Running experiments when there are network effects
Traditional A/B tests assume that whether or not one person got a treatment has no effect on the experiment outcome for another person. But that’s not a safe assumption, especially when there are netw...
27 Tammi 202024min
Zeroing in on what makes adversarial examples possible
Adversarial examples are really, really weird: pictures of penguins that get classified with high certainty by machine learning algorithms as drumsets, or random noise labeled as pandas, or any one of...
20 Tammi 202022min
Unsupervised Dimensionality Reduction: UMAP vs t-SNE
Dimensionality reduction redux: this episode covers UMAP, an unsupervised algorithm designed to make high-dimensional data easier to visualize, cluster, etc. It’s similar to t-SNE but has some advanta...
13 Tammi 202029min
Data scientists: beware of simple metrics
Picking a metric for a problem means defining how you’ll measure success in solving that problem. Which sounds important, because it is, but oftentimes new data scientists only get experience with a f...
5 Tammi 202024min
Communicating data science, from academia to industry
For something as multifaceted and ill-defined as data science, communication and sharing best practices across the field can be extremely valuable but also extremely, well, multifaceted and ill-define...
30 Joulu 201926min
Optimizing for the short-term vs. the long-term
When data scientists run experiments, like A/B tests, it’s really easy to plan on a period of a few days to a few weeks for collecting data. The thing is, the change that’s being evaluated might have ...
23 Joulu 201919min
Interview with Prof. Andrew Lo, on using data science to inform complex business decisions
This episode features Prof. Andrew Lo, the author of a paper that we discussed recently on Linear Digressions, in which Prof. Lo uses data to predict whether a medicine in the development pipeline wil...
16 Joulu 201927min
Using machine learning to predict drug approvals
One of the hottest areas in data science and machine learning right now is healthcare: the size of the healthcare industry, the amount of data it generates, and the myriad improvements possible in the...
8 Joulu 201925min
