Data Skeptic (general)

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.

Direct download: big-data-tools-and-trends.mp3
Category:general -- posted at: 9:28am PST

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

Direct download: deep-learning-primer.mp3
Category:general -- posted at: 8:00am PST

Versioning isn't just for source code. Being able to track changes to data is critical for answering questions about data provenance, quality, and reproducibility. Daniel Whitenack joins me this week to talk about these concepts and share his work on Pachyderm. Pachyderm is an open source containerized data lake.

During the show, Daniel mentioned the Gopher Data Science github repo as a great resource for any data scientists interested in the Go language. Although we didn't mention it, Daniel also did an interesting analysis on the 2016 world chess championship that complements our recent episode on chess well. You can find that post here

Supplemental music is Lee Rosevere's Let's Start at the Beginning.

 

Thanks to Periscope Data for sponsoring this episode. More about them at periscopedata.com/skeptics

Periscope Data

 

 

 

Direct download: data-provenance-and-reproducibility-with-pachyderm.mp3
Category:general -- posted at: 9:09am PST

Logistic Regression is a popular classification algorithm. In this episode, we discuss how it can be used to determine if an audio clip represents one of two given speakers. It assumes an output variable (isLinhda) is a linear combination of available features, which are spectral bands in the discussion on this episode.

 

Keep an eye on the dataskeptic.com blog this week as we post more details about this project.

 

Thanks to our sponsor this week, the Data Science Association.  Please check out their upcoming conference in Dallas on Saturday, February 18th, 2017 via the link below.

 

dallasdatascience.eventbrite.com

 

Direct download: logistic-regression.mp3
Category:general -- posted at: 9:08am PST

Prior work has shown that people's response to competition is in part predicted by their gender. Understanding why and when this occurs is important in areas such as labor market outcomes. A well structured study is challenging due to numerous confounding factors. Peter Backus and his colleagues have identified competitive chess as an ideal arena to study the topic. Find out why and what conclusions they reached.

Our discussion centers around Gender, Competition and Performance: Evidence from Real Tournaments from Backus, Cubel, Guid, Sanchez-Pages, and Mañas. A summary of their paper can also be found here.

 

Direct download: studying-competition-and-gender-through-chess.mp3
Category:general -- posted at: 8:00am PST

Deep learning can be prone to overfit a given problem. This is especially frustrating given how much time and computational resources are often required to converge. One technique for fighting overfitting is to use dropout. Dropout is the method of randomly selecting some neurons in one's network to set to zero during iterations of learning. The core idea is that each particular input in a given layer is not always available and therefore not a signal that can be relied on too heavily.

 

Direct download: dropout.mp3
Category:general -- posted at: 7:56am PST

In this episode I speak with Clarence Wardell and Kelly Jin about their mutual service as part of the White House's Police Data Initiative and Data Driven Justice Initiative respectively.

The Police Data Initiative was organized to use open data to increase transparency and community trust as well as to help police agencies use data for internal accountability. The PDI emerged from recommendations made by the Task Force on 21st Century Policing.

The Data Driven Justice Initiative was organized to help city, county, and state governments use data-driven strategies to help low-level offenders with mental illness get directed to the right services rather than into the criminal justice system.

Direct download: police-data-initiative-and-data-driven-justice-initiative.mp3
Category:general -- posted at: 8:10am PST

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.

 
Direct download: the-library-problem.mp3
Category:general -- posted at: 8:07am PST

Today's episode is a reading of Isaac Asimov's Franchise.  As mentioned on the show, this is just a work of fiction to be enjoyed and not in any way some obfuscated political statement.  Enjoy, and happy holidays!

Direct download: 2016-holiday-special.mp3
Category:general -- posted at: 8:00am PST

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.

Direct download: entropy.mp3
Category:general -- posted at: 12:23am PST

Cloud services are now ubiquitous in data science and more broadly in technology as well. This week, I speak to Mark Souza, Tobias Ternström, and Corey Sanders about various aspects of data at scale. We discuss the embedding of R into SQLServer, SQLServer on linux, open source, and a few other cloud topics.

Direct download: ms-connect-conference.mp3
Category:general -- posted at: 8:24am PST

Today's episode is all about Causal Impact, a technique for estimating the impact of a particular event on a time series. We talk to William Martin about his research into the impact releases have on app and we also chat with Karen Blakemore about a project she helped us build to explore the impact of a Saturday Night Live appearance on a musician's career.

Martin's work culminated in a paper Causal Impact for App Store Analysis. A shorter summary version can be found here. His company helping app developers do this sort of analysis can be found at crestweb.cs.ucl.ac.uk/appredict/.

Direct download: causal-impact.mp3
Category:general -- posted at: 7:56am PST

The Bootstrap is a method of resampling a dataset to possibly refine it's accuracy and produce useful metrics on the result. The bootstrap is a useful statistical technique and is leveraged in Bagging (bootstrap aggregation) algorithms such as Random Forest. We discuss this technique related to polling and surveys.

