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Here is a new challenge for this week. The link to the solution for last week’s challenge is HERE.
The use case:
We received some text data and that includes an embedded line-feed character.
The objective is to remove the new line character, convert the date-time string to a date-time formatted field and then do some renaming per the sample output.
Good luck, I look forward to your feedback.
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Hopefully everyone had fun with the Grand Prix challenges and a few of you are ready to jump in the ring next year for the 2017 Grand Prix at Inspire in Las Vegas. The solution for the final lap (challenge #32) is HERE.
Let’s dial it back this week and look another real world example of using Alteryx to reshape data into a usable format for analysis.
Use Case: A radio station is trying to analyze data they receive from Nielsen disclosing the number of listeners the station has on a weekly basis by program. The challenge is that the data is formatted in a way that makes it challenging to use for analytics.
Objective: Reshape the data detailing the listening stats for the 30 programs listed in the data.
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For the fourth challenge let’s look at parsing Dates from text strings. To view the previous challenge, click HERE.
A dataset contains a text field that has a date embedded within the text. The problem is that the date is represented a few different ways. For example:
16-APR-2005
Nov•16,•1900
4-SEP-00
Jan•5•2000
The goal is to create a new Date/Time field populated with the dates contained within the text field. You will also need to standardize the dates so that they are all formatted the same.
We have listed this as an advanced exercise since parsing out the dates can be challenging depending on the technique you employ to do it. As always, we love to hear your comments. Have fun!
UPDATE 12/7/2015:
The solution has been uploaded
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Hi Maveryx,
We posted the solution JSON file to Cloud Quest #5. Check it out and let us know what you think! Send suggestions to academy@alteryx.com or leave a comment below.
Let’s dive into this week's quest!
Download the provided JSON start file and upload it into your Analytics Cloud library. For more detailed instructions on how to import and export Designer Cloud workflow files, check out the pinned article .
Scenario:
This week’s quest revolves around data provided about standardized math exams administered in New York City Public Schools from 2013 to 2019. Download the provided district_math_results.csv dataset and reconnect it in your starting workflow file. Ensure that the Interpret Column Datatypes checkbox is not selected in the Input Data tool options. The dataset includes details on the district, grade, year, category (male/female); the number of students who took the test; and the percentage of students who achieved each level, with Level 4 being the highest.
You have two tasks:
Calculate the change, by district, in the percentage of 8th grade female students who achieved a Level 4 score in 2019 compared to 2013.
Identify the top three districts that showed the most significant improvement in the Level 4 percentage over the same period.
Hint: Configure the Cross Tab tool to create a new column based on the Year field, labeling the columns as Year 2013 and Year 2019. The Level 4 percentage should serve as the value for these columns, using the Use First Value method. Ensure the data is grouped by District.
A combination of the Sample, Filter, Cross Tab, Formula, and Select tools should solve your problem, but not necessarily in this sequence.
A combination of the Sample, Filter, Cross Tab, Formula, and Select 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 workflow screenshot as attachments to your comment.
Here’s to a successful quest!
Source: https://infohub.nyced.org/docs/default-source/default-document-library/2014-15-to-2022-23-nyc-regents-overall-and-by-category---public.xlsx
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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!
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