kindly advise and share the link

A solution to last week's challenge can be found here.

This data set provides information related to various clothes-size-prediction. Using this data, find the minimum and maximum weight, age, height per size and the percentage that each size represents rounding the numbers to 2 decimal places. If a line of data is incomplete remove it.

Hint: Nobody is under the age of 10 or over 100 years old.

Here is the new weekly challenge. The link to the solution for last challenge is HERE. For this challenge let’s look at ranking records when multiple records can have the same rank.

The objective is to determine the top 5 ranking based on the count, however since there are multiple rows with same count (similar to a round of golf) multiple people can be in the same place (Rank) if they have the same score.

We have listed this as a beginner challenge and I expect it will go very quickly for many of you. Let us know what you think, we are looking forward to hearing your feedback.

UPDATE 1/25/2016

The solution has been uploaded.

Hi all! I am reaching out because I need help with my workflow.

In summary, I perform a daily activity to calculate the average of last 30 days. (D-1 to D-30) However, when performing this activity on monday, I need Alteryx to copy paste the results of the last day column (in this case 2026_04_10) and create two extra columns with this data 2026_04_11 and 2026_04_12.

This should only happen during the weekends and on holidays.

A holiday example:

Lets suppose 2026_04_15 is a holiday. On 2026_04_16 I will perform the automation and it should generate 2026_04_14 and copy paste the results of 04_14 on 2026_04_15.

On both examples, if Alteryx copy pasted values due to holiday or weekends, it should remove columns in the beginning to make it only 30day interval (in the aforementioned weekend example 2026_04_11 and 2026_04_12)

Is there a way to do this? I'm completely stuck. I have no issues on creating an extra table with holiday dates to use as a reference in the automation, but I cant think of a formula to do this..

Thanks

Last week's challenge can be found HERE!

You have been given some order information that contains a list of orders with SKUs and related quantities. Additionally, you have a data set with the total inventory for each SKU.

You have been tasked with figuring out how to consume all of the inventory with the least amount of orders. In other words, you have been asked to fill the largest orders for each SKU in descending order.

Additionally, only complete orders may be fulfilled. For example, if an order calls for 4 units of a SKU, and only 3 are left in inventory, you can't fulfill the order.

The link to the solution for last challenge #35 is HERE. 

Use Case:  An analytical consulting company downloads medical journal publication data from the web and would like to extract all of the authors for the listed entries.

The text input contains details about each article where FAU indicates the author name for the article – in most case there are multiple authors. The details of each article are contained in lines that begin with PMID and end with an empty line.

Objective: Parse out each article PMID and list each author in sequential columns as seen in the Results.yxmd file.

Here is this week’s challenge, I would like to thank everyone for playing along and for your feedback.  The link to the solution for last challenge #33 is HERE.  For this chalenge let’s look at creating a macro from scratch.  I recommend you first solve the problem with tools on the canvas, then cake a macro from the tools.

Use Case:  Our customer has a need to convert date/time strings into a date-time format.

Examples of the different input formats include:  4/8/2015 4:00, 5/10/2015 13:00.  The conversion is automatic when the hours are 2 digits (10-24), but it ignores hours 1-9 (creates NULL values on conversion).

Objective:  Create a macro to effectively convert and preserve the data in a date-time format.

Hi, 

I have attached my dataset (an Alteryx output - already transformed data) where I have nominal codes in column A. For some of these nominals, I want to duplicate the rows for additional nominals that weren't in my original dataset, keeping the values in columns B-D the same, but updating column A for the additional nominal. 

For example, there are currently 81 rows for nominal 50220 - 27 x core, 27 x micro, 27 x indirect, (one for each month Dec23 - Feb26). I want to replicate these rows for nominals 50230 and 50210, each would have 27 rows for each business unit and the 'value' in column D for each month and corresponding business unit would be the same for each nominal. I'd therefore have an additional 162 rows of data. 

I know this is straightforward enough to do in excel for just these two, but I have several examples to add and need to do it on a monthly basis using an input that will never have the missing nominals.

How do I go about this? 

Many Thanks 

The link to last week’s challenge (challenge #21) is HERE.  

This week’s challenge is a little special.  I have mentioned before that as internal users or Alteryx we all do these exercises every week and we started doing this at the beginning of 2014.  There have been a lot of exercises since then and the product has changed a lot of the past few years.   The exercise posted here is exercise #1, the inaugural exercise that we completed internally.

