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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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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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A solution to last week's challenge can be found here.
This week's challenge focuses on a group of relatives and their birthdays. Of the six family members, we have one person's date of birth and a series of clues to calculate the others. Provide the DOB for all family members and the day of the week s/he was born.
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We hope you enjoyed last week's challenge. The solution has been posted here. For the second challenge lets look at removing characters and splitting data into columns based on delimiters.
Many products will export textual data with delimiters such as quotes. This is done so that strings can contain delimiters or control characters within them. Having more than one type of delimiter can be hard for ETL programs to interpret. In the input text file, there are two different delimiters (double quotes, single quotes) and they surround different data types.
Use Alteryx to strip out the delimiters as superfluous and format the data as represented in the output.
You may notice that we have started classifying the exercises into beginner, Intermediate and advanced. This classification is used by Alteryx internally to sequence exercises as users advance.
Update 11/23/2015:
The solution has been uploaded.
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