474 episodes
Data Skeptic Kyle Polich
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- Technology
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4.6 • 59 Ratings
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The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
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The Panel Study of Income Dynamics
Noura Insolera, a Research Investigator with the Panel Study of Income Dynamics (PSID), joins us to share how PSID conducts longitudinal household surveys. She also shared some interesting findings from their data exploration, particularly on the observation and trends in food insecurity.
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Survey Design Working Session
Susan Gerbic joins Kyle to review some of the surveys Data Skeptic has launch, draft a new survey about podcast listening habits, and then review the results of that survey. You can see those results at the link below.
https://survey.dataskeptic.com/survey/result/1675102237053
Watch the videos Susan mentioned on her Youtube page at the link below.
https://www.youtube.com/playlist?list=PL7VAuaQDhPTVaLeI1IcpYph5lH19xA1u4 -
Bot Detection and Dyadic Surveys
The use of social bots to fill out online surveys is becoming prevalent. Today, we speak with Sara Bybee, a postdoctoral research scholar at the University of Utah. Sara shares from her research, how she detected social bots, the strategies to curb them, and how underrepresented groups can be more represented in surveys.
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Reproducible ESP Testing
Our guest today is Zoltán Kekecs, a Ph.D. holder in Behavioural Science. Zoltán highlights the problem of low replicability in journal papers and illustrates how researchers can better ensure complete replication of their research and findings. He used Bem’s experiment as an example, extensively talking about his methodology and results.
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A Survey of Data Science Methodologies
On the show, Iñigo Martinez, a Ph.D. student at the University of Navarra shares his survey results which investigated how data practitioners perform data science projects. He revealed the methodologies typically used by data practitioners and the success factors in data science projects.
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Opinion Dynamics Models
On the show today, Dino Carpentras, a post-doctoral researcher at the Computational Social Science group at ETH Zürich joins us to discuss how opinion dynamics models are built and validated. He explained how quantifying opinions is complex, and strategies to develop robust models for measuring and predicting public opinions.
Customer Reviews
Crypto entertainment
I found the show as a result of a google search, I started listening as a way of building on my developing interest in data science and my hope to learn more about deep learning.
Generally I find that podcasts assume the listener has been there from the beginning and so to make sure I didn’t miss out on any important topics, I found the first episode and started listening.
At the point of writing this review, I’m still working my way through the back catalogue, I’m somewhere in 2015, in fact my review title is based on the crypto zoology episode which was pure entertainment.
Kyle, Lin Da and Yoshi are fantastic hosts. I’ve definitely learned a few things so far, I especially like the bigger sized mini episodes that explain really cool things like k-means clustering in really simple ways, just enough to give you an understanding and help you go off and do a bit more work on your own.
One thing to be aware of, with both Kyle and several of his guests, you will find many references to really great books, I’m spending a small fortune on reading material as a result 😁
Really enjoy the show, the format and the hosts. Highly recommended!
Incredulous
What's the sentiment ? I got a pointer for this podcast
from a data scientist in London. The only Podcast I rate
anywhere near this level is TWIST. I can't belive it's free !
Love this show
I am getting into data science and I really like the way this show tackles topics in ML and stats. I absolutely loved the Library Problem show because it was so cool to see a real life example worked all the way though and hear about how a data scientist might decide what to use (all these methods and ideas we'd heard about in pervious shows). If it's not giving away too many of your interview secrets, I'd love to hear more like the Library Problem.
This is an excellent show for anyone wanting to learn more about data science.
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