Direct download: the-bootstrap.mp3
Category:general -- posted at: 8:00am PST

The Gini Coefficient (as it relates to decision trees) is one approach to determining the optimal decision to introduce which splits your dataset as part of a decision tree. To pick the right feature to split on, it considers the frequency of the values of that feature and how well the values correlate with specific outcomes that you are trying to predict.

Direct download: gini-index.mp3
Category:general -- posted at: 8:00am PST

Financial analysis techniques for studying numeric, well structured data are very mature. While using unstructured data in finance is not necessarily a new idea, the area is still very greenfield. On this episode,Delia Rusu shares her thoughts on the potential of unstructured data and discusses her work analyzing Wikipedia to help inform financial decisions.

Delia's talk at PyData Berlin can be watched on Youtube (Estimating stock price correlations using Wikipedia). The slides can be found here and all related code is available on github.

Direct download: unstructured-data-for-finance.mp3
Category:general -- posted at: 8:00am PST

AdaBoost is a canonical example of the class of AnyBoost algorithms that create ensembles of weak learners. We discuss how a complex problem like predicting restaurant failure (which is surely caused by different problems in different situations) might benefit from this technique.

Direct download: adaboost.mp3
Category:general -- posted at: 8:00am PST

Platform as a service is a growing trend in data science where services like fraud analysis and face detection can be provided via APIs. Such services turn the actual model into a black box to the consumer. But can the model be reverse engineered?

Florian Tramèr shares his work in this episode showing that it can. The paper Stealing Machine Learning Models via Prediction APIs is definitely worth your time to read if you enjoy this episode. Related source code can be found in https://github.com/ftramer/Steal-ML.

Direct download: stealing-models-from-the-cloud.mp3
Category:general -- posted at: 7:54am PST

For machine learning models created with the random forest algorithm, there is no obvious diagnostic to inform you which features are more important in the output of the model. Some straightforward but useful techniques exist revolving around removing a feature and measuring the decrease in accuracy or Gini values in the leaves. We broadly discuss these techniques in this episode.

Direct download: feature-importance.mp3
Category:general -- posted at: 9:24am PST

As cities provide bike sharing services, they must also plan for how to redistribute bicycles as they inevitably build up at more popular destination stations. In this episode, Hui Xiong talks about the solution he and his colleagues developed to rebalance bike sharing systems.

Direct download: nyc-bike-share-rebalancing.mp3
Category:general -- posted at: 8:00am PST

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.

Direct download: random-forest.mp3
Category:general -- posted at: 8:00am PST

Jo Hardin joins us this week to discuss the ASA's Election Prediction Contest. This is a competition aimed at forecasting the results of the upcoming US presidential election competition. More details are available in Jo's blog post found here.

You can find some useful R code for getting started automatically gathering data from 538 via Jo's github and official contest details are available here. During the interview we also mention Daily Kos and 538.

Direct download: asa-election-prediction-with-jo-hardin.mp3
Category:general -- posted at: 8:00am PST

The F1 score is a model diagnostic that combines precision and recall to provide a singular evaluation for model comparison.  In this episode we discuss how it applies to selecting an interior designer.

Direct download: f1-score.mp3
Category:general -- posted at: 9:49am PST

Urban congestion effects every person living in a city of any reasonable size. Lewis Lehe joins us in this episode to share his work on downtown congestion pricing. We explore topics of how different pricing mechanisms effect congestion as well as how data visualization can inform choices.

You can find examples of Lewis's work at setosa.io. His paper which we discussed during the interview isDistance-dependent congestion pricing for downtown zones.

On this episode, we discuss State of California data which can be found at pems.dot.ca.gov.

Direct download: urban-congestion.mp3
Category:general -- posted at: 8:00am PST

Heteroskedasticity is a term used to describe a relationship between two variables which has unequal variance over the range.  For example, the variance in the length of a cat's tail almost certainly changes (grows) with age.  On the other hand, the average amount of chewing gum a person consume probably has a consistent variance over a wide range of human heights.

We also discuss some issues with the visualization shown in the tweet embedded below.

Image claiming relationship between income and tickets issued

Direct download: heteroskedasticity.mp3
Category:general -- posted at: 8:00am PST

Our guest today is Michael Cuthbert, an associate professor of music at MIT and principal investigator of the Music21 project, which we focus our discussion on today.

Music21 is a python library making analysis of music accessible and fun. It supports integration with popular formats such as MIDI, MusicXML, Lilypond, and others. It's also well integrated with The Elvis Project, enabling users to import large volumes of music for easy analysis. Music21 is a great platform for musicologists and machine learning researchers alike to explore patterns and structure in music.

Direct download: music21.mp3
Category:general -- posted at: 8:00am PST

Paxos is a protocol for arriving a consensus in a distributed computing system which accounts for unreliability of the nodes.  We discuss how this might be used in the real world in the event of a massive disaster.

