Alteryx Academy

A solution to last week's challenge can be found here.    This week's challenge was submitted by @CharlieS - Thank you for your submission!   Each of the states in the USA have a proud history and unique culture. They also have unique two letter abbreviations. Inspired by this NPR Sunday Puzzle, use the two letter abbreviations from four states that border each other to create eight letter words.   One input contains spatial objects for each state and the other contains eight letter words. Find the possible combinations given the constraints above. ... View more

Hi Community members,   A solution to last week’s challenge can be found here.   This two-part challenge was submitted by Guilherme Dias, @Bennu. Thank you, Guilherme, for your great submission!   In the previous challenge, you have already identified critical production trends and model behaviors for electric vehicles. Now, shift your focus to innovation and decline — two sides of product lifecycle in the EV industry.   In the second part of this challenge, you analyze which years saw surges of new model launches and which saw waves of production extinction. Reminder: Only include years from 2010 to 2024 in your analysis, as 2025 data is incomplete.   Here are the two tasks you need to accomplish:   Determine which two years had the most distinct new models launched and from which brands were these models launched. Identify the year with the highest number of distinct models that stopped being produced, relative to the prior year. Count each model that was produced in year Y but not in year Y+1 as “extinct,” even if it returns in later years.   You are provided with one dataset (the same dataset from part 1 of this challenge): Electric Vehicle Data – provides the model year, manufacturer (brand) and car model.   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://catalog.data.gov/dataset/electric-vehicle-population-data   Download Start File ... View more

Aggregate Consumer Purchases: For this week’s exercise we will look at customer purchase behavior to decide if we should offer a “Meal Deal” that would add a side and drink to a purchase of pizza or a burger. The incoming data is larger than usual for these exercises so I have packaged the workflow as an Alteryx Package. The link to the solution for last challenge #7 is HERE.   This week’s Objective: In order to decide if we should start including a new "Meal Deal" on our menu we want to study the potential impact on recent transactions. Please identify the number and percentage of orders since July 1, 2013 which include the following categories of food: Pizza OR Burger along with a Side and Drink.   Summary of Data: Point of Sale data includes the ticket level information, and the lookup table categorizes items into higher level food categories.   Hint: Don't forget to join to the lookup table and filter by date.   As always we look forward to your feedback and suggestions!   UPDATE 01/18/2016: The solution has been uploaded.   UPDATE 12/28/2016: The challenge, text and solution have been updated. ... View more

    Hi Community members,   A solution to last week’s challenge can be found here.    This challenge was submitted by Casey Koopmans @cwkoops. Thank you, Casey, for this interesting challenge!   In global supply chain operations, duty drawback programs allow exporters to reclaim duties paid on imported raw materials once the resulting finished goods are exported. To comply with such programs, companies must maintain traceability between the raw materials used and the finished goods produced. For this challenge, imagine you work with a manufacturer that processes raw materials into finished goods. Each raw material is received in lots, and the manufacturer needs to trace how much of each raw material lot is consumed in each finished good lot, following a strict First In, First Out (FIFO) usage policy. Your task is to develop a FIFO allocation model that maps the consumption of raw material lots to finished good lots, enabling full traceability. You are provided with two datasets: a.        Raw Material Lots: a table that lists all the raw material lots received: b.        Finished Good Requirements: a table that lists each finished good lot and its corresponding raw material requirements:   Each finished good lot consumes a certain amount of a specific raw material type (SUB), which must be traced back to specific raw material lots.   Your task for this challenge: Using FIFO allocation, build an iterative macro that helps determine which raw material lots are consumed by each finished good lot, and how much of each lot is used. To do so, consider the following:   Group raw material lots by ITEM (raw material type) and sort them in the order received. Iterate through each finished good lot in the order produced. For each finished good lot:    - Identify the required SUB (raw material type) and SUB QTY (quantity required).    - Allocate this quantity using the earliest available raw material lots (FIFO).    - Deduct the used quantity from raw lot availability and record which lots were used and how much. Output a table that shows:    - Finished Good Lot ID (FG LOT)    - Raw Material Type (SUB)    - Raw Material Lot (RAW LOT)    - Quantity Used (QTY USED)   Source: Sample Data set provided in the workflow as text inputs.   Good luck! 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.   In this week’s challenge, you apply your skills with formulas, macros, and analytic apps to simulate a basic probability experiment: flipping a coin. This challenge helps you practice building iterative logic and user-driven apps in Alteryx Designer.   Here are the steps you need to follow to build the flip-coin app:   Create an Iterative Macro (`Coin Flip Macro.yxmc`) that repeats the coin flip simulation for a specified number of times. This macro should: Initialize with a Text Input containing iterations, head count and tail count. Update the counts on each iteration. Use a condition to stop once the iteration count reaches the user-specified total. Once the macro is created, build the analytic app that does the following: Prompts the user with a Text Box question: "How Many Coin Flips?" Passes this input into the macro to determine the number of simulations it runs. Displays how many times "Heads" and "Tails" occurred.   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   Download Solution File ... View more