In the spirit of that, you will find the workflow will build from top down instead of from left to right, that is the way Alteryx used to work.  For some more fun I would ask that before building the workflow, go to the “About” box from the HELP menu and double click the Colorado flag next to the text “Created in Boulder, Colorado”  after that, the tool icons will change to the original Alteryx tool icons that used to be in the product.  Don’t worry, once you close Alteryx and re-open it the icons will return to normal.

Now for the challenge , this week’s challenge is an intermediate difficulty one that requires parsing a text field to get at values to summarize. 

Use case:  The log files from an ATM machine have the transaction amounts embedded in text strings.  The user needs to have these text amounts summarized by row (transaction).

Objective: For this assignment the numbers directly following the text ‘ATM2.’ are dollar amounts for transactions. Summarize the values on a row by row basis.

Have fun!

The link to last week’s challenge (challenge #25) is HERE

This week’s challenge is a use case faced by one of our customers that can leverage the geo-spatial tools available in Alteryx.  If you have the spatial data available you can output your results as drive-distance, in the event that you do not have the drive-distance data available, please use the straight-line distance.  The basic tools and setup of the data needed will be identical.  Output results will be provided for both methods.  In the event that you use drive-distance and your results vary slightly from the sample output, it may be due to variance in the data set used for the exercise.

Use Case:  in order to audit their employee expense reports, a service company would like to calculate how far (in miles) their technician is traveling from their hotels, to the worksite, then to their destination hotel on a daily basis.

The Data:  The source is collected in a way that record 1 contains the spatial object for the beginning hotel for day 1, record 2 is the spatial object for the worksite for day 1, and record 3 is the spatial object for the ending hotel for day 1.  This pattern repeats for three successive days.

Find the distance on a daily basis the technician is either driving or straight line distance if you don’t have the spatial data available.

Enjoy!

Welcome to Week 75!! We have a few people vying for a new shiny badge!!

This challenge is based off the word game Scrabble. In the game of Scrabble, you are given 7 tiles with a letter printed on them. Additionally, a point value is assigned to the letters. The player tries to find the word that is worth the most points. For example, a player may draw the following 7 tiles: G (2 points), A (1 point), R (1 point), E (1 point), T (1 point), V (4 point). It is important to note that the longest word assembled is not always the best word – for example the word of ‘parget’ is worth less then ‘paver’.

The challenge this week is to find the best word (from a provided text file) that can be assembled from 7 provided letters. Use the workflow and the files attached to get to your solution.

Advanced Users and Challenge 75 Racers: The game of Scrabble also provides a max of 2 blank tiles. These tiles are a wildcard and can be used as any letter, but are worth 0 points. While it is unlikely that a single player has both blanks at a single time, we must be prepared for when it takes place. Try to find the highest scoring word using the blank tiles – but be sure that they are not scored!

A solution to last week's Challenge has been posted HERE!

I learned the worst thing to say to a bunch of football fans: "The Super Bowl?  It's just another football game".   The looks of horror on my colleagues' faces when I said that are burned into my memory forever.  And let me warn you: don't say that when you have anything important to do because you will be subject to an hour-long debate about player stats, offense/defense match-ups, the importance of turnovers (and not just the apple flavored ones) and the proper way to make chili. 

Anyway, after this debate, I did what any data nerd would do: I took to the internet in search of datasets and fired up my Alteryx Designer to answer this question: Is the Super Bowl just another game?  I decided that I'd do a little experiment.  Using the Predictive tools and data from the New England Patriots's 2016 and 2017 seasons, I wanted to see how a linear regression model developed on regular season games (including post-season) performed when used to predict the number of points the Patriots would score during the Super Bowl. 

First, I downloaded data (source: here) for the New England Patriots for the 2016 and 2016 seasons (provided as inputs in the Start File), which required a bit of parsing to prepare for later use.  Then, I set out on some data investigation to begin my linear regression model development.  My approach (which may not be the same one you use in your modeling approach)  was to choose the four (4) variables from the "Score", "Offense" and "Defense" data categories with the most significant relationship to the variable "TM", which indicates the number of points the Patriots scored.  With my variables selected, I began the model creation.  My approach (which may differ from yours😞 develop the model on on values except for one pair of regular season games and the Super Bowl games.