Direct download: paxos.mp3
Category:general -- posted at: 8:00am PST

Machine learning models are often criticized for being black boxes. If a human cannot determine why the model arrives at the decision it made, there's good cause for skepticism. Classic inspection approaches to model interpretability are only useful for simple models, which are likely to only cover simple problems.

The LIME project seeks to help us trust machine learning models. At a high level, it takes advantage of local fidelity. For a given example, a separate model trained on neighbors of the example are likely to reveal the relevant features in the local input space to reveal details about why the model arrives at it's conclusion.

In this episode, Marco Tulio Ribeiro joins us to discuss how LIME (Locally Interpretable Model-Agnostic Explanations) can help users trust machine learning models. The accompanying paper is titled "Why Should I Trust You?": Explaining the Predictions of Any Classifier.

Direct download: trust-in-ml.mp3
Category:general -- posted at: 8:00am PST

Analysis of variance is a method used to evaluate differences between the two or more groups.  It works by breaking down the total variance of the system into the between group variance and within group variance.  We discuss this method in the context of wait times getting coffee at Starbucks.

Direct download: anova.mp3
Category:general -- posted at: 8:00am PST

When humans describe images, they have a reporting bias, in that the report only what they consider important. Thus, in addition to considering whether something is present in an image, one should consider whether it is also relevant to the image before labeling it.

Ishan Misra joins us this week to discuss his recent paper Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels which explores a novel architecture for learning to distinguish presence and relevance. This work enables web-scale datasets to be useful for training, not just well groomed hand labeled corpora.

Direct download: ishan.mp3
Category:general -- posted at: 8:00am PST

Survival analysis techniques are useful for studying the longevity of groups of elements or individuals, taking into account time considerations and right censorship. This episode explores how survival analysis can describe marriages, in particular, using the non-parametric Cox proportional hazard model.

This episode discusses some good summaries of survey data on marriage and divorce which can be found here.

The python lifelines library is a good place to get started for people that want to do some hands on work.

Direct download: survival-analysis.mp3
Category:general -- posted at: 8:00am PST

This week is an insightful discussion with Claudia Perlich about some situations in machine learning where models can be built, perhaps by well-intentioned practitioners, to appear to be highly predictive despite being trained on random data. Our discussion covers some novel observations about ROC and AUC, as well as an informative discussion of leakage.

Much of our discussion is inspired by two excellent papers Claudia authored: Leakage in Data Mining: Formulation, Detection, and Avoidance and On Cross Validation and Stacking: Building Seemingly Predictive Models on Random Data. Both are highly recommended reading!

Direct download: Predictive_Models_on_Random_Data.mp3
Category:general -- posted at: 8:00am PST

An ROC curve is a plot that compares the trade off of true positives and false positives of a binary classifier under different thresholds. The area under the curve (AUC) is useful in determining how discriminating a model is. Together, ROC and AUC are very useful diagnostics for understanding the power of one's model and how to tune it.

Direct download: roc-auc.mp3
Category:general -- posted at: 8:00am PST

I'm joined by Chris Stucchio this week to discuss how deliberate or uninformed statistical practitioners can derive spurious and arbitrary results via multiple comparisons. We discuss p-hacking and a variety of other important lessons and tips for proper analysis.

You can enjoy Chris's writing on his blog at chrisstucchio.com and you may also like his recent talk Multiple Comparisons: Make Your Boss Happy with False Positives, Guarenteed.

Direct download: multiple-comparisons.mp3
Category:general -- posted at: 8:00am PST

If you'd like to make a good prediction, your best bet is to invent a time machine, visit the future, observe the value, and return to the past. For those without access to time travel technology, we need to avoid including information about the future in our training data when building machine learning models. Similarly, if any other feature whose value would not actually be available in practice at the time you'd want to use the model to make a prediction, is a feature that can introduce leakage to your model.

Direct download: leakage.mp3
Category:general -- posted at: 8:00am PST

Kristian Lum (@KLdivergence) joins me this week to discuss her work at @hrdag on predictive policing. We also discuss Multiple Systems Estimation, a technique for inferring statistical information about a population from separate sources of observation.

If you enjoy this discussion, check out the panel Tyranny of the Algorithm? Predictive Analytics & Human Rights which was mentioned in the episode.

Direct download: predictive-policing.mp3
Category:general -- posted at: 8:00am PST

Distributed computing cannot guarantee consistency, accuracy, and partition tolerance. Most system architects need to think carefully about how they should appropriately balance the needs of their application across these competing objectives. Linh Da and Kyle discuss the CAP Theorem using the analogy of a phone tree for alerting people about a school snow day.

Direct download: cap-theorem.mp3
Category:general -- posted at: 8:00am PST

A startup is claiming that they can detect terrorists purely through facial recognition. In this solo episode, Kyle explores the plausibility of these claims.

Direct download: detecting-terrorists.mp3
Category:general -- posted at: 8:00am PST

Goodhart's law states that "When a measure becomes a target, it ceases to be a good measure". In this mini-episode we discuss how this affects SEO, call centers, and Scrum.

