Alteryx Academy

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. ... View more

A solution to last week’s challenge can be found here.   To solve this week’s challenge, use Designer Desktop or Designer Cloud. This challenge comes to us from @randall_king    Haven’t heard about Designer Cloud yet? Watch a demo. https://www.alteryx.com/products/designer-cloud-trifacta   Do you often find that you have to clean data to get it in a readable and usable format? In this weekly challenge, you need to clean data and organize it into a format that is more suitable for analysis. The dataset contains information about the Olympic Games: 1. Participating Country 2. Country Code 3. Total Summer Games 4. Total Gold Medals Summer 5. Total Silver Medals Summer 6. Total Bronze Medals Summer 7. Total Medals Combined Summer 8. Total Winter Games 9. Total Gold Medals Winter 10. Total Silver Medals Winter 11. Total Bronze Medals Winter 12. Total Medals Combined Winter 13. Total Combined Games 14. Total Gold Medals Combined Games 15. Total Silver Medals Combined Games 16. Total Bronze Medals Combined Games 17. Total All Medals Combined Games   However, the data is all compiled in one field.   Your goal is to: - Parse the data in columns. - Clean out the special characters for the Country Code (for example, update “(AFG)” to “AFG”). - Name the new columns (as shown above). Data Source: www.kaggle.com/datasets/rushikeshlavate/olympic-games-medal-datasetfrom-1896-to-2018   ... View more

A solution to last week's Challenge has been posted HERE!    This year's (2017-2018) flu season has been reported to be the most severe in the past decade.  In particular, influenza-related hospitalizations are on the rise across a wider range of age groups than normal.  For this week's Challenge, we'll use data from the Center for Disease Control (CDC) concerning California's data on influenza-related hospitalizations.  Create a table that shows the cumulative number of hospitalizations per age group for each flu season.   Looking for additional analyses to do?  How about a week-by-week comparison of hospitalizations?  Or perhaps a forecast of the number of expected hospitalizations for a particular age group?  Or maybe a visual representation of the data is up your alley!     Stay healthy out there!    ... View more

Hi everyone!   A solution to last week’s challenge can be found here.   In this weekly challenge, you will find a dataset containing the New York Times articles from January 1, 2020 to December 31, 2020.   Your first task is to identify which day of the week has the highest number of sports articles published.   Your second task is to identify how many times the word "Football" or "football" is in the headline or keywords on the day of the week that has the highest number of sports articles published.   As a final result, you have to write a statement similar to this: The word football appears ____ times in sports articles published on _____.   BONUS! Forecast in what future month articles will surpass the 20 million comments milestone using the high end of the 70% confidence interval. (HINT: Consider a cumulative sum for all posts since 2020)   For the bonus, you will need to Download and Install Predictive tools [https://help.alteryx.com/20213/designer/download-and-use-predictive-tools]. Also, don’t worry if your output it’s slightly different from the output here. What is important is to understand the results.   Good luck!! ... View more

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  #09 is HERE. For this challenge  let’s look at the date and time functions in Alteryx.   The use case: A distribution center receives a package. At the point of receipt, the package is scanned and a timestamp is captured for arrival date/time. The company is trying to reduce the amount of time the package is at its facility and as a result is trying to analyze how long packages remain at the facility.   The objective for this challenge  is to calculate the delta between arrival date/time and the Time_Now field (this field has the date/time of the creation of this challenge  and will be changing as time goes on). Create a unique field for Days, Hours, Minutes and Seconds.   We have listed this as an intermediate exercise   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 2/1/2016 You can find the solution below. ... View more