Data Skeptic

Data Skeptic

Australia

Data Skeptic alternates between short mini episodes with the host explaining concepts from data science to his non-data scientist wife, and longer interviews featuring practitioners and experts on interesting topics related to data, all through the eye of scientific skepticism.

Episodes

Reinventing Sponsored Search Auctions  

In this Data Skeptic episode, Kyle is joined by guest Ruggiero Cavallo to discuss his latest efforts to mitigate the problems presented in this new world of online advertising. Working with his collaborators, Ruggiero reconsiders the search ad allocation and pricing problems from the ground up and redesigns a search ad selling system. He discusses a mechanism that optimizes an entire page of ads globally based on efficiency-maximizing search allocation and a novel technical approach to computing prices.

[MINI] The Perceptron  

Today's episode overviews the perceptron algorithm. This rather simple approach is characterized by a few particular features. It updates its weights after seeing every example, rather than as a batch. It uses a step function as an activation function. It's only appropriate for linearly separable data, and it will converge to a solution if the data meets these criteria. Being a fairly simple algorithm, it can run very efficiently. Although we don't discuss it in this episode, multi-layer perceptron networks are what makes this technique most attractive.

The Data Refuge Project  

DataRefuge is a public collaborative, grassroots effort around the United States in which scientists, researchers, computer scientists, librarians and other volunteers are working to download, save, and re-upload government data. The DataRefuge Project, which is led by the UPenn Program in Environmental Humanities and the Penn Libraries group at University of Pennsylvania, aims to foster resilience in an era of anthropogenic global climate change and raise awareness of how social and political events affect transparency.

 

[MINI] Automated Feature Engineering  

If a CEO wants to know the state of their business, they ask their highest ranking executives. These executives, in turn, should know the state of the business through reports from their subordinates. This structure is roughly analogous to a process observed in deep learning, where each layer of the business reports up different types of observations, KPIs, and reports to be interpreted by the next layer of the business. In deep learning, this process can be thought of as automated feature engineering. DNNs built to recognize objects in images may learn structures that behave like edge detectors in the first hidden layer. Proceeding layers learn to compose more abstract features from lower level outputs. This episode explore that analogy in the context of automated feature engineering.

Linh Da and Kyle discuss a particular image in this episode. The image included below in the show notes is drawn from the work of Lee, Grosse, Ranganath, and Ng in their paper Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations.

 

Big Data Tools and Trends  

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.

[MINI] Primer on Deep Learning  

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

Data Provenance and Reproducibility with Pachyderm  

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

 

 

 

[MINI] Logistic Regression on Audio Data  

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

The figures below are referenced during the episode.

 

 

The top waveform is Linh Da, the bottom is Kyle.  We use the same order below.

Studying Competition and Gender Through Chess  

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.

 

[MINI] Dropout  

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.

 

The Police Data and the Data Driven Justice Initiatives  

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.

The Library Problem  

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.

 
2016 Holiday Special  

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!

[MINI] Entropy  

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.

MS Connect Conference  

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.

Causal Impact  

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/.

[MINI] The Bootstrap  

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.

[MINI] Gini Coefficients  

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.

Unstructured Data for Finance  

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.

[MINI] AdaBoost  

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.

0:00/0:00
Video player is in betaClose