Alteryx Academy

Hi Community members,   A solution to last week’s challenge can be found here.   This challenge was submitted by our ACE Ippei Nakagawa, @gawa. Thank you, Gawa, for your submission!   In this week’s challenge, your task is to detect transactions that may indicate money laundering. Money laundering schemes often follow a circular pattern, such as: A sends money to B B sends money to C C sends the same money back to A   These transactions form a loop, and your goal is to identify such suspicious loops in the dataset.   To qualify as potential money laundering, a group of transactions must meet all the following criteria: Consistent Amount: The same amount of money is transferred in each transaction forming a loop. Cyclic Return: The money eventually returns to the original sender, forming a complete loop. Chronological Order: Transactions within the loop occur in strictly ascending order of transaction date. Minimum Hops: A valid loop must include at least two intermediary transactions (i.e., at least three participants in total, including the original sender).   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   Source: Randomly generated dataset using Python. ... View more

A solution to last week’s challenge can be found here.  Source: www.californiaavocado.com/blog/avocado-fruit-or-vegetable   Love them or hate them, Avocados are a popular food that have been increasing in popularity. Did you know that they are technically a berry?   At any rate, this week's challenge is to find the regions with the largest increase (by percentage) of avocado consumption for [total volume] and each PLU code. PLU codes are the 4 digit codes which classify produce in retail stores. The final output should include both conventionally grown and organic results. ... View more

A solution to last week's challenge can be found here!    This week's challenge comes to us from @bdelarosa - thank you for your contribution!    Using the provided input files, organize the data to display:   - Inventory levels for products by location - Previous sales history per product per location - Average sales per product per location - Total incoming product per product per location - Earliest Purchase Order date a product may arrive.   Display all inventory levels even if no sales exist or no products are on purchase order.    Source: GIPHY ... View more

The link to the solution for last challenge #37 is HERE.   One of the powers of Alteryx is to be able to batch processes without the need to write scripts of use complicated code.   A single output tool can be configured to generate many output files.   Use Case:  A company needs to blend data from three sources and generate an output file for each product - region combination, a total of 15 output files.   Objective:  Create a cross join between the Product Group, Region Reference and Data tables to produce 15 unique CSV Data files.  Please note that only 1 output tool should be leveraged in your solution.   Thanks to all that are playing along! ... View more

The link to last week’s exercise (exercise #45) is HERE   I love to share real world use cases as exercises.  This week’s exercise comes from a customer that had data coming from in a legacy system and needed to transform the date field for a new system.  It is a straightforward exercise but shows how easily Alteryx can apply business logic to most any data transformation problem.   Use case: The Input contains dates formatted as year, month and day. In this case, the first character determines if the year should begin with 19 or 20.   If the first character is 0 then the year starts with 19, and when the first character is 1 the year starts with 20.  The remainder of the date following the 0 or 1 is the remaining year digits followed by month followed by day.   Objective: Please convert these strings into date formatted field. ... View more