Alteryx Academy

A solution to last week’s challenge can be found here.  Source: https://www.dailymail.co.uk/news/article-4965690/Humpback-whale-breaches-Sydney-Harbour-sunset.html   Every year whales around the world migrate from their feeding grounds to their breeding grounds. Some species travel an outstanding 12,000 miles round trip!   Use the data sets below to analyze migration movements for different whale species. The file Whale_Migration_Data contains a list of whales whose migration patterns have been tracked from 2001-2003, and the total kilometers each whale swam on each day of their migration. The file Whale_Pods contains a list of whale ids and pod ids, which indicates the pod each whale belongs to. The file Whale_Species contains information about the whale species of each pod.   Find the total number of kilometers each pod swam for migration for each year, then find the average number of kilometers each whale in that pod swam (total kilometers / number of whales in the pod). Then, find the pod with the highest average kilometers swam per whale for each species for each year. ... 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

A solution to last week's challenge can be found here!   At Alteryx, we have a quarterly event called "Innovation Days".   As a previous project, @ErikB  implemented a Pig-Latin converter.     Etlay's ytray isthay oneyay orfay ourselvesyay! ( Let's try this one for ourselves!)   Oinkyay oinkyay!       ... View more

A solution to last week's Challenge can be found here!   Love...it's not the only thing in the air this week!  The Environmental Protection Agency (EPA) measures the concentrations of certain pollutants, like ozone (O 3 ),  and assigns an Air Quality Index (AQI) value to that concentration.  For each pollutant with measurements, calculate the number of days in the month of February assigned as each category on the EPA's Air Quality Index.    For this Challenge, I chose to work with data recorded in Denver, CO.  The dataset provided includes cities from all over the US; feel free to "choose your own adventure" and select a city that interests you!  Design the workflow to be as dynamic as possible; imagine that someone will run this workflow every month.  What can you do to make sure that the results reflect the month in which the workflow is run?    ... View more