Direct download: goodharts-law.mp3
Category:general -- posted at: 8:00am PST

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.

Direct download: data-science-at-eharmony.mp3
Category:general -- posted at: 8:00am PST

Mystery shoppers and fruit cultivation help us discuss stationarity - a property of some time serieses that are invariant to time in several ways. Differencing is one approach that can often convert a non-stationary process into a stationary one. If you have a stationary process, you get the benefits of many known statistical properties that can enable you to do a significant amount of inferencing and prediction.

Direct download: stationarity.mp3
Category:general -- posted at: 8:00am PST

I'm joined by Wes McKinney (@wesmckinn) and Hadley Wickham (@hadleywickham) on this episode to discuss their joint project Feather. Feather is a file format for storing data frames along with some metadata, to help with interoperability between languages. At the time of recording, libraries are available for R and Python, making it easy for data scientists working in these languages to quickly and effectively share datasets and collaborate.

Direct download: feather.mp3
Category:general -- posted at: 8:00am PST

Bargaining is the process of two (or more) parties attempting to agree on the price for a transaction.  Game theoretic approaches attempt to find two strategies from which neither party is motivated to deviate.  These strategies are said to be in equilibrium with one another.  The equilibriums available in bargaining depend on the the transaction mechanism and the information of the parties.  Discounting (how long parties are willing to wait) has a significant effect in this process.  This episode discusses some of the choices Kyle and Linh Da made in deciding what offer to make on a house.

Direct download: bargaining.mp3
Category:general -- posted at: 8:00am PST

Deepjazz is a project from Ji-Sung Kim, a computer science student at Princeton University. It is built using Theano, Keras, music21, and Evan Chow's project jazzml. Deepjazz is a computational music project that creates original jazz compositions using recurrent neural networks trained on Pat Metheny's "And Then I Knew". You can hear some of deepjazz's original compositions on soundcloud.

Direct download: deepjazz.mp3
Category:general -- posted at: 8:00am PST

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:
 
 
Direct download: acf.mp3
Category:general -- posted at: 8:00am PST

This week I spoke with Elham Shaabani and Paulo Shakarian (@PauloShakASU) about their recent paper Early Identification of Violent Criminal Gang Members (also available onarXiv). In this paper, they use social network analysis techniques and machine learning to provide early detection of known criminal offenders who are in a high risk group for committing violent crimes in the future. Their techniques outperform existing techniques used by the police. Elham and Paulo are part of the Cyber-Socio Intelligent Systems (CySIS) Lab.

Direct download: predicting-violent-offenders.mp3
Category:general -- posted at: 8:00am PST

A dinner party at Data Skeptic HQ helps teach the uses of fractional factorial design for studying 2-way interactions.

Direct download: Fractional_factorial_design.mp3
Category:general -- posted at: 8:00am PST

Cheng-tao Chu (@chengtao_chu) joins us this week to discuss his perspective on common mistakes and pitfalls that are made when doing machine learning. This episode is filled with sage advice for beginners and intermediate users of machine learning, and possibly some good reminders for experts as well. Our discussion parallels his recent blog postMachine Learning Done Wrong.

Cheng-tao Chu is an entrepreneur who has worked at many well known silicon valley companies. His paper Map-Reduce for Machine Learning on Multicore is the basis for Apache Mahout. His most recent endeavor has just emerged from steath, so please check out OneInterview.io.

Direct download: machine_learning_done_wrong.mp3
Category:general -- posted at: 8:00am PST

Co-host Linh Da was in a biking accident after hitting a pothole. She sustained an injury that required stitches. This is the story of our quest to file a 311 complaint and track it through the City of Los Angeles's open data portal.

My guests this episode are Chelsea Ursaner (LA City Open Data Team), Ben Berkowitz (CEO and founder of SeeClickFix), and Russ Klettke (Editor of pothole.info)

Direct download: potholes.mp3
Category:general -- posted at: 8:24am PST

Certain data mining algorithms (including k-means clustering and k-nearest neighbors) require a user defined parameter k. A user of these algorithms is required to select this value, which raises the questions: what is the "best" value of k that one should select to solve their problem?

This mini-episode explores the appropriate value of k to use when trying to estimate the cost of a house in Los Angeles based on the closests sales in it's area.

Direct download: the-elbow-method.mp3
Category:general -- posted at: 8:00am PST

Today on Data Skeptic, Lachlan Gunn joins us to discuss his recent paper Too Good to be True. This paper highlights a somewhat paradoxical / counterintuitive fact about how unanimity is unexpected in cases where perfect measurements cannot be taken. With large enough data, some amount of error is expected.

The "Too Good to be True" paper highlights three interesting examples which we discuss in the podcast. You can also watch a lecture from Lachlan on this topic via youtube here.

