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A solution to last week’s challenge can be found here.
John has decided that it's time to adopt a dog. However, he has never owned a pet before. He is having trouble deciding on which furry friend would be best.
Your challenge is to help John make a choice.
You will use three datasets that contain all the information needed for the research. The ranking for each criterion goes from 1 (low/not good) to 5 (high/excellent).
John wants a dog that would suit his lifestyle best, and here are the most important criteria. A small dog that will adapt easily to living in an apartment and doesn't bark too much, as neighbors would complain if barking is excessive. Since John is a novice owner, the dog needs to be easy to train and friendly with strangers.
Use these five criteria to select three dogs. Then create a report showing your findings and help John make his decision.
Following our users' comments, this Weekly Challenge has been updated. Thank you for your participation.
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Hi Maveryx,
We posted the solution JSON file to Cloud Quest #8. Check it out and let us know what you think! Send suggestions to [email protected] or leave a comment below!
Let’s dive into this week's quest!
Upload the provided Cloud Quest 9 - Start File.json file into your Analytics Cloud library.
The necessary datasets are pre-populated in the Text Input tools.
For more detailed instructions on how to import and export Designer Cloud workflow files, check out the pinned article Cloud Quest Submission Process Update.
Scenario:
In this week’s quest, a general contractor needs your assistance assessing his business using date-time analysis.
Bob, who had been working as a home builder, decided to transition his business towards leasing construction equipment. Among other equipment, he has two cranes he has been leasing for a while. He wants to get a better idea how his crane leasing business has been going.
Create a workflow that will show how many days both of his cranes have been leased at the same time during the years 2016-2018, the only years where he has a full set of data for both cranes.
Hint: In Designer Desktop, the Generate Rows tool would be the most efficient way to tokenize records within a date range. Unfortunately, the Generate Rows tool is not yet available in Designer Cloud (coming soon in 2024!), but you can still solve this quest thinking outside the box with a cartesian join from the Append Columns tool.
A combination of the Summarize, Filter, Append Columns, Formula, and Sample tools should solve your problem, but not necessarily in this sequence.
If you find yourself struggling with any of the tasks, feel free to explore these interactive lessons in the Maveryx Academy for guidance:
Getting Started with Designer Cloud
Building Connections in Designer Cloud
Building Your Workflow in Designer Cloud
Once you have completed your quest, go back to your Analytics Cloud library.
Download your workflow solution file.
Include your JSON file and a screenshot of your workflow as attachments to your comment.
Here’s to a successful quest!
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Hi Maveryx,
A solution to last week’s challenge can be found here.
This week's challenge, contributed by our Community member AntoBennetsha Jabamalai (@AntobennetshaJ), will sharpen your data preparation skills. A big thanks to Anto!
Who among us has not encountered the task of handling dates in various formats in our daily work? But let’s be honest: who regularly checks the date format before streamlining their processes?
In this challenge, we are faced with the task of cleaning a set of dates in a survey dataset. The date was collected in a string field, which meant that survey respondents entered the dates in different formats. This resulted in dates having different delimiters and additional, unnecessary information. For practical analysis of the survey data, it is crucial to standardize all dates into a consistent format (YYYY-MM-DD) and organize them from the oldest to the most recent.
Need a bit of guidance? The interactive lesson Separating Data into Columns and Rows in Academy offers insights on handling values like those in our dataset, which include multiple separators such as slashes, hyphens, and dots, as well as other superfluous characters.
Good luck!
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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, Team Maveryx!
A solution to last week’s challenge can be found here.
It is that time again—the Superbowl is just around the corner! To get ready, grab your favorite snacks, call over some friends, and dive into some football stats. A big shoutout to Kenda Sanderson (@kenda) for bringing us this fantastic challenge!
The Superbowl is about the best of the best! You will use the provided Fantasy Football dataset to determine which positions typically have the best rank and how that has changed over time.
Complete the following tasks to solve this challenge:
Determine the five positions (POS) with the lowest overall rank (RK) for all years.
Create a line graph and a table that clearly show the fluctuation in rank over time, specifically for those five positions.
Let's discover what fascinating insights you can extract from this challenge!
If you need a little help, you can review these lessons in Academy:
Connecting to Multiple Sheets at Once
Changing Data Layouts
Multi-Row Formula
Reporting in Designer (all lessons)
Source: https://fantasydata.com/nfl/fantasy-football-leaders?season=2018&seasontype=1&scope=1&subscope=1&startweek=1&endweek=1&aggregatescope=1&range=1
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