Challenges & Quests

Hi Community members,   A solution to last week’s challenge can be found here.   This challenge was submitted by Erin Miller. Thank you, @Erin , for another great challenge!   According to Erin, someone who wins at Mario Kart seems to know every shortcut and trick, and it’s easy to assume they’re always choosing the perfect character and kart combo. But not anymore! We now have access to in-game data for every driver, kart/body, tire, and glider in Mario Kart 8 Deluxe.   Using the datasets provided, the mission is to identify the most optimal combinations that deliver the best overall racing performance.   What You Need to Do Compute the average value for each stat category (speed, acceleration, handling, etc.) across each individual dimension: driver, kart/body, tire, and glider. Determine the average stat values for every combination of driver, kart/body, tire, and glider. For example: What is the average speed for a specific driver when paired with a given kart, tire, and glider? For each combination, calculate a total weighted average using the provided weights table (or create your own!) to reflect the relative importance of each stat.   The final output should include each driver–kart–tire–glider combination, the corresponding average stat values, and a total weighted score.   Feeling a Little “Extra”? Use a macro to automate repetitive tasks (such as calculating averages).   Once you have completed your challenge, include your solution file and a screenshot of your workflow as attachments to your comment.   Good Luck! The Academy Team   Source: https://www.kaggle.com/datasets/marlowspringmeier/mario-kart-8-deluxe-ingame-statistics   Download Start File   ... View more

A solution to last week's challenge can be found here.    Using the values in the attached file "womens_world_cup_data.txt", determine which team won the most matches.      Go Team USA!   ... 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

Aggregate Consumer Purchases: For this week’s exercise we will look at customer purchase behavior to decide if we should offer a “Meal Deal” that would add a side and drink to a purchase of pizza or a burger. The incoming data is larger than usual for these exercises so I have packaged the workflow as an Alteryx Package. The link to the solution for last challenge #7 is HERE.   This week’s Objective: In order to decide if we should start including a new "Meal Deal" on our menu we want to study the potential impact on recent transactions. Please identify the number and percentage of orders since July 1, 2013 which include the following categories of food: Pizza OR Burger along with a Side and Drink.   Summary of Data: Point of Sale data includes the ticket level information, and the lookup table categorizes items into higher level food categories.   Hint: Don't forget to join to the lookup table and filter by date.   As always we look forward to your feedback and suggestions!   UPDATE 01/18/2016: The solution has been uploaded.   UPDATE 12/28/2016: The challenge, text and solution have been updated. ... View more

Hi Community members,   A solution to last week’s challenge can be found here.   This challenge was submitted by Andrew Bacon, @abacon. Thank you, Andrew, for your submission!   As summer heats up — and with it, a season full of hot dog eating competitions across the US — it's time to host your own version... in Alteryx!   In this challenge, you’ll simulate a 3-round hot dog eating competition using an iterative macro.   You’re given a dataset of 12 contestants with the following info: Name Hot Dogs per Minute (HDPM) — their starting speed Drop Rate per Minute — how much slower they get each round Group — initial competition group (1, 2, or 3)   Each round, contestants eat hot dogs at their current HDPM. After each round, the contestant's HDPM drops based on their personal rate.   The rules for each round are as follows:   Round 1 – 10 Minutes: 3 groups of 4 contestants — top 2 from each group advance Round 2 – 11 Minutes: 2 groups of 3 contestants — top 2 from each group advance Round 3 – 12 Minutes: Final 4 compete — the top eater wins   Your tasks: Identify the winner and report how many hot dogs they ate in the final round. List all advancing contestants from each round, along with how many hot dogs they ate in that round.   Use an iterative macro to simulate each round and apply the performance drop over time.   Hint: Pay close attention to the number of contestants advancing after each round.   Once you have completed your challenge, include your solution file and a screenshot of your workflow as attachments to your comment.   Good Luck! The Academy Team   Download Start File      |   Download Solution File ... View more