Direct download: too_good_to_be_true.mp3
Category:general -- posted at: 8:00am PST

How well does your model explain your data? R-squared is a useful statistic for answering this question. In this episode we explore how it applies to the problem of valuing a house. Aspects like the number of bedrooms go a long way in explaining why different houses have different prices. There's some amount of variance that can be explained by a model, and some amount that cannot be directly measured. R-squared is the ratio of the explained variance to the total variance. It's not a measure of accuracy, it's a measure of the power of one's model.

Direct download: r-squared.mp3
Category:general -- posted at: 8:00am PST

 
Direct download: think_again.mp3
Category:general -- posted at: 7:30am PST

[MINI] Multiple Regression

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.

Direct download: multiple_regressions.mp3
Category:general -- posted at: 7:30am PST

Samuel Mehr joins us this week to share his perspective on why people are musical, where music comes from, and why it works the way it does. We discuss a number of empirical studies related to music and musical cognition, and dispense a few myths about music along the way.

Some of Sam's work discussed in this episode include Music in the Home: New Evidence for an Intergenerational Link,Two randomized trials provide no consistent evidence for nonmusical cognitive benefits of brief preschool music enrichment, and Miscommunication of science: music cognition research in the popular press. Additional topics we discussed are also covered in a Harvard Gazette article featuring Sam titled Muting the Mozart effect.

You can follow Sam on twitter via @samuelmehr.

Direct download: samuel.mp3
Category:general -- posted at: 7:30am PST

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.

Direct download: kd_trees.mp3
Category:general -- posted at: 7:13am PST

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.

Direct download: auditing_algorithms.mp3
Category:general -- posted at: 7:00am PST

Today's episode begins by asking how many left handed employees we should expect to be at a company before anyone should claim left handedness discrimination. If not lefties, let's consider eye color, hair color, favorite ska band, most recent grocery store used, and any number of characteristics could be studied to look for deviations from the norm in a company.

When multiple comparisons are to be made simultaneous, one must account for this, and a common method for doing so is with the Bonferroni Correction. It is not, however, a sure fire procedure, and this episode wraps up with a bit of skepticism about it.

Direct download: bonferroni-correction2.mp3
Category:general -- posted at: 7:00am PST

A recent paper in the journal of Judgment and Decision Making titled On the reception and detection of pseudo-profound bullshit explores empirical questions around a reader's ability to detect statements which may sound profound but are actually a collection of buzzwords that fail to contain adequate meaning or truth. These statements are definitively different from lies and nonesense, as we discuss in the episode.

This paper proposes the Bullshit Receptivity scale (BSR) and empirically demonstrates that it correlates with existing metrics like the Cognitive Reflection Test, building confidence that this can be a useful, repeatable, empirical measure of a person's ability to detect pseudo-profound statements as being different from genuinely profound statements. Additionally, the correlative results provide some insight into possible root causes for why individuals might find great profundity in these statements based on other beliefs or cognitive measures.

The paper's lead author Gordon Pennycook joins me to discuss this study's results.

If you'd like some examples of pseudo-profound bullshit, you can randomly generate some based on Deepak Chopra's twitter feed.

To read other work from Gordon, check out his Google Scholar page and find him on twitter via @GordonPennycook.

And just for fun, if you think you've dreamed up a Data Skeptic related pseudo-profound bullshit statement, tweet it with hashtag #pseudoprofound. If I see an especially clever or humorous one, I might want to send you a free Data Skeptic sticker.

 
Direct download: pseudo-profound-episode.mp3
Category:general -- posted at: 7:00am PST

Today's mini episode discusses the widely known optimization algorithm gradient descent in the context of hiking in a foggy hillside.

Direct download: gradient_descent.mp3
Category:general -- posted at: 12:00am PST

This episode is a discussion of data visualization and a proposed New Year's resolution for Data Skeptic listeners. Let's kill the word cloud.

Direct download: lets_kill_the_word_cloud.mp3
Category:general -- posted at: 12:00am PST

Today's episode is a reading of Isaac Asimov's The Machine that Won the War. I can't think of a story that's more appropriate for Data Skeptic.

Direct download: 2015_Holiday_Special.mp3
Category:general -- posted at: 12:00am PST

In this interview with Aaron Halfaker of the Wikimedia Foundation, we discuss his research and career related to the study of Wikipedia. In his paper The Rise and Decline of an open Collaboration Community, he highlights a trend in the declining rate of active editors on Wikipedia which began in 2007. I asked Aaron about a variety of possible hypotheses for the phenomenon, in particular, how automated quality control tools that revert edits automatically could play a role. This lead Aaron and his collaborators to develop Snuggle, an optimized interface to help Wikipedians better welcome new comers to the community.

We discuss the details of these topics as well as ORES, which provides revision scoring as a service to any software developer that wants to consume the output of their machine learning based scoring.

You can find Aaron on Twitter as @halfak.

Direct download: wikipedia-revision-scoring-as-a-service.mp3
Category:general -- posted at: 6:30am PST

Today's topic is term frequency inverse document frequency, which is a statistic for estimating the importance of words and phrases in a set of documents.

