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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
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 diabe
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. Thi
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 a
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 w
This is a re-release of an episode that first ran on January 29, 2017.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.
This episode features Zach Drake, a working data scientist and PhD candidate in the Criminology, Law and Society program at George Mason University. Zach specializes in bringing data science methods to studies of criminal behavior, and got in t
As protests sweep across the United States in the wake of the killing of George Floyd by a Minneapolis police officer, we take a moment to dig into one of the ways that data science perpetuates and amplifies racism in the American criminal just
A message from Ben around algorithmic bias, and how our models are sometimes reflections of ourselves.
This is a re-release of an episode that originally aired on April 1, 2018If 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 architect
This is a re-release of an episode that was originally released on February 26, 2017.When you're estimating something about some object that's a member of a larger group of similar objects (say, the batting average of a baseball player, who
The power of finely-grained, individual-level data comes with a drawback: it compromises the privacy of potentially anyone and everyone in the dataset. Even for de-identified datasets, there can be ways to re-identify the records or otherwise f
What do you get when you combine the causal inference needs of econometrics with the data-driven methodology of machine learning? Usually these two don’t go well together (deriving causal conclusions from naive data methods leads to biased answ
You may not realize it consciously, but beautiful visualizations have rules. The rules are often implict and manifest themselves as expectations about how the data is summarized, presented, and annotated so you can quickly extract the informati
It’s pretty common to fit a function to a dataset when you’re a data scientist. But in many cases, it’s not clear what kind of function might be most appropriate—linear? quadratic? sinusoidal? some combination of these, and perhaps others? Gaus
The abundance of data in healthcare, and the value we could capture from structuring and analyzing that data, is a huge opportunity. It also presents huge challenges. One of the biggest challenges is how, exactly, to do that structuring and ana
AI is evolving incredibly quickly, and thinking now about where it might go next (and how we as a species and a society should be prepared) is critical. Professor Stuart Russell, an AI expert at UC Berkeley, has a formulation for modifications
Most data scientists bounce back and forth regularly between doing analysis in databases using SQL and building and deploying machine learning pipelines in R or python. But if we think ahead a few years, a few visionary researchers are starting
Many of us have the privilege of working from home right now, in an effort to keep ourselves and our family safe and slow the transmission of covid-19. But working from home is an adjustment for many of us, and can hold some challenges compared
Covid-19 is turning the world upside down right now. One thing that’s extremely important to understand, in order to fight it as effectively as possible, is how the virus spreads and especially how much of the spread of the disease comes from c
This week’s episode is a re-release of a recent episode, which we don’t usually do but it seems important for understanding what we can all do to slow the spread of covid-19. In brief, public health measures for infectious diseases get most of
When you need to untangle cause and effect, but you can’t run an experiment, it’s time to get creative. This episode covers difference in differences and synthetic controls, two observational causal inference techniques that researchers have us
This is a re-release of an episode that originally ran on October 21, 2018.The Poisson distribution is a probability distribution function used to for events that happen in time or space. It’s super handy because it’s pretty simple to use and
Recent research into neural networks reveals that sometimes, not all parts of the neural net are equally responsible for the performance of the network overall. Instead, it seems like (in some neural nets, at least) there are smaller subnetwor
Data privacy is a huge issue right now, after years of consumers and users gaining awareness of just how much of their personal data is out there and how companies are using it. Policies like GDPR are imposing more stringent rules on who can us
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