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The link to the solution for last challenge #37 is HERE.
One of the powers of Alteryx is to be able to batch processes without the need to write scripts of use complicated code. A single output tool can be configured to generate many output files.
Use Case: A company needs to blend data from three sources and generate an output file for each product - region combination, a total of 15 output files.
Objective: Create a cross join between the Product Group, Region Reference and Data tables to produce 15 unique CSV Data files. Please note that only 1 output tool should be leveraged in your solution.
Thanks to all that are playing along!
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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.
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Hi Maveryx,
A solution to last week’s challenge can be found here.
Are you already missing Inspire 2024? If you did not have the chance to attend the conference, the next three challenges will give you a taste of what our competitors faced during the incredible Grand Prix event, which celebrated its 15th anniversary this year. The year 2024 also marks 30 years since the tragic passing of Formula 1 driver Ayrton Senna.
A huge congratulations to Molly Harras on winning the 2024 Alteryx Grand Prix! If you are not aware, Molly won the Grand Prix for two years in a row: 2023 and 2024. Her incredible skill and dedication have shone through!
The Grand Prix challenges are divided into three laps:
Lap 1: Data Blending and Preparation – June 10
Lap 2: Spatial Analysis – June 17
Lap 3: Predictive Analysis – June 24
Pit stops during a race can make or break a team. Using the provided datasets from three different races (Qatar, Silverstone, and Japan), identify the driver who executed the fastest individual pit stop, measured from entering to exiting the pit lane.
The solution should consist of a single row containing the following data points: [Driver], [DriverNumber], [Race Name], and [PitStopTime] (in seconds).
Note: Truncate times to the second before you determine the pit stop duration.
Feel free to use the hints provided in the workflow.
Need a refresher? Review these lessons in Academy to gear up:
Multi-Row Formula
Separating Data into Columns and Rows
Good luck!
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Hi Maveryx,
A solution to last week’s challenge can be found here.
This challenge was submitted by Ramesh Neelamana and has been modified to align with our learning objectives. Thank you, Ramesh, for contributing to this excellent challenge!
A bookstore wants to analyze book sales over a specified period to determine whether sales are increasing, decreasing, or remaining stable.
Your task is to investigate book sales for each 6-month period ending in March, April, and May of 2024. For example, when analyzing March, include the sales for March plus the preceding 5 months. The March 2024 analysis should include sales from October 1, 2023, to March 31, 2024, and similarly for April and May.
Calculate the total number of books sold for each analyzed period and identify the most popular book sold during that period.
For this task, you are provided with two datasets: one containing book sales data from 2021 to 2024, and the other containing the specific periods of time that require analysis.
Need a refresher? Review these lessons in Academy to gear up:
Joining Data
Writing Conditional Statements
Good luck!
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Hello Maveryx,
After a two-week hiatus from our weekly challenges, while we all attended Inspire, we're back to our regular routine! I hope you enjoyed your break!
A solution to last week’s challenge can be found here.
This challenge was created by @Qiu and inspired by a question posted in our Community. Qiu, your contributions are priceless, and we cannot thank you enough!
The dataset contains a single column with the total monthly allowance for each person in a group of five teenagers. In an effort to budget their money, they want to determine how much, if anything, they would have left over if they budgeted $150 of spending each week for the first four weeks of the month.
Your task is to allocate the money by dividing each teenager’s total amount over five columns representing each week of the month plus a carryover week (Week 1, Week 2, Week 3, Week 4, Carry_Forward), considering their weekly spending limit [$150]). Any remaining funds after the first four weeks should be rolled over into the Carry Forward column.
Need a refresher? Review these lessons in Academy to gear up:
Multi-Row Formula
Writing Conditional Statements
Good luck!
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