Direct download: tf_-_idf.mp3
Category:general -- posted at: 8:45am PST

Early astronomers could see several of the planets with the naked eye. The invention of the telescope allowed for further understanding of our solar system. The work of Isaac Newton allowed later scientists to accurately predict Neptune, which was later observationally confirmed exactly where predicted. It seemed only natural that a similar unknown body might explain anomalies in the orbit of Mercury, and thus began the search for the hypothesized planet Vulcan.

Thomas Levenson's book "The Hunt for Vulcan" is a narrative of the key scientific minds involved in the search and eventual refutation of an unobserved planet between Mercury and the sun. Thomas joins me in this episode to discuss his book and the fascinating story of the quest to find this planet.

During the discussion, we mention one of the contributions made by Urbain-Jean-Joseph Le Verrier which involved some complex calculations which enabled him to predict where to find the planet that would eventually be called Neptune. The calculus behind this work is difficult, and some of that work is demonstrated in a Jupyter notebook I recently discovered from Paulo Marques titled The-Body Problem.

Thomas Levenson is a professor at MIT and head of its science writing program. He is the author of several books, including Einstein in Berlin and Newton and the Counterfeiter: The Unknown Detective Career of the World’s Greatest Scientist. He has also made ten feature-length documentaries (including a two-hour Nova program on Einstein) for which he has won numerous awards. In his most recent book "The Hunt for Vulcan", explores the century spanning quest to explain the movement of the cosmos via theory and the role the hypothesized planet Vulcan played in the story.

Follow Thomas on twitter @tomlevenson and check out his blog athttps://inversesquare.wordpress.com/.

Pick up your copy of The Hunt for Vulcan at your local bookstore, preferred book buying place, or at the Penguin Random House site.

Direct download: the-hunt-for-vulcan.mp3
Category:general -- posted at: 12:00am PST

Today's episode discusses the accuracy paradox. There are cases when one might prefer a less accurate model because it yields more predictive power or better captures the underlying causal factors describing the outcome variable you are interested in. This is especially relevant in machine learning when trying to predict rare events. We discuss how the accuracy paradox might apply if you were trying to predict the likelihood a person was a bird owner.

Direct download: the-accuracy-paradox.mp3
Category:general -- posted at: 12:00am PST

... or should this have been called data science from a neuroscientist's perspective? Either way, I'm sure you'll enjoy this discussion with Laurie Skelly. Laurie earned a PhD in Integrative Neuroscience from the Department of Psychology at the University of Chicago. In her life as a social neuroscientist, using fMRI to study the neural processes behind empathy and psychopathy, she learned the ropes of zooming in and out between the macroscopic and the microscopic -- how millions of data points come together to tell us something meaningful about human nature. She's currently at Metis Data Science, an organization that helps people learn the skills of data science to transition in industry.

In this episode, we discuss fMRI technology, Laurie's research studying empathy and psychopathy, as well as the skills and tools used in common between neuroscientists and data scientists. For listeners interested in more on this subject, Laurie recommended the blogs Neuroskeptic, Neurocritic, and Neuroecology.

We conclude the episode with a mention of the upcoming Metis Data Science San Francisco cohort which Laurie will be teaching. If anyone is interested in applying to participate, they can do so here.

Direct download: neuroscience.mp3
Category:general -- posted at: 12:00am PST

A discussion of the expected number of cars at a stoplight frames today's discussion of the bias variance tradeoff. The central ideal of this concept relates to model complexity. A very simple model will likely generalize well from training to testing data, but will have a very high variance since it's simplicity can prevent it from capturing the relationship between the covariates and the output. As a model grows more and more complex, it may capture more of the underlying data but the risk that it overfits the training data and therefore does not generalize (is biased) increases. The tradeoff between minimizing variance and minimizing bias is an ongoing challenge for data scientists, and an important discussion for skeptics around how much we should trust models.

Direct download: bias-variance-tradeoff.mp3
Category:general -- posted at: 12:00am PST

The recent opinion piece Big Data Doesn't Exist on Tech Crunch by Slater Victoroff is an interesting discussion about the usefulness of data both big and small. Slater joins me this episode to discuss and expand on this discussion.

Slater Victoroff is CEO of indico Data Solutions, a company whose services turn raw text and image data into human insight. He, and his co-founders, studied at Olin College of Engineering where indico was born. indico was then accepted into the "Techstars Accelarator Program" in the Fall of 2014 and went on to raise $3M in seed funding. His recent essay "Big Data Doesn't Exist" received a lot of traction on TechCrunch, and I have invited Slater to join me today to discuss his perspective and touch on a few topics in the machine learning space as well.

Direct download: big-data-doesnt-exist.mp3
Category:general -- posted at: 12:00am PST

The degree to which two variables change together can be calculated in the form of their covariance. This value can be normalized to the correlation coefficient, which has the advantage of transforming it to a unitless measure strictly bounded between -1 and 1. This episode discusses how we arrive at these values and why they are important.