What's the difference between your predicted values and actual values for your regular season games and Super Bowl games?  Is the Super Bowl just another game? 

Extra Challenge: How'd you do on this Jeopardy Category?  Admittedly, I was in good company with these contestants!       

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.

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!

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.

A solution to last week’s challenge can be found here.

Last week's solution has been posted HERE.

A university analyst has been asked to help with course and classroom resource planning. The university sends course times in military time format. The university analyst wants the fields Military Begin Time and Military End Time to display in standard, 12-hour increments. So, if a course ends at 16:30, he would like it to show as 4:30. He also wants to know what the elapsed time is between the two fields mentioned above. How long is each course? Minutes will be the time unit measured.

A solution to last week’s challenge can be found here.

To solve this week’s challenge, use Designer Desktop.

Challenge Created by: @PhilipMannering 

Description:

Based on the data provided, your task this week is to find all possible words in the Spelling Bee puzzle sorted by length (descending). The Spelling Bee puzzle is a grid of seven letters. The rules for valid words are:

1. You must only use letters found in the grid.
2. You must use the center letter at least once in each word.
3. Words must contain at least four letters.
4. Letters can be used more than once.

The first input contains the grid of letters.

Hint: You will (quite literally) require ALTERYX to find the perfect pangram.

Designer Version Used to Create: 2020.3

The link to last week’s challenge (challenge #20) is HERE.  For this week’s challenge we have an easier one that requires the reformatting date fields.  The input data has very poorly formatted month and year information that will require you to build some logic to make it usable. 

Use Case: A company needs to reformat a month/year flag in a data asset they received for a consulting project.

You will need to take the Date field provided and separate it into fields for the Month and Year.  For example, F07 should become 2 columns where month is Feb and year is 07.

Have fun!

The link to last week’s challenge (challenge #19) is HERE.  For this week’s challenge we need to clean up an unformatted text file with unstructured data.

Use Case: A sales executive got a series of leads from the DMA conference. Unfortunately it’s a text file that we want to restructure into a tabular form to load into Salesforce.

The result should be a table formatted the same as the output sample.

As always there are many ways to approach the same problem in Alteryx.  I am looking forward hear about some of your solutions.

Enjoy!

A solution to last week’s challenge can be found here. Source: https://www.dailymail.co.uk/news/article-4965690/Humpback-whale-breaches-Sydney-Harbour-sunset.html

Every year whales around the world migrate from their feeding grounds to their breeding grounds. Some species travel an outstanding 12,000 miles round trip!

Use the data sets below to analyze migration movements for different whale species.

• The file Whale_Migration_Data contains a list of whales whose migration patterns have been tracked from 2001-2003, and the total kilometers each whale swam on each day of their migration.
• The file Whale_Pods contains a list of whale ids and pod ids, which indicates the pod each whale belongs to.
• The file Whale_Species contains information about the whale species of each pod.

Find the total number of kilometers each pod swam for migration for each year, then find the average number of kilometers each whale in that pod swam (total kilometers / number of whales in the pod). Then, find the pod with the highest average kilometers swam per whale for each species for each year.

A solution to last week’s challenge can be found here.

To solve this week’s challenge, use Designer Cloud or Designer Desktop.

Do you have to reformat lists because the last name and first name are listed in a different order?

In this weekly challenge, you need to rearrange an improperly formatted list of subscribers. Some of the subscribers’ names are listed with the last name(s) followed by a comma, and then the first name(s).

Your goal is to reorder the list with the first name(s) followed by the last name(s), and put the list in alphabetical order. For example, the subscriber “BORROEL GOMEZ, JUANA” should be listed as “JUANA BORROEL GOMEZ” with no punctuation.

Note that some subscribers can have more than one first name or last name.

Haven’t heard about Designer Cloud yet? Watch a demo. 

The solution to last week's challenge can be found HERE. 

Determine the items of clothing that have the highest average rating. In your analysis, include 1) only items of clothing that have at least 10 positive feedback reviews and 2) the five highest rated clothing items from each class.

The original data and its metadata can be found here. 

