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Rank #101 in Science category

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Data Skeptic

Updated 4 days ago

Rank #101 in Science category

Education
Technology
Courses
Science
Read more

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.

Read more

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.

iTunes Ratings

394 Ratings
Average Ratings
273
78
17
18
8

The “banter” is truly awful

By fizzbuzzbot - Jul 12 2019
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The main podcast is good. But, I have to skip the episodes where he talks to the female co-host as if she’s an idiot while she asks ridiculous questions and makes insipid comments. Honestly, she’s frequently repeating back something simple that he said as if it’s a deep mystery of the universe. i.e. Female co-host: “computer?” (Sounding utterly puzzled) Male host: “Yes, a computer” (launches into 10 minute explanation as if to a child). Then, often she will laugh or sigh or giggle for no apparent reason. I’ve wondered if she’s actually high when she talk so slow and giggles at random. If she’s not and this is scripted, pretty demeaning.

Good. Could be great.

By Peaceful bird - Jul 02 2019
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This show is very good, but would be great if the co-host was more science-minded. As it stands, the mini episodes consist of Kyle explaining technical concepts to Linh Da, who is intended to be the layperson and prevent Kyle from getting too jargon-y. She is effective in that capacity. However, quite a bit of time gets wasted with arguments that would mostly not occur if Kyle were speaking to, say, a trained biologist, or even an attorney. Because it gets pretty annoying, I have to keep my listening of the show fairly sparse. Great show overall, and the deeper dives with guests are killer.

iTunes Ratings

394 Ratings
Average Ratings
273
78
17
18
8

The “banter” is truly awful

By fizzbuzzbot - Jul 12 2019
Read more
The main podcast is good. But, I have to skip the episodes where he talks to the female co-host as if she’s an idiot while she asks ridiculous questions and makes insipid comments. Honestly, she’s frequently repeating back something simple that he said as if it’s a deep mystery of the universe. i.e. Female co-host: “computer?” (Sounding utterly puzzled) Male host: “Yes, a computer” (launches into 10 minute explanation as if to a child). Then, often she will laugh or sigh or giggle for no apparent reason. I’ve wondered if she’s actually high when she talk so slow and giggles at random. If she’s not and this is scripted, pretty demeaning.

Good. Could be great.

By Peaceful bird - Jul 02 2019
Read more
This show is very good, but would be great if the co-host was more science-minded. As it stands, the mini episodes consist of Kyle explaining technical concepts to Linh Da, who is intended to be the layperson and prevent Kyle from getting too jargon-y. She is effective in that capacity. However, quite a bit of time gets wasted with arguments that would mostly not occur if Kyle were speaking to, say, a trained biologist, or even an attorney. Because it gets pretty annoying, I have to keep my listening of the show fairly sparse. Great show overall, and the deeper dives with guests are killer.
Cover image of Data Skeptic

Data Skeptic

Updated 4 days ago

Rank #101 in Science category

Read more

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.

Rank #1: [MINI] Multiple Regression

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This episode is a discussion of multiple regression: the use of observations that are a vector of values to predict a response variable. For this episode, we consider how features of a home such as the number of bedrooms, number of bathrooms, and square footage can predict the sale price.

Unlike a typical episode of Data Skeptic, these show notes are not just supporting material, but are actually featured in the episode.

The site Redfin gratiously allows users to download a CSV of results they are viewing. Unfortunately, they limit this extract to 500 listings, but you can still use it to try the same approach on your own using the download link shown in the figure below.

Feb 19 2016
18 mins
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Rank #2: Quantum Computing

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In this week's episode, Scott Aaronson, a professor at the University of Texas at Austin, explains what a quantum computer is, various possible applications, the types of problems they are good at solving and much more. Kyle and Scott have a lively discussion about the capabilities and limits of quantum computers and computational complexity.

Dec 01 2017
47 mins
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Rank #3: Being Bayesian

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This episode explores the root concept of what it is to be Bayesian: describing knowledge of a system probabilistically, having an appropriate prior probability, know how to weigh new evidence, and following Bayes's rule to compute the revised distribution.

We present this concept in a few different contexts but primarily focus on how our bird Yoshi sends signals about her food preferences.

Like many animals, Yoshi is a complex creature whose preferences cannot easily be summarized by a straightforward utility function the way they might in a textbook reinforcement learning problem. Her preferences are sequential, conditional, and evolving. We may not always know what our bird is thinking, but we have some good indicators that give us clues.

Oct 26 2018
24 mins
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Rank #4: The Complexity of Learning Neural Networks

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Over the past several years, we have seen many success stories in machine learning brought about by deep learning techniques. While the practical success of deep learning has been phenomenal, the formal guarantees have been lacking. Our current theoretical understanding of the many techniques that are central to the current ongoing big-data revolution is far from being sufficient for rigorous analysis, at best. In this episode of Data Skeptic, our host Kyle Polich welcomes guest John Wilmes, a mathematics post-doctoral researcher at Georgia Tech, to discuss the efficiency of neural network learning through complexity theory.

Oct 20 2017
38 mins
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Rank #5: Advertising Attribution with Nathan Janos

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A conversation with Convertro's Nathan Janos about methodologies used to help advertisers understand the affect each of their marketing efforts (print, SEM, display, skywriting, etc.) contributes to their overall return.

Jun 06 2014
1 hour 16 mins
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Rank #6: The Library Problem

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We close out 2016 with a discussion of a basic interview question which might get asked when applying for a data science job. Specifically, how a library might build a model to predict if a book will be returned late or not.