Direct download: covariance_and_correlation.mp3
Category:general -- posted at: 12:00am PST

Today's guest is Cameron Davidson-Pilon. Cameron has a masters degree in quantitative finance from the University of Waterloo. Think of it as statistics on stock markets. For the last two years he's been the team lead of data science at Shopify. He's the founder of dataoragami.net which produces screencasts teaching methods and techniques of applied data science. He's also the author of the just released in print book Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference, which you can also get in a digital form.

This episode focuses on the topic of Bayesian A/B Testing which spans just one chapter of the book. Related to today's discussion is the Data Origami post The class imbalance problem in A/B testing.

Lastly, Data Skeptic will be giving away a copy of the print version of the book to one lucky listener who has a US based delivery address. To participate, you'll need to write a review of any site, book, course, or podcast of your choice on datasciguide.com. After it goes live, tweet a link to it with the hashtag #WinDSBook to be given an entry in the contest. This contest will end November 20th, 2015, at which time I'll draw a single randomized winner and contact them for delivery details via direct message on Twitter.

Direct download: bayesian-methods-for-hackers.mp3
Category:general -- posted at: 12:00am PST

The central limit theorem is an important statistical result which states that typically, the mean of a large enough set of independent trials is approximately normally distributed.  This episode explores how this might be used to determine if an amazon parrot like Yoshi produces or or less waste than an African Grey, under the assumption that the individual distributions are not normal.

Direct download: Central_Limit_Theorem.mp3
Category:general -- posted at: 12:00am PST

Today's guest is Chris Hofstader (@gonz_blinko), an accessibility researcher and advocate, as well as an activist for causes such as improving access to information for blind and vision impaired people. His background in computer programming enabled him to be the leader of JAWS, a Windows program that allowed people with a visual impairment to read their screen either through text-to-speech or a refreshable braille display. He's the Managing Member of 3 Mouse Technology. He's also a frequent blogger primarily at chrishofstader.com.

For web developers and site owners, Chris recommends two tools to help test for accessibility issues: tenon.io and dqtech.co.

A guest post from Chris appeared on the Skepchick blogged titled Skepticism and Disability which lead to the formation of the sister site Skeptibility.

In a discussion of skepticism and favorite podcasts, Chris mentioned a number of great shows, most notably The Pod Delusion to which he was a contributor. Additionally, Chris has also appeared on The Atheist Nomads.

Lastly, a shout out from Chris to musician Shelley Segal whom he hosted just before the date of recording of this episode. Her music can be found on her site or via bandcamp.

Direct download: accessible-technology.mp3
Category:general -- posted at: 12:00am PST

Our episode this week begins with a correction. Back in episode 28 (Monkeys on Typewriters), Kyle made some bold claims about the probability that monkeys banging on typewriters might produce the entire works of Shakespeare by chance. The proof shown in the show notes turned out to be a bit dubious and Dave Spiegel joins us in this episode to set the record straight.

In addition to that, our discussion explores a number of interesting topics in astronomy and astrophysics. This includes a paper Dave wrote with Ed Turner titled "Bayesian analysis of the astrobiological implications of life's early emergence on Earth" as well as exoplanet discovery.

Direct download: Shakespeare-abiogenesis-exoplanets.mp3
Category:general -- posted at: 12:30am PST

There are several factors that are important to selecting an appropriate sample size and dealing with small samples. The most important questions are around representativeness - how well does your sample represent the total population and capture all it's variance?

Linhda and Kyle talk through a few examples including elections, picking an Airbnb, produce selection, and home shopping as examples of cases in which the amount of observations one has are more or less important depending on how complex the underlying system one is observing is.

Direct download: sample_sizes.mp3
Category:general -- posted at: 11:47pm PST

There's an old adage which says you cannot fit a model which has more parameters than you have data. While this is often the case, it's not a universal truth. Today's guest Jake VanderPlas explains this topic in detail and provides some excellent examples of when it holds and doesn't. Some excellent visuals articulating the points can be found on Jake's blog Pythonic Perambulations, specifically on his post The Model Complexity Myth.

We also touch on Jake's work as an astronomer, his noteworthy open source contributions, and forthcoming book (currently available in an Early Edition) Python Data Science Handbook.

Direct download: model_complexity_myth.mp3
Category:general -- posted at: 12:00am PST

There are many occasions in which one might want to know the distance or similarity between two things, for which the means of calculating that distance is not necessarily clear. The distance between two points in Euclidean space is generally straightforward, but what about the distance between the top of Mount Everest to the bottom of the ocean? What about the distance between two sentences?

This mini-episode summarizes some of the considerations and a few of the means of calculating distance. We touch on Jaccard Similarity, Manhattan Distance, and a few others.

Direct download: distance_measures.mp3
Category:general -- posted at: 12:00am PST

ContentMine is a project which provides the tools and workflow to convert scientific literature into machine readable and machine interpretable data in order to facilitate better and more effective access to the accumulated knowledge of human kind. The program's founder Peter Murray-Rust joins us this week to discuss ContentMine. Our discussion covers the project, the scientific publication process, copywrite, and several other interesting topics.