A solution to last week's Challenge has been posted here.   I loved seeing the variety of solutions that our Challengers came up with!  Admittedly, my solution probably has more tools than it should but it gets the job done...with only one Join tool!  In the spirit of friendly competition, especially with the self-imposed challenge of "how many Join tools does it take to calculate pumpkin production", I thought I'd give a shout out to our "winner".  Last week, our Challengers' solutions contained an average of 4 Join tool.  Our "winner" with the Fewest Join Tools used: @vishalgupta, with 0 Join tools!  You read that right...ZERO!  There are a few Find/Replace tools in there, though...but I'll take it!  Take the time to check out everyone's solutions; the variety is awesome, and I learned a few new tricks myself! 

This week's Challenge will give you the chance to show off your skills with the Predictive Tools to answer one of the most pressing issues of our time: Have we reached Peak Pumpkin?  Is the time of the demand for EVERYTHING PUMPKIN losing steam?  Has Maple Pecan become a force to be reckoned with?

For this week's Challenge, we have five input files, one for each branch of a store, with data on pumpkin product sales from 2010 to 2016.  Amalgamate the data into a single data stream to forecast the total expected sales for each product category for the year 2017.  

Notes:
- Any Null values should be filled with the average value of that product for that week, across all stores.
- You will need to install the Predictive tools to use the Time Series tools (if you don't have them installed already). 
- Choose the time series model whose Akaike Information Criterion, corrected (abbreviated by AICC) are consistently lower. The data from Store 1 is the best and most complete dataset to use for deciding on a model since it contains no Null values.
- The Predictive District on the Gallery has some tools and samples that you may find helpful for this exercise!

Notes for Users on versions 2018.2 and more recent: 

Changes to the version of R used in the Predictive tools have caused ARIMA calculations to yield different results than those in the original post.  Please reference the following instructions and start/solution files:

- Any Null values should be filled with the average value of that product for that week, across all stores. 
- You will need to install the Predictive tools to use the Time Series tools (if you don't have them installed already). 
- Perform this challenge using an ETS model to forecast values for the next year.  Leave all settings, aside from those needed to configure the model, to "Auto". 

- Expect your output to contain negative values

- Refer to the start file "challenge_94_2018_2_start_file.yxmd" and solution file "challenge_94_2018_2_solution.yxmd" that I posted to my reply to another Community user on page 3 of this Challenge's post. 

A solution to last week's Challenge can be found here! 

A big thanks to those of you that joined us last week at Inspire for the Weekly Challenge session!  It was so much fun solving with you all! 

This week, we're identifying the most popular baby names that were registered between the years of 1880 and 2017.  Given the provided dataset, determine the most popular names for Males and Females for each available year.  The column "Field_1" contains three concatenated values: the name, the associated gender (Male or Female) and the number of occurrences that the name appeared in birth records.   The column "FileName" contains the name of the file in which the record is found; the data was read in from a zip file that contained text files for each year (1880-2017) of records. 

A solution to last week's challenge can be found here! 

Using the data provided in the start file, create an ordered list of the provided unofficial holidays.  

GIPHY 

Get ready for Answer the Phone like Buddy the Elf Day on December 18! 

For this week’s challenge we can look at using Alteryx to automate a repetitive task.   The link to last week’s challenge (challenge #18) is HERE.

Use Case: Customer has 100's of xls files with 1 common sheet available in all workbooks. Through one process, the user would like to read across all of the xls files and return the values contained in specific cells - Row 2, Column 3 and Row 8, Column 2 for each sheet within each XLS workbook.

The result should be a table OR browse tool containing 3 columns: XLS File, Row2_ Column3, and Row8_Column2.

You will only have 2 xls files for this challenge, Book1 and Book2, but keep in mind that the use case is for 100s of Excel files with the same schema.  You won’t want to use 2 input tools since that would not scale to 100’s.  Also, for all data consumption, please check the box for First Row Contains Data.  This is because in the headers for an Excel file are in row #1.

Good luck and keep it simple, this should be an easy challenge!

A solution to last week’s challenge can be found here. Source: www.scantron.com/assessment-solutions/

Who said girls can't do math? It's 2020 and girls are thriving in many math and science areas, including data science. Just check out our Women of Analytics page!

In this week's challenge, we have the results from the New York City Public School standardized math exams from 2013 to 2019. Examining only 8th grade students, find the percentage of female students that achieved a Level 4 score for each district in the years 2013 and 2019. Then find the 3 districts that had the biggest improvement in this percentage.