
The Python Podcast.__init__
A Friendly Approach To Regression Models For Programmers
Jan 2, 2022 · 45 min · Episode 346 · 36.0 MB
0:00-45:15
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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
- @MatthewBRudd on Twitter
Picks
- Tobias
- The Argument podcast from the NY Times
- Matthew
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.
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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
pythonpodcast.com/linode
pythonpodcast.comLinkedIn
linkedin.com@MatthewBRudd
twitter.comThe Argument
nytimes.comPrimus
primusville.comClaypool Lennon Delirium
theclaypoollennondelirium.comSouth of Reality
shop.atorecords.comRegression: A Friendly Guide
manning.comSewanee University of the South
new.sewanee.eduSewanee Data Lab
new.sewanee.eduMark Lutz Python books
learning-python.comElements of Statistical Learning
hastie.su.domainsLinear Regression
en.wikipedia.orgLogistic Regression
en.wikipedia.orgModeling Binary Data
routledge.comData Engineering Podcast
dataengineeringpodcast.comsite
pythonpodcast.comiTunes
itunes.apple.comThe Freak Fandango Orchestra
freemusicarchive.orgCC BY-SA
creativecommons.org
- 0:57Introduction to the Guest: Matthew Rudd
- 1:45Matthew's Journey with Python and R
- 4:23Writing a Book on Statistical Regression
- 6:54Applications and Use Cases of Regression
- 9:40Target Audience and Goals for the Book
- 13:22Common Pitfalls in Regression Modeling
- 16:38Statistical Models vs. Machine Learning Models
- 21:42Balancing Theory and Practice in the Book
- 25:08Challenges of Writing a Technical Book
- 31:10Interesting Applications of Regression Techniques
- 38:26Resources for Learning More About Regression