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Adversarial Learning

Author: @joelgrus + @akm

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@akm and @joelgrus talk about data, science, data science, Dave Shing, and whatever else we feel like
19 Episodes
On this episode, Andrew is joined by Joel Grus (@joelgrus), the author of Data Science from Scratch, whose second edition just came out. They discuss spreadsheets, writing a book, Python 3, type annotations, Jupyter notebooks, reproducibility, and what it's like to do standup comedy at a conference that has a code of conduct. Please listen to it.
Our guest this episode is data scientist Peadar Coyle. Topics include Brexit whether Europe is a Python continent or an R continent GDPR how data science is different in Europe than it is in the USA Joel's stock rant against "domain expertise" PyMC3 and Bayesian Analysis teaching online courses whether "primer" is pronounced "primer" or "primer" what Joel and Andrew are most excited about in data science Please listen to it.
Episode 17: Josh Wills

Episode 17: Josh Wills


Adversarial Learning is back from hiatus! Our guest is famous data scientist Josh Wills. We discuss why Josh is a famous data scientist, what it's like working at Slack, data science conferences, NLP's "imagenet moment", whether Joel should remove the MapReduce chapter from the 2nd edition of Data Science from Scratch, and which is the best Rush album. Please listen to it.
Adversarial Learning is back! In this long-delayed episode (thanks, technical difficulties) we are joined by data scientist Schaun Wheeler to discuss our favorite topic, data ethics. Highlights include: * Schaun's Medium post "An ethical code can’t be about ethics" * Do we need a "Hippocratic Oath" for data science * How to hire data scientists who won't steal people's kidneys * Why Joel has a Values Mug * The Manifesto for Data Practices * Is this all secretly a competency problem? * Skin in the Game * Are data ethics issues really just business ethics issues? Please listen to it!  (More episodes coming soon!)
Our guest this week is data scientist for good Lisa Green.  Topics of discussion include What is ethics  Joel's previous life as a financial analyst and the ethical dilemmas therein whether there's anything incriminating in Joel's Yahoo history "identity theft" as a bullshit concept  Google's corrupt bargain with the NHS what the medical code of ethics actually says  polycentric ethics the difference between unethical and incompetent what good a code of ethics does when the "ethical" problems are emergent from the choices of many people that terrible article about the Seattle Nazi convention neuroticism  the Joel test  vgr's bad tweet Please listen to it.
Our guest this week is flashcard kingpin and former Partially Derivative co-host Chris Albon. Topics of discussion include how good it feels not to have a podcast machine learning flashcards being a "natsec bro" whether Chris would punch a Nazi whether Chris would sexually harass a Nazi whether "magister" is a good woke replacement for "master" whether that Andrew Ng job posting is appropriate and whether any of us would apply for it killing your heroes having a day a week without social media treadmill desks Chris's next podