Data Science

Machine learning & data science for beginners and experts alike.
SusanCS
Alteryx Community Team
Alteryx Community Team

Beta, Pearson, Spearman, macros and more — last month’s Data Science Portal discussions covered a lot of territory! Here are three interesting conversations that happened in August. (And it’s not too late to revisit July’s highlights, if you missed them!)



Calculating Beta with Alteryx

@Joker_Hazard needed a method to calculate beta using Alteryx, based on covariance and variance. This metric is often used to assess a stock’s volatility relative to the entire market. @atcodedog05 and @apathetichell jumped in to help with tips, plus a workflow that can easily handle the 1,500 companies @Joker_Hazard wants to check out. 



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 Better calculate beta. Image via GIPHY

 

 

 

Updating Parameters for New Data

Two interesting conversations revolved around how to make a tool more adaptable for a specific analysis. First, @Rohit_Karvekar asked how to adjust the preset minimum value for support in the MB Rules Tool@danilang and @apathetichell offered suggestions for altering the R code underlying the tool for this purpose. 

 

More recently, @neilgallen@phottovy@danilang and @mceleavey have been discussing how to dynamically adjust the parameters of the Find Nearest Neighbors Tool. This challenge has yet to be solved, so hop in if you have ideas!



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 Yes, that is who you think it is in blue. Image via GIPHY

 

 

 

These questions are a great reminder that there’s more to the R-based predictive tools than meets the eye. You can tinker with the code within them, and also obtain the models they generate to use elsewhere.



The Perfect Correlation

Finally, @suresh_abeyweera asked a great question about using the Pearson Correlation Tool as part of developing a recommendation engine, and wanting to group data by users to calculate correlations among ratings. @phottovy pointed out that the Spearman Correlation Tool has a built-in group by option, but the Pearson tool doesn’t — then went the extra mile and shared a batch macro that groups and then calculates the Pearson correlations. Amazing! (Also, if you’d like a refresher on Pearson vs. Spearman correlations, here’s a useful guide.)

 

Thanks to everyone who participated in these great discussions! Also, be sure to stop by the Data Science Mixer podcast pages for our Cocktail Conversations. In August, we talked on the podcast about the role of data science in the grocery supply chain and in human resources — if you eat food and/or work, I promise you’ll find these episodes compelling. Cheers!



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Image via GIPHY 

 


Blog teaser photo by Mike Kononov on Unsplash

Susan Currie Sivek
Senior Data Science Journalist

Susan Currie Sivek, Ph.D., is the data science journalist for the Alteryx Community. She explores data science concepts with a global audience through blog posts and the Data Science Mixer podcast. Her background in academia and social science informs her approach to investigating data and communicating complex ideas — with a dash of creativity from her training in journalism. Susan also loves getting outdoors with her dog and relaxing with some good science fiction. Twitter: @susansivek

Susan Currie Sivek, Ph.D., is the data science journalist for the Alteryx Community. She explores data science concepts with a global audience through blog posts and the Data Science Mixer podcast. Her background in academia and social science informs her approach to investigating data and communicating complex ideas — with a dash of creativity from her training in journalism. Susan also loves getting outdoors with her dog and relaxing with some good science fiction. Twitter: @susansivek

Comments
Joker_Hazard
8 - Asteroid

I was not expecting this. Thanks for the post 🙂