Alteryx Academy

Last week's challenge is posted HERE!   OK, I have a problem... for you to solve!   We are selling bottles of wine - each of which has a unique ID since it's a limited vintage. Customers can request their wine bottles be delivered in various different sizes of delivery box. Customers order wine in ranges - from ID 1 to 25 for example.   Challenge: Build a macro that takes in order ranges, allows the user to input the box size, and outputs the order broken into the requested box sizes.   There are 3 sample outputs displayed to test the different box sizes: - Box size of 6 - Box size of 12 - Box size of 96 The macro must check for, or prevent 3 specific error conditions: - a null value for either the from or to value on an order range - non-numeric values in the from & to values - situations where the from ID is greater than the to ID   BONUS - Instead of using an interface tool to allow the user to set 6/12/96 box sizes, build a batch macro that will package all three scenarios at once! DOUBLE BONUS - Complete this challenge whilst listening to some UB40 ... View more

A solution to last week’s challenge can be found here.    This week's challenge was submitted by @wwatson  - Thanks for your submission!   Two sets of performance data are provided below. Plot that data on bar charts, calculate a trendline for each graph, and export the charts to a single Powerpoint slide.   The method for calculating the trend line can be found on various websites. The author used this one. ... View more

A solution to last week's challenge can be found here.   Use Designer Desktop or Designer Cloud, Trifacta Classic to solve this week's challenge.   Are you ready to showcase your analytical skills and uncover valuable insights from the exciting world of international women’s football (or soccer as it is called in the United States)? This Weekly Challenge will put your data manipulation and analysis prowess to the test.    We will be using data gathered from over 4,000 women’s football match results from around the world.   Your challenge is to create a league table, for only the FIFA World Cup tournaments, that pulls together a number of statistics for each team: Games Played (GP), Wins, Draws, Losses, Goals For (GF), Goals Against (GA), Win %, and Average Goals scored per game. From this table, you will be able to answer three intriguing questions:     Which team has the best winning percentage at World Cup tournaments? Which team scores the most goals on average? Which team has the worst defense; that is, the most goals conceded? Using the shootouts.csv dataset, we would also like you to answer this question on penalty shootouts:   Which team has the best penalty shootout performance?     Source: Kaggle Women's International Football Results   ... View more

The link to last week’s challenge (exercise #52) is HERE   This week Challenge is to perform some parsing of report formatted data to turn it into a table.  I have seen several solutions but keep in mind that you will want to make your solution as dynamic as possible.  My solution that I will post next week uses a batch macro but I look forward to seeing how you solve the problem.  Have fun and remember there is usually more than one solution.   Use case:  A company has a system that produces data that is not formatted into columns and rows. In this case, each row is separated by a line of dashes and each field is delimited by a |.    Objective:  You goal is to create a process that will dynamically transform the data into a series of columns and rows keeping in mind that the number of columns and rows may vary on a case by case basis. ... View more

Hi Maveryx,   A solution to last week’s challenge can be found here.    In April, we celebrate Earth Month, a time dedicated to raising awareness and taking action for environmental conservation and sustainability.   This weekly challenge delves into temperatures, highlighting their crucial role in our planet's health. The dataset presents comprehensive information on global temperature records, covering various countries worldwide. It includes average temperature records in Celsius for major cities from 1743 to 2013. To solve this challenge, we will be concentrating on the data from 1950 onwards.   Your tasks are as follows: Determine which cities have average temperatures greater than or equal to 25 degrees. Among the cities identified in the previous task, identify the country with the highest number of such cities. Examining all countries within the dataset, pinpoint the year with the highest average temperature and the year with the lowest average temperature across the globe.   Need a refresher? Review these lessons in Academy to gear up: Sorting Data Separating Data into Columns and Rows Summarizing Data Source: https://www.kaggle.com/datasets/maso0dahmed/global-temperature-records-1850-2022   Good luck!   ... View more