Challenges & Quests

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

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

Hi Community members,   A solution to last week’s challenge can be found here.   This challenge was submitted by Griffin Welsh, @griffinwelsh. Thank you, Griffin, for your submission, and for making our Halloween even more fun!   Halloween is just around the corner, and you have been tasked with creating the scariest possible haunted house for your accounting department. You have received survey results showing each employee’s fear and corresponding fear intensity.   However, not everyone filled out the survey correctly.   Your goal is to determine the scariest haunted house feature, measured by the total sum of fear intensity from all respondents. For respondents who listed a fear but did not provide an intensity, use the median intensity from other respondents who reported the same fear. Ignore any respondents who did not list a fear in their response.   Bonus Task Was the first challenge not scary enough for you? You have now been given a budget of $1,700 to build your haunted house. Your goal is to find the combination of features that maximizes the total fear intensity while staying within budget. Each feature can only be used once.   Using your results, create a short proposal paragraph describing your recommendations based on the data.   Once you’ve completed the challenge, include your solution file and a screenshot of your workflow as attachments to your comment. Good Luck! The Academy Team   Source: Dataset generated by Microsoft copilot.   Download Start File Download Solution File  ... View more

Hi Community members,   A solution to last week’s challenge can be found here.   This challenge was submitted by Geetika Tadlimbekar, @Inactive User. Thank you, Geetika, for your submission!   The Golden Rectangle is often considered one of the most aesthetically pleasing shapes in art and architecture. This proportion appears in famous works such as the Mona Lisa and the Parthenon. In this challenge, your goal is to analyze a dataset of rectangles and identify those that most closely match the Golden Ratio.   Tasks Calculate the ratio (longer side divided by shorter side) for each rectangle in the dataset. Identify the top 4 rectangles whose ratios are closest to the Golden Ratio (approximately 1.618).   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

Hi Community members,   A solution to last week’s challenge can be found here.   This challenge was submitted by Erin Miller, @Erin . Thank you, Erin, for your submission!   You are a data analyst reviewing independent medical review outcomes. Your objective is to identify patterns that influence whether a health plan's decision is upheld or overturned.   Use the dataset to analyze trends and answer the following questions.   For each diagnosis category, calculate the percentage of cases where the health plan’s decision was upheld and where it was overturned. Exclude any records with missing diagnosis categories. For each gender (male or female), identify the age range most commonly associated with overturned decisions. Determine which diagnosis and treatment categories have the highest number of overturned decisions. Create a graph/chart showing how the percentage of overturned decisions has changed over time, broken down by year. 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/prasad22/ca-independent-medical-review   Download Start File Download Solution File  ... View more