linear digression

Linear Digressions

Competing in a machine learning competition on Kaggle is a kind of rite of passage for data scientists.  Losing unexpectedly at the very end of the contest is also something that a lot of us have experienced.  It's not just bad luck: a very specific combination of overfitting on popular competitions can take someone who is in the top few spots in the final days of a contest and bump them down hundreds of slots in the final tally.

How to Lose at Kaggle

This week: everybody's favorite WWII-era classifier metric!  But it's not just for winning wars, it's a fantastic go-to metric for all your classifier quality needs.

Rock the ROC Curve

This week's episode is just in time for JupyterCon in NYC, August 22-25...

Jupyter notebooks are probably familiar to a lot of data nerds out there as a great open-source tool for exploring data, doing quick visualizations, and packaging code snippets with explanations for sharing your work with others.  If you're not a data person, or you are but you haven't tried out Jupyter notebooks yet, here's your nudge to go give them a try.  In this episode we'll go back to the old days, before notebooks, and talk about all the ways that data scientists like to work that wasn't particularly well-suited to the command line + text editor setup, and talk about how notebooks have evolved over their lifetime to become even more powerful and well-suited to the data scientist's workflow.

Jupyter Notebooks

As data science matures as a field, it's becoming clearer what attributes a data science team needs to have to elevate their work to the next level.  Most of our episodes are about the cool work being done by other people, but this one summarizes some thinking Katie's been doing herself around how to guide data science teams toward more mature, effective practices.  We'll go through five key characteristics of great data science teams, which we collectively refer to as "disciplined data science," and why they matter.

Disciplined Data Science

The Fourier transform is one of the handiest tools in signal processing for dealing with periodic time series data. Using a Fourier transform, you can break apart a complex periodic function into a bunch of sine and cosine waves, and figure out what the amplitude, frequency and offset of those component waves are. It's a really handy way of re-expressing periodic data--you'll never look at a time series graph the same way again.

The Fourier Transform

Jeff Dean and his collaborators at Google are turning the machine learning world upside down (again) with a recent paper about how machine learning models can be used as surprisingly effective substitutes for classic data structures. In this first part of a two-part series, we'll go through a data structure called b-trees. The structural form of b-trees make them efficient for searching, but if you squint at a b-tree and look at it a little bit sideways then the search functionality starts to look a little bit like a regression model--hence the relevance of machine learning models. If this sounds kinda weird, or we lost you at b-tree, don't worry--lots more details in the episode itself.

The Case for Learned Index Structures, Part 1: B-Trees

One interesting trend we've noted recently is the proliferation of papers, articles and blog posts about data science that don't just tell the result--they include data and code that allow anyone to repeat the analysis. It's far from universal (for a timely counterpoint, read this article <http://www.sciencemag.org/news/2018/02/missing-data-hinder-replication-artificial-intelligence-studies>), but we seem to be moving toward a new normal where data science conclusions are expected to be shown, not just told.

Relevant links:
https://github.com/fivethirtyeight/data
https://blog.patricktriest.com/police-data-python/

Open Data and Open Science

A few weeks ago, we podcasted about a neural network that was being touted as "better than doctors" in diagnosing pneumonia from chest x-rays, and how the underlying dataset used to train the algorithm raised some serious questions. We're back again this week with further developments, as the author of the original blog post pointed us toward more developments. All in all, there's a lot more clarity now around how the authors arrived at their original "better than doctors" claim, and a number of adjustments and improvements as the original result was de/re-constructed. 

Anyway, there are a few things that are cool about this. First, it's a worthwhile follow-up to a popular recent episode. Second, it goes *inside* an analysis to see what things like imbalanced classes, outliers, and (possible) signal leakage can do to real science. And last, it raises a really interesting question in an age when computers are often claimed to be better than humans: what do those claims really mean?

Relevant links:
https://lukeoakdenrayner.wordpress.com/2018/01/24/chexnet-an-in-depth-review/

What makes a machine learning algorithm "superhuman"?

After all the back-patting around making data science datasets and code more openly available, we figured it was time to also dump a bucket of cold water on everyone's heads and talk about the things that can go wrong when data and code is a little too open. 

In this episode, we'll talk about two interesting recent examples: a de-identified medical dataset in Australia that was re-identified so specific celebrities and athletes could be matched to their medical records, and a series of military bases that were spotted in a public fitness tracker dataset.

When Private Data Isn't Private Anymore

This week's episodes is for data scientists, sure, but also for data science managers and executives at companies with data science teams. These folks all think very differently about the same question: what should a data science team be working on? And how should that decision be made? That's the subject of a talk that I (Katie) gave at Strata Data in early March, about how my co-department head and I select projects for our team to work on.

We have several goals in data science project selection at Civis Analytics (where I work), which can be summarized under "balance the best attributes of bottom-up and top-down decision-making." We achieve this balance, or at least get pretty close, using a process we've come to call the Idea Factory (after a great book about Bell Labs). This talk is about that process, how it works in the real world of a data science company and how we see it working in the data science programs of other companies.

Relevant links: https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/63905

How to pick projects for a professional data science team

If you've done image recognition or computer vision tasks with a neural network, you've probably used a convolutional neural net. This episode is all about the architecture and implementation details of convolutional networks, and the tricks that make them so good at image tasks.

Convolutional Neural Nets

Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.

https://hdsr.mitpress.mit.edu/pub/xsrt4zs2/release/2

The Data Science Open Source Ecosystem

This is a re-release of an episode that originally ran in October 2019.

If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that use cases. You also wouldn’t be YouTube, which found themselves with this problem (gigantic data needs and several very different use cases of what they needed to do with that data) and went a different way: they built one analytics data system to serve them all. Procella, the system they built, is the topic of our episode today: by deconstructing the system, we dig into the four motivating uses of this system, the complexity they had to introduce to service all four uses simultaneously, and the impressive engineering that has to go into building something that “just works.”

Procella: YouTube's super-system for analytics data storage

A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied with data science methods, should get in touch to come talk to our audience about their work. This week we’re excited to bring on Todd Hendricks, Bay Area data scientist and a volunteer who reached out to tell us about his studies with the Stanford Open Policing dataset.

A Data Science Take on Open Policing Data

The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that classifies liver cancer, and asks the question—are patients now getting better care, and achieving better outcomes, with these algorithms in the mix? The answer isn’t no, exactly, but it’s not a resounding yes, because these algorithms interact with a very complex system (the healthcare system) and other shortcomings of that system are proving hard to automate away. Getting a faster diagnosis from an image might not be an improvement if the image is now harder to capture (because of strict data quality requirements associated with the algorithm that wouldn’t stop a human doing the same job). Likewise, an algorithm getting a prediction mostly correct might not be an overall benefit if it introduces more dramatic failures when the prediction happens to be wrong. For every data scientist whose work is deployed into some kind of product, and is being used to solve real-world problems, these papers underscore how important and difficult it is to consider all the context around those problems.

A Reality Check on AI-Driven Medical Assistants

All good things must come to an end, including this podcast. This is the last episode we plan to release, and it doesn’t cover data science—it’s mostly reminiscing, thanking our wonderful audience (that’s you!), and marveling at how this thing that started out as a side project grew into a huge part of our lives for over 5 years.

It’s been a ride, and a real pleasure and privilege to talk to you each week. Thanks, best wishes, and good night!

—Katie and Ben