Linear Digressions

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Searching for Datasets with Google Oct 15, 2018

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If you wanted to find a dataset of jokes, how would you do it? What about a dataset of podcast episodes? If your answer was “I’d try Google,” you might have been disappointed—Google is a great search engine for many types of web data, but it didn’t have any special tools to navigate the particular challenges of, well, dataset data. But all that is different now: Google recently announced Google Dataset Search, an effort to unify metadata tagging around datasets and complementary efforts on the search side to recognize and organize datasets in a way that’s useful and intuitive. So whether you’re an academic looking for an economics or physics or biology dataset, or a big old nerd modeling jokes or analyzing podcasts, there’s an exciting new way for you to find data.

It's our fourth birthday Oct 08, 2018

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We started Linear Digressions 4 years ago… this isn’t a technical episode, just two buddies shooting the breeze about something we’ve somehow built together.

Gigantic Searches in Particle Physics Sep 30, 2018

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This week, we’re dusting off the ol’ particle physics PhD to bring you an episode about ambitious new model-agnostic searches for new particles happening at CERN. Traditionally, new particles have been discovered by “targeted searches,” where scientists have a hypothesis about the particle they’re looking for and where it might be found. However, with the huge amounts of data coming out of CERN, a new type of broader search algorithm is starting to be deployed. It’s a strategy that casts a very wide net, looking in many different places at the same time, which also introduces all kinds of interesting questions—even a one-in-a-thousand occurrence happens when you’re looking in many thousands of places.

Data Engineering Sep 24, 2018

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If you’re a data scientist, you know how important it is to keep your data orderly, clean, moving smoothly between different systems, well-documented… there’s a ton of work that goes into building and maintaining databases and data pipelines. This job, that of owner and maintainer of the data being used for analytics, is often the realm of data engineers. From data extraction, transform and loading procedures to the data storage strategy and even the definitions of key data quantities that serve as focal points for a whole organization, data engineers keep the plumbing of data analytics running smoothly.

Text Analysis for Guessing the NYTimes Op-Ed Author Sep 16, 2018

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A very intriguing op-ed was published in the NY Times recently, in which the author (a senior official in the Trump White House) claimed to be a minor saboteur of sorts, acting with his or her colleagues to undermine some of Donald Trump’s worst instincts and tendencies. Pretty stunning, right? So who is the author? It’s a mystery—the op-ed was published anonymously. That hasn’t stopped people from speculating though, and some machine learning on the vocabulary used in the op-ed is one way to get clues.

The Three Types of Data Scientists, and What They Actually Do Sep 09, 2018

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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

Agile Development for Data Scientists, Part 2: Where Modifications Help Aug 26, 2018

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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 1: The Good Aug 19, 2018

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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/

Re - Release: How To Lose At Kaggle Aug 13, 2018

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We've got a classic for you this week as we take a week off for the dog days of summer. See you again next week! 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.

Troubling Trends In Machine Learning Scholarship Aug 06, 2018

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There's a lot of great machine learning papers coming out every day--and, if we're being honest, some papers that are not as great as we'd wish. In some ways this is symptomatic of a field that's growing really quickly, but it's also an artifact of strange incentive structures in academic machine learning, and the fact that sometimes machine learning is just really hard. At the same time, a high quality of academic work is critical for maintaining the reputation of the field, so in this episode we walk through a recent paper that spells out some of the most common shortcomings of academic machine learning papers and what we can do to make things better. Relevant links: https://arxiv.org/abs/1807.03341

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