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Hi Community members,   A solution to last week’s challenge can be found here.   This challenge was submitted by Douglas Perez, @dougperez . Thank you, Douglas, for your submission!   A company recently hosted an internal Alteryx certification event to promote professional growth and upskilling across the organization. Each participant was assigned to a team, and throughout the event, employees earned various professional certifications.   Now that the event has concluded, it’s time to analyze the results and determine which team came out on top!   You’ve been provided with two datasets: A certifications dataset containing certification records, each with its status (Expires or Expired) and the date. A team mapping dataset linking each participant to their respective team.   Analyze the results and rank the teams based on the number of valid certifications earned by their members. Follow the rules below:   Only include certifications that are currently valid (status is Expires). Focus only on certifications with names that include Alteryx Designer or Server. Exclude any certifications that mention Cloud or Trifacta. Aggregate the results by team and rank them from highest to lowest based on the number of valid certifications.   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: Dataset generated by ChatGPT.   Download Start File ... View more

Hi Maveryx,   A solution to last week’s challenge can be found here.   This challenge comes to us from @NicoleJohnson    Thank you for your contribution, Nicole!   Picture yourself as an auditor within a prominent financial institution. Your primary duty involves performing meticulous security audits across various software applications. This entails deciphering the extent of access granted to individual users based on the last password update and identifying instances of any breaches in security protocols.   In this challenge, your task is to determine the number of password and security violations attributable to each user. The dataset you are working with contains data for three distinct users. However, there is a slight complication: all the pertinent information is consolidated within a single column. Hint: The number of password or security violations is indicated in parentheses following PSWD-VIO and SEC-VIO. For example, PSWD-VIO(2) indicates the user has two password violations and SEC-VIO(3) indicates the user has three security violations.   ... View more

Hello Community Members!   A solution to last week’s challenge can be found here.   Who is up for a predictive challenge? With Black Friday just around the corner, it is the perfect time to put your predictive skills to the test. This week, take on the task of predicting how many customers are likely to switch their telecommunications provider, also known as churning.   This challenge, submitted by Ollie Clarke (@OllieClarke), is designed to take your abilities with the Designer Predictive palette to the next level.   You have a training dataset (Training Data.yxdb) containing information about a telecommunication company’s customers and whether they have churned. You also have a testing dataset (Testing Data.yxdb) with information about new customers. Your task is to predict how many of the new customers from the testing dataset are likely to churn from the company.   Just follow the steps and you will be able to tackle this challenge, even if it is your first time building a predictive model!   Here is how to get started: Split the Training Data: Divide the training data into two samples with a seed number of 1: Estimation (70%) and Validation (30%). Build Four Models: Using the Estimation output, create four models—Boosted Model, Decision Tree, Forest Model, and Logistic Regression. Use their default configurations to predict churn, using all variables (columns) except for ID. Compare Models: Use the Validation output to compare the models and identify the one with the highest F1 score. (Use the E anchor from the Model Comparison tool to identify the highest F1 score.) Score the Testing Data: Rerun the entire training dataset through the best model, then score the testing data to calculate the likelihood of churn (Score_yes > 0.5). Count how many customers in the testing dataset are likely to churn. Need a refresher? Review the following lessons in Academy to gear up: Predictive Modeling Creating a Predictive Model Happy solving! Source: https://kaggle.com/competitions/customer-churn-prediction-2020   The Academy Team ... View more

As we continuously strive to improve and enhance our certification offerings, we want to inform you about an update to the certification program. ... View more

  Hi Maveryx,   A solution to last week’s challenge can be found here.    Are you a big fan of Formula 1?    This challenge is an exciting part of the Alteryx Formula 1 Fanalytics event.    Rev your engines and get ready to race to the finish. Your goal is to analyze driver lap time data and pinpoint the best driver in rainy conditions, making them the top choice for any wet race conditions.     Bonus: Tune in to a recording of the “Alteryx + McLaren: Formula To Success with AI & Analytics” webinar to hear from McLaren Racing CEO, Zak Brown, and Alteryx CIO Trevor Schulze on how McLaren and Alteryx ride the data wave using AI and analytics to conquer change.    Source: All data provided for this challenge is entirely fictional. ... View more