date.  ... View more