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Linear Digressions

Data

The Medici were one of the great ruling families of Europe during the Renaissance.  How did they come to rule?  Not power, or money, or armies, but through the strength of their social network.  And speaking of great historical social networks, analysis of the network of letter-writing during the Enlightenment is helping humanities scholars track the dispersion of great ideas across the world during that time, from Voltaire to Benjamin Franklin and everyone in between.

Relevant links:
https://www2.bc.edu/~jonescq/mb851/Mar12/PadgettAnsell_AJS_1993.pdf
http://republicofletters.stanford.edu/index.html

Great Social Networks in History

Survival analysis is all about studying how long until an event occurs--it's used in marketing to study how long a customer stays with a service, in epidemiology to estimate the duration of survival of a patient with some illness, and in social science to understand how the characteristics of a war inform how long the war goes on.  This episode talks about the special challenges associated with survival analysis, and the tools that (data) scientists use to answer all kinds of duration-related questions.

Survival Analysis

First up in this episode: a crash course in natural language processing, and important steps if you want to use machine learning techniques on text data.  Then we'll take that NLP know-how and talk about a really cool analysis of State of the Union text, which analyzes the topics and word choices of every President from Washington to Obama.

Relevant link:
https://civisanalytics.com/blog/data-science/2016/01/15/data-science-on-state-of-the-union-addresses/

Text Analysis on the State Of The Union

"People with a family history of heart disease are more likely to eat healthy foods, and have a high incidence of heart attacks."  Did the healthy food cause the heart attacks?  Probably not.  But establishing causal links is extremely tricky, and extremely important to get right if you're trying to help students, test new medicines, or just optimize a website.  In this episode, we'll unpack randomized experiments, like AB tests, and maybe you'll be smarter as a result.  Will you be smarter BECAUSE of this episode?  Well, tough to say for sure...

Relevant link:
http://tylervigen.com/spurious-correlations

Experiments and Messy, Tricky Causality

This episode started out as a discussion of recommendation engines, like Netflix uses to suggest movies.  There's still a lot of that in here.  But a related topic, which is both interesting and important, is how to keep data private in the era of large-scale recommendation engines--what mistakes have been made surrounding supposedly anonymized data, how data ends up de-anonymized, and why it matters for you.

Relevant links:
http://www.netflixprize.com/
http://bits.blogs.nytimes.com/2010/03/12/netflix-cancels-contest-plans-and-settles-suit/?_r=0
http://arxiv.org/PS_cache/cs/pdf/0610/0610105v2.pdf

Recommendation Engines and Privacy

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

If you're a data scientist at a firm that does a lot of software building, chances are good that you've seen or heard engineers sometimes talking about "agile software development." If you don't work at a software firm, agile practices might be newer to you. In either case, we wanted to go through a great series of blog posts about some of the practices from agile that are relevant for how data scientists work, in hopes of inspiring some transfer learning from software development to data science.

Relevant links:
https://www.locallyoptimistic.com/post/agile-analytics-p1/
https://www.locallyoptimistic.com/post/agile-analytics-p2/
https://www.locallyoptimistic.com/post/agile-analytics-p3/

Agile Development for Data Scientists, Part 1: The Good

There's just too much interesting stuff at the intersection of agile software development and data science for us to be able to cover it all in one episode, so this week we're picking up where we left off last time. We'll give a quick overview of agile for those who missed last week or still have some questions, and then cover some of the aspects of agile that don't work well out-of-the-box when applied to data analytics. Fortunately, though, there are some straightforward modifications to agile that make it work really nicely for data analytics!

Relevant links:
https://www.agilealliance.org/agile101/12-principles-behind-the-agile-manifesto/
https://www.locallyoptimistic.com/post/agile-analytics-p1/
https://www.locallyoptimistic.com/post/agile-analytics-p2/
https://www.locallyoptimistic.com/post/agile-analytics-p3/

Agile Development for Data Scientists, Part 2: Where Modifications Help

If you've been in data science for more than a year or two, chances are you've noticed changes in the field as it's grown and matured. And if you're newer to the field, you may feel like there's a disconnect between lots of different stories about what data scientists should know, or do, or expect from their job. This week, we cover two thought pieces, one that arose from interviews with 35(!) data scientists speaking about what their jobs actually are (and aren't), and one from the head of data science at AirBnb organizing core data science work into three main specialties.

Relevant links:
https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists
https://www.linkedin.com/pulse/one-data-science-job-doesnt-fit-all-elena-grewal

The Three Types of Data Scientists, and What They Actually Do

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