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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 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 Maveryx,   A solution to last week’s challenge can be found here.    Don't forget that we are running a campaign inviting all of you to contribute to our upcoming 2024 Weekly Challenges! Your ideas and participation are crucial in shaping an engaging and innovative year ahead. Visit our blog post to review the guidelines and submit your challenge to earn our brand-new accolade, the Weekly Challenge Contributor Badge!    A special thanks to Mark Thompson (@Watermark)   for submitting this challenge some time ago. Your contribution is greatly appreciated, Mark! It is a fantastic opportunity for our users to test and enhance their RegEx tool skills.   Your company recently adopted a new customer relationship management (CRM) tool but overlooked a crucial detail: how to link company records in the new CRM to the companies in the legacy financial system. The common link between these systems is the company’s URL, but the website data entered by the sales team in the CRM is inconsistent and often incorrect.   As the person responsible for solving this issue, your tasks are to:   Match each company in the legacy financial system with its corresponding record in the new CRM. Analyze the CRM data to identify how many entries contain “dirty data,” meaning entries with subdirectories in the URL. Determine the number of distinct websites (base URL) that matched during the data integration process. Identify companies within the US that have multiple opportunities in the CRM (more than one). (Hint: Use the domain URL.) Identify companies with multiple opportunities (more than one) outside of the US.   Hint: To identify the countries, look for two-character top-level domain (TLD) codes in the URLs. Assume that any other code (for example, .com, .net, or .org) is associated with a US-based company.   If you want to learn how to use regular expressions to parse your data, you can review the following lessons in Academy: Using RegEx in Expressions Creating Regular Expressions Parsing Data with RegEx   Good luck!   ... View more