361 episodes

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.

Data Skeptic Kyle Polich

    • Science
    • 4.6 • 54 Ratings

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.

    Flesch Kincaid Readability Tests

    Flesch Kincaid Readability Tests

    Given a document in English, how can you estimate the ease with which someone will find they can read it?  Does it require a college-level of reading comprehension or is it something a much younger student could read and understand?
    While these questions are useful to ask, they don't admit a simple answer.  One option is to use one of the (essentially identical) two Flesch Kincaid Readability Tests.  These are simple calculations which provide you with a rough estimate of the reading ease.
    In this episode, Kyle shares his thoughts on this tool and when it could be appropriate to use as part of your feature engineering pipeline towards a machine learning objective.
    For empirical validation of these metrics, the plot below compares English language Wikipedia pages with "Simple English" Wikipedia pages.  The analysis Kyle describes in this episode yields the intuitively pleasing histogram below.  It summarizes the distribution of Flesch reading ease scores for 1000 pages examined from both Wikipedias.
     

    Social Networks

    Social Networks

    Mayank Kejriwal, Research Professor at the University of Southern California and Researcher at the Information Sciences Institute, joins us today to discuss his work and his new book Knowledge, Graphs, Fundamentals, Techniques and Applications by Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekley.
    Works Mentioned
    “Knowledge, Graphs, Fundamentals, Techniques and Applications”by Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekley

    The QAnon Conspiracy

    The QAnon Conspiracy

    QAnon is a conspiracy theory born in the underbelly of the internet.  While easy to disprove, these cryptic ideas captured the minds of many people and (in part) paved the way to the 2021 storming of the US Capital.
    This is a contemporary conspiracy which came into existence and grew in a very digital way.  This makes it possible for researchers to study this phenomenon in a way not accessible in previous conspiracy theories of similar popularity.
    This episode is not so much a debunking of this debunked theory, but rather an exploration of the metadata and origins of this conspiracy.
    This episode is also the first in our 2021 Pilot Season in which we are going to test out a few formats for Data Skeptic to see what our next season should be.  This is the first installment.  In a few weeks, we're going to ask everyone to vote for their favorite theme for our next season.
     

    Benchmarking Vision on Edge vs Cloud

    Benchmarking Vision on Edge vs Cloud

    Karthick Shankar, Masters Student at Carnegie Mellon University, and Somali Chaterji, Assistant Professor at Purdue University, join us today to discuss the paper "JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads"
    Works Mentioned:
    https://ieeexplore.ieee.org/abstract/document/9284314
    “JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads.”
    by: Karthick Shankar, Pengcheng Wang, Ran Xu, Ashraf Mahgoub, Somali ChaterjiSocial Media
    Karthick Shankar
    https://twitter.com/karthick_sh
    Somali Chaterji
    https://twitter.com/somalichaterji?lang=en
    https://schaterji.io/

Customer Reviews

4.6 out of 5
54 Ratings

54 Ratings

K800Dave ,

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!

Iain W UK Twist Fan ,

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 !

Neenbeans ,

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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