The Python Podcast.__init__

A Friendly Approach To Regression Models For Programmers


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Summary

Statistical regression models are a staple of predictive forecasts in a wide range of applications. In this episode Matthew Rudd explains the various types of regression models, when to use them, and his work on the book "Regression: A Friendly Guide" to help programmers add regression techniques to their toolbox.

Announcements
  • Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
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  • Your host as usual is Tobias Macey and today I’m interviewing Matthew Rudd about the applications of statistical modeling and regression, and how to start using it for your work
  • Interview
    • Introductions
    • How did you get introduced to Python?
    • Can you start by describing some use cases for statistical regression?
    • What was your motivation for writing a book to explain this family of algorithms to programmers?
      • What are your goals for the book?
      • Who is the target audience?
      • What are some of the different categories of regression algorithms?
      • What are some heuristics for identifying which regression to use?
      • How have you approached the balance of using software principles for explaining the work of building the models with the mathematical underpinnings that make them work?
      • What are some of the concepts that are most challenging for people who are first working with regression models?
      • What are the most interesting, innovative, or unexpected ways that you have seen statistical regression models used?
      • What are the most interesting, unexpected, or challenging lessons that you have learned while working on your book?
      • What are some of the resources that you recommend for folks who want to learn more about the inner workings and applications of regression models after they finish your book?
      • Keep In Touch
        • LinkedIn
        • @MatthewBRudd on Twitter
        • Picks
          • Tobias
            • The Argument podcast from the NY Times
            • Matthew
              • Primus
              • Claypool Lennon Delirium
              • South of Reality
              • Links
                • Regression: A Friendly Guide
                • Sewanee University of the South
                • Sewanee Data Lab
                • Mark Lutz Python books
                • Elements of Statistical Learning
                • Linear Regression
                • Logistic Regression
                • Modeling Binary Data
                • Closing Announcements
                  • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
                  • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
                  • If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
                  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
                  • The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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                    The Python Podcast.__init__By Tobias Macey

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