Dec 30 2016
35 mins
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Rank #7: [MINI] Primer on Deep Learning

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In this episode, we talk about a high-level description of deep learning.  Kyle presents a simple game (pictured below), which is more of a puzzle really, to try and give  Linh Da the basic concept.

Thanks to our sponsor for this week, the Data Science Association. Please check out their upcoming Dallas conference at dallasdatascience.eventbrite.com

Feb 10 2017
14 mins
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Rank #8: Crypto

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How do people think rationally about small probability events?

What is the optimal statistical process by which one can update their beliefs in light of new evidence?

This episode of Data Skeptic explores questions like this as Kyle consults a cast of previous guests and experts to try and answer the question "What is the probability, however small, that Bigfoot is real?"

Jul 17 2015
1 hour 24 mins
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Rank #9: Zillow Zestimate

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Zillow is a leading real estate information and home-related marketplace. We interviewed Andrew Martin, a data science Research Manager at Zillow, to learn more about how Zillow uses data science and big data to make real estate predictions.

Sep 01 2017
37 mins
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Rank #10: Big Data Tools and Trends

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In this episode, I speak with Raghu Ramakrishnan, CTO for Data at Microsoft.  We discuss services, tools, and developments in the big data sphere as well as the underlying needs that drove these innovations.

Feb 17 2017
30 mins
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Rank #11: Transfer Learning

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Sebastian Ruder is a research scientist at DeepMind.  In this episode, he joins us to discuss the state of the art in transfer learning and his contributions to it.

Jul 08 2019
29 mins
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Rank #12: Data Science at eHarmony

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I'm joined this week by Jon Morra, director of data science at eHarmony to discuss a variety of ways in which machine learning and data science are being applied to help connect people for successful long term relationships.

Interesting open source projects mentioned in the interview include Face-parts, a web service for detecting faces and extracting a robust set of fiducial markers (features) from the image, and Aloha, a Scala based machine learning library. You can learn more about these and other interesting projects at the eHarmony github page.

In the wrap up, Jon mentioned the LA Machine Learning meetup which he runs. This is a great resource for LA residents separate and complementary to datascience.la groups, so consider signing up for all of the above and I hope to see you there in the future.

May 27 2016
42 mins
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Rank #13: Game Theory

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Thanks to our sponsor The Great Courses.

This week's episode is a short primer on game theory.

For tickets to the free Data Skeptic meetup in Chicago on Tuesday, May 15 at the Mendoza College of Business (224 South Michigan Avenue, Suite 350), click here,

May 11 2018
24 mins
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Rank #14: Auditing Algorithms

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Algorithms are pervasive in our society and make thousands of automated decisions on our behalf every day. The possibility of digital discrimination is a very real threat, and it is very plausible for discrimination to occur accidentally (i.e. outside the intent of the system designers and programmers). Christian Sandvig joins us in this episode to talk about his work and the concept of auditing algorithms.

Christian Sandvig (@niftyc) has a PhD in communications from Stanford and is currently an Associate Professor of Communication Studies and Information at the University of Michigan. His research studies the predictable and unpredictable effects that algorithms have on culture. His work exploring the topic of auditing algorithms has framed the conversation of how and why we might want to have oversight on the way algorithms effect our lives. His writing appears in numerous publications including The Social Media Collective, The Huffington Post, and Wired.

One of his papers we discussed in depth on this episode was Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms, which is well worth a read.

Jan 29 2016
42 mins
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Rank #15: [MINI] Entropy

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Classically, entropy is a measure of disorder in a system. From a statistical perspective, it is more useful to say it's a measure of the unpredictability of the system. In this episode we discuss how information reduces the entropy in deciding whether or not Yoshi the parrot will like a new chew toy. A few other everyday examples help us examine why entropy is a nice metric for constructing a decision tree.

Dec 16 2016
16 mins
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Rank #16: [MINI] Random Forest

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Random forest is a popular ensemble learning algorithm which leverages bagging both for sampling and feature selection. In this episode we make an analogy to the process of running a bookstore.

Oct 07 2016
12 mins
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Rank #17: [MINI] Auto-correlative functions and correlograms

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When working with time series data, there are a number of important diagnostics one should consider to help understand more about the data. The auto-correlative function, plotted as a correlogram, helps explain how a given observations relates to recent preceding observations. A very random process (like lottery numbers) would show very low values, while temperature (our topic in this episode) does correlate highly with recent days.   See the show notes with details about Chapel Hill, NC weather data by visiting:   https://dataskeptic.com/blog/episodes/2016/acf-correlograms
Apr 22 2016
14 mins
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Rank #18: [MINI] k-d trees

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This episode reviews the concept of k-d trees: an efficient data structure for holding multidimensional objects. Kyle gives Linhda a dictionary and asks her to look up words as a way of introducing the concept of binary search. We actually spend most of the episode talking about binary search before getting into k-d trees, but this is a necessary prerequisite.

Feb 05 2016
14 mins
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Rank #19: [MINI] p-values

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In this mini, we discuss p-values and their use in hypothesis testing, in the context of an hypothetical experiment on plant flowering, and end with a reference to the Particle Fever documentary and how statistical significance played a role.

Jun 13 2014
16 mins
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Rank #20: [MINI] The T-Test

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The t-test is this week's mini-episode topic. The t-test is a statistical testing procedure used to determine if the mean of two datasets differs by a statistically significant amount. We discuss how a wine manufacturer might apply a t-test to determine if the sweetness, acidity, or some other property of two separate grape vines might differ in a statistically meaningful way.

Oct 17 2014
17 mins
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