Direct download: contentmine.mp3
Category:general -- posted at: 12:30am PST

Today's mini-episode explains the distinction between structured and unstructured data, and debates which of these categories best describe recipes.

Direct download: structured_unstructured.mp3
Category:general -- posted at: 11:22pm PST

Yusan Lin shares her research on using data science to explore the fashion industry in this episode. She has applied techniques from data mining, natural language processing, and social network analysis to explore who are the innovators in the fashion world and how their influence effects other designers.

If you found this episode interesting and would like to read more, Yusan's papers Text-Generated Fashion Influence Model: An Empirical Study on Style.com and The Hidden Influence Network in the Fashion Industry are worth reading.

Direct download: yusan_lin.mp3
Category:general -- posted at: 12:01am PST

[MINI] PageRank

PageRank is the algorithm most famous for being one of the original innovations that made Google stand out as a search engine. It was defined in the classic paper The Anatomy of a Large-Scale Hypertextual Web Search Engine by Sergey Brin and Larry Page. While this algorithm clearly impacted web searching, it has also been useful in a variety of other applications. This episode presents a high level description of this algorithm and how it might apply when trying to establish who writes the most influencial academic papers.

Direct download: pagerank.mp3
Category:general -- posted at: 12:01am PST

Crypto

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?"

Direct download: Data_Skeptic_-_Crypto.mp3
Category:general -- posted at: 2:34am PST

A recent episode of the Skeptics Guide to the Universe included a slight rant by Dr. Novella and the rouges about a shortcoming in operating systems.  This episode explores why such a (seemingly obvious) flaw might make sense from an engineering perspective, and how data science might be the solution.

In this solo episode, Kyle proposes the concept of "annoyance mining" - the idea that with proper logging and enough feedback, data scientists could be provided the right dataset from which they can detect flaws and annoyances in software and other systems and automatically detect potential bugs, flaws, and improvements which could make those systems better.

As system complexity grows, it seems that an abstraction like this might be required in order to keep maintaining an effective development cycle.  This episode is a bit of a soap box for Kyle as he explores why and how we might track an appropriate amount of data to be able to make better software and systems more suited for the users.

Direct download: annoyance_mining.mp3
Category:general -- posted at: 11:18pm PST

This episode contains converage of the 2015 Data Fest hosted at UCLA.  Data Fest is an analysis competition that gives teams of students 48 hours to explore a new dataset and present novel findings.  This year, data from Edmunds.com was provided, and students competed in three categories: best recommendation, best use of external data, and best visualization.

Direct download: Data_Fest_2015.mp3
Category:general -- posted at: 11:55pm PST

Nicole Goebel joins us this week to share her experiences in oceanography studying phytoplankton and other aspects of the ocean and how data plays a role in that science.

 

We also discuss Thinkful where Nicole and I are both mentors for the Introduction to Data Science course.

Last but not least, check out Nicole's blog Data Science Girl and the videos Kyle mentioned on her Youtube channel featuring one on the diversity of phytoplankton and how that changes in time and space.

Direct download: Oceanography_and_Data_Science.mp3
Category:general -- posted at: 12:06am PST

I'm joined this week by Alex Boklin to explore the topic of magical thinking especially in the context of Rhonda Byrne's "The Secret", and the similarities it bears to The Global Consciousness Project (GCP). The GCP puts forward the hypothesis that random number generators elicit statistically significant changes as a result of major world events.

Direct download: The_Secret_and_the_Global_Consciousness_Project.mp3
Category:general -- posted at: 12:05am PST

This miniepisode discusses the technique called Cross Validation - a process by which one randomly divides up a dataset into numerous small partitions. Next, (typically) one is held out, and the rest are used to train some model. The hold out set can then be used to validate how good the model does at describing/predicting new data.

Direct download: MINI_Cross_Validation.mp3
Category:general -- posted at: 7:51am PST

This episode features a discussion with statistics PhD student Zach Seeskin about a project he was involved in as part of the Eric and Wendy Schmidt Data Science for Social Good Summer Fellowship.  The project involved exploring the relationship (if any) between streetlight outages and crime in the City of Chicago.  We discuss how the data was accessed via the City of Chicago data portal, how the analysis was done, and what correlations were discovered in the data.  Won't you listen and hear what was found? 

Direct download: Streetlight_Outage_and_Crime_Rate_Analysis_with_Zach_Seeskin.mp3
Category:general -- posted at: 6:00am PST

In this week's episode, we discuss applied solutions to big data problem with big data engineer Jay Shankar.  The episode explores approaches and design philosophy to solving real world big data business problems, and the exploration of the wide array of tools available.

 

Direct download: Data_Skeptic_Podcast_-_Big_Data_Tools.mp3
Category:general -- posted at: 6:00am PST