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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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Join us for Part 6 of our 6 Part series on preparing for the Advanced Certification Exam.
Part I - Spatial Analytics in Alteryx
Part II - Building Macros
Part III - Building Apps
Part IV - Reporting/Publication
Part V - Advanced Prep/Parsing Concepts and Tools
Part VI - Recap and Exam Day Overview
You can download the data and the workflow below. Please, make sure you unzip the entire folder and extract all the files.
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By JoeMAlteryx Alumni (Retired)Videos01-09-201901:35 PM
Last week's solution can be found here.
Have you ever wanted to take a sneak peek at one of the questions on the Alteryx Designer Expert exam? We thought you might, so for the first time ever we’re releasing one of our retired Expert exam questions as a Weekly Challenge! The amazing @CristonS created this question and it made its debut at the first Expert exam in Anaheim last summer. This question gave everyone a hard time, and most people avoided it all together, so if it seems intimidating you’re not alone! We wanted to keep this in the same format as the actual exam question so you won’t see an output file, just an input. We’ll post the answer and our solution next week.
You are provided a dataset (Q2_variables.yxdb) that contains multiple variables. Select the ten (10) numeric variables with the highest Mean Decrease Gini coefficient from the variable importance plot. Use these variables to build a model to predict the target variable, [H0]. Compare two models: one based on all of the selected variables, and another that includes the selected variables except [F_38]. What is the effect of removing this variable [F_38] from the model? Provide the Chi-Sq effect as your answer.
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Hi Maveryx,
A solution to last week’s challenge can be found here.
This challenge was submitted by @AkimasaKajitani . Thanks Aki for this great contribution!
You and your friends enjoy high-energy songs. Since your friends know you are skilled at using Alteryx, they have asked you to create a 60-minute playlist using the Optimization tool. This playlist should contain the most popular and energetic songs, each included only once.
Each record in the provided dataset contains the variable (song ID), song name, popularity, level of energy (ranging from 0 to 1, with 0 being less energetic and 1 being more energetic), and song duration. Additionally, you are given the formatted for the B anchor of the Optimization tool. This represents the maximum duration of 3600 seconds, or 60 minutes (rhs).
Your task is to create a maximum 60-minute playlist that maximizes song popularity from the top 100 high-energy songs. Each song should only be included once. Additionally, output the total popularity value of (Objective ).
Summary of Data:
You are provided with a file containing a song popularity dataset with energy values for each song and its duration. Additionally, you are given the formatted input for the B anchor or the Optimization tool.
Hints:
Ensure you select Maximize Objective in the Optimization tool settings and spend most of your time structuring the data for the Specify the Model as Matrices option. You will not need to change any other settings in the tool (other than Maximize Objective).
Field names matter! Check out this article, which features data format.
song_popularity = coefficient
lb and ub values are a binary datatype of 0,1
Need a refresher? Review these resources to gear up:
Tool Mastery | Optimization
Article: Beginners Guide To Alteryx Optimization
Help Documentation: Optimization Tool
Source: The dataset was modified to align with the learning objectives of the challenge. https://www.kaggle.com/datasets/maharshipandya/-spotify-tracks-dataset
Good luck!
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A solution to last week’s challenge can be found here.
When humans deal with numbers, decimal number (Base10) is common. But we can also represent values in other bases. For example, a hexadecimal number is in Base 16; one use of this system is to represent HTML color codes. Computers also use the binary (Base2) and hexadecimal number systems extensively!
Your task is to create a Base Converter by making an analytic application. Your app will convert the given decimal numbers into the specified base ‘n’ numbers. You have to configure the application interface so that user can specify the base ‘n’ (‘n’ must be set to integer between 2 and 16).
Note that number exceeding 10 is usually expressed in alphabet( 10->a, 11->b,,,). Please use the provided Integer to letter table to convert your answer to the correct format.
An example output is provided for Base 16.
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