Alteryx Academy

  Hello Community members,   A solution to last week’s challenge can be found here.    This week’s challenge was submitted by Talia Sanders (@tsand22) . Thank you, Talia, for this incredible challenge!   Every year, parents and guardians need to purchase school supplies for their children at the beginning of the school year. This is a major cost that many families struggle to budget for.   In Part 1 of this challenge, your objective is to calculate the cost of school supplies for one child in each grade of elementary school (from kindergarten to sixth grade).   There are two datasets provided: School Supply List Text Input: A list of school supplies separated by grade. Cost of School Supplies Text Input: The cost of each unit from a list of school supplies.   Need a refresher? Review the following lessons in Academy to gear up: Changing Data Layouts Sorting Data   Ready? Good luck! The Academy Team   Download Start File   Data Source: https://www.in.gov/doe/it/data-center-and-reports/ ... View more

For those of you following along, thank you, you can find the solution to last week’s challenge (challenge #23) is HERE.    This week’s challenge will use the predictive time series tool called ARIMA.  If you don’t have the predictive tools you can find the installer at http://downloads.alteryx.com/downloads.html look for the link to “Predictive tools only”.   The predictive tools in Alteryx execute the analytics in an open source application called ‘R’, the advantage of using Alteryx vs. R is that Alteryx provides a straight-forward user interface and eliminates the need to program directly in the R language.  If you want to read more about what is happening under the hood, here is a link to the Wiki on ARIMA. https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average   The use case:  A retailer would like to forecast how many units of a particular product will be purchased from their locations based on a historical trend.   The source data contains weekly data for 2012 and 2013 details how many units have been moved. Some of the data, however, is populated with NULL values. For the NULL values, please assign the monthly average. If the monthly average is also NULL, assign the annual average.   Objective:  Forecast the number of units that will be sold in the six weeks following the available data. Have fun! ... View more

  Hi Maveryx,   A solution to last week’s challenge can be found here.    This challenge was submitted by our ACE and an active member in the Alteryx Community, Ippei Nakagawa (@gawa)  . Thank you Gawa for your contribution and we look forward to the next ones that you may bring to the table!   Across the United States, an annular solar eclipse was observed on October 14, 2023, and a total solar eclipse will be observed on April 8, 2024. In this challenge, you will determine which lucky cities in the USA observed an annular solar eclipse in 2023 and which ones will observe the total solar eclipse in 2024, as well as one lucky city that will observe both! The provided datasets include solar eclipse information from 2023 and 2024 including the name of the city; latitude and longitude of the city; how long the eclipse will last; and the eclipse’s category (P: partial, T: total, A: annual).   Your tasks: Create a combined list of the cities that observed the annular eclipse in 2023 and which cities will observe the total eclipse in 2024. Create a map to visualize each eclipse’s path throughout the year for 2023 and 2024. Determine the lucky city that will get to see both eclipses!   Hint: The provided datasets are JSON files. To facilitate the data extraction, use the Parse JSON tool in the Developer tab of Designer.   Need a refresher? Review these lessons in Academy to gear up:   Changing Data Layouts Parsing JSON Creating Spatial Objects   Sources: https://svs.gsfc.nasa.gov/ https://svs.gsfc.nasa.gov/vis/a000000/a005000/a005073/2023_city_times.json https://svs.gsfc.nasa.gov/vis/a000000/a005000/a005073/2024_city_times.json   Good luck!   ... View more

The link to last week’s challenge (exercise #53) is HERE.   This week’s challenge is to parse out City, State and ZIP code from some unformatted input data.    The data is in a nonstandard format - it is missing commas and some city names are two words and some city name are only one word, making parsing a challenge.   You need to be able to parse out the city name, state, and zip code if available.   Your goal is to create a process that will transform the data into a data table with separated columns for City, State, and ZIP.   Enjoy and as always I look forward to seeing some creative solutions. ... View more

A solution to last week's challenge can be found here.    This week's challenge was submitted by @Maskell_Rascal & @mceleavey - Thanks for your submission!   For this week's challenge, we have provided a list of transactions including the [Client_Number], [Date], & [Finance Amount]. Since not every customer makes a purchase everyday, they only appear on dates that they have made a purchase. We would like to include current customers in the dataset with a "finance amount" of 0 for the missing dates after their first purchase. They should not have entries for dates prior to their first purchase. Assume first purchase dates are the earliest entry in the dataset. ... View more