Alteryx Academy

    Hi Community,   We posted the solution JSON file to Cloud Quest #47. Check it out and let us know what you think! Send suggestions to academy@alteryx.com or leave a comment below!     Let’s dive into this week's quest!   Download the provided JSON file containing your starting data and workflow files. Upload Start Cloud Quest 48.json file to your Alteryx One library. All necessary datasets are contained within Text Input tools in the workflow.   For more detailed instructions on how to import and export Designer Cloud workflow files, check out the pinned article Cloud Quest Submission Process.         Scenario:   Your film studio is starting production on a new project. As the film’s producer, you need to recommend the top five actors who offer the strongest mix of popularity, past financial performance and dependable on set behavior. Then recommend the top three shooting locations that will give you the strongest financial performance while keeping location costs low. The dataset contains a list of actors, their popularity and diva scores, and information about the performance of their previous films.   Tasks: Rank the top five actors by their average Value Score across all previous films. Rank the top three locations by location score: average revenue divided by average cost ratio.   Notes:  Popularity Score (1 to 20): higher values indicate stronger marketability. Diva Score (1 to 100): lower values indicate better on set behavior. Normalized previous film revenue: the actor’s contribution to box office results. Value Score: the average of the scaled popularity score, the scaled and inverted diva score, and the normalized revenue.   Hint: Normalization means you place different numeric fields on the same scale. You take each value, subtract the minimum of that field, and divide by the full range. This produces a number between 0 and 1. It lets you compare popularity, revenue and diva score without one field dominating the others because of its original size. Image: Generated by Google Gemini, Dec 03, 2025, https://gemini.google.com.   Earn Cloud Quest badges:   After completing your quest, head back to your Analytics Cloud library: Download your workflow solution file. In your reply, attach both your JSON solution file and a screenshot of your workflow. Keep submitting—every solution gets you closer to earning more Cloud Quest badges!   Here’s to a successful quest! - The Academy Team     Download Start 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

A solution to last week's challenge can be found here.    This week's challenge focuses on a group of relatives and their birthdays. Of the six family members, we have one person's date of birth and a series of clues to calculate the others. Provide the DOB for all family members and the day of the week s/he was born. ... View more

  Hello Community members, A solution to last week’s challenge can be found here.   We are back with another exciting challenge, and this one is all about hot sauces! A big thank you to James Gust for creating this spicy challenge!   This week’s challenge is the first of two parts. For Part 1, you have a collection of individual reviews of hot sauces, but the data is spread across multiple tables due to how it was stored in a relational database. Now, it is your job to bring it all together and extract meaningful insights!   Your datasets include the following: Hot Sauce Data.xlsx: Contains details about the sauce names, manufacturing information, ratings, spiciness, viscosity labels, tastings, and flavors. Tasting and Flavors1 Text Input: Maps tasting IDs to Flavor IDs. Flavors Text Input: Maps Flavor IDs to their corresponding flavor names. Your goal in this challenge is to identify the viscosity and flavor labels associated with each sauce. To do so, you need to: Find the viscosity labels for each sauce (how thick or runny they are). Find the flavor labels associated with each sauce (for example, spicy, tangy, garlicky). Ensure each Sauce ID is unique in the final output, avoiding duplicate entries. Your output should include Sauce ID, viscosity, and flavors. HINT: Joining data by flavor and tastings is key to finding your answers!   Need a refresher? Review the following lessons in Academy to gear up: Joining Data Summarizing Data   We can’t wait to review your solutions!   Happy solving! 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 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