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

The solution to last week's challenge can be found HERE.   This week we are changing it up a bit and pulling a question that was asked to the Community last week! The issue was that the user had a spatial object that had "holes" inside the object. The challenge was to fill in any holes within the object. The answer to this solution can be solved in very few steps, but the challenge comes in knowing or discovering the right configurations within the tools used to solve the problem. If you would like any hints or to find out how the question was solved, check out the original post which also contains the solution:   https://community.alteryx.com/t5/Advanced-Analytics/Remove-holes-from-a-polygon-object/m-p/48652   ... View more

The link to last week’s challenge is HERE.   Wow, exercise #51 already.  Can’t believe it has been nearly a year!       For this week I would like to investigate some of the geospatial tools available in Alteryx   The spatial tools will allow you to calculate distances, create trade areas, find intersection of spatial objects, calculate sizes, etc..  Even if you don’t have the spatial data from Alteryx (the spatial data provides supporting data packages for geocoding, address standardization and calculations using the road network), most of the tools will still perform as long as you have the spatial references or objects already available in your input data.   Use Case: For regulatory purposes we have been asked to identify the all the counties within a 15-mile radius of each of our stores as well as identifying the percent of the county’s area that is overlapped (Coverage).   Objective: Part 1) Calculate the percent overlap by individual store Part 2) Calculate the percent of overlap by county for the entire store network   ... View more

A solution to last week’s challenge can be found here. Source: Canva   To solve this week’s challenge, use Designer Desktop. This challenge comes to us from @Pawel_Paleczny    . Thank you for your contribution. Let’s take some data from the FIFA World Cup that took place in Qatar and perform analyses on the location. The dataset contains information about the stadiums: stadium name, city, capacity, latitude, and longitude. For this challenge: 1. Find all the host cities of the World Cup on the map. 2. Rank the distance of the stadiums starting from the farthest from Doha, the capital of Qatar. The distance should be measured in miles. For the geographic data of the stadiums, let's use the geographic coordinates of the cities.   Data Source: https://www.kaggle.com/datasets/palecznypawel/worldcup2022-fixture   ... View more

A solution to last week's Challenge has been posted HERE!    This year's (2017-2018) flu season has been reported to be the most severe in the past decade.  In particular, influenza-related hospitalizations are on the rise across a wider range of age groups than normal.  For this week's Challenge, we'll use data from the Center for Disease Control (CDC) concerning California's data on influenza-related hospitalizations.  Create a table that shows the cumulative number of hospitalizations per age group for each flu season.   Looking for additional analyses to do?  How about a week-by-week comparison of hospitalizations?  Or perhaps a forecast of the number of expected hospitalizations for a particular age group?  Or maybe a visual representation of the data is up your alley!     Stay healthy out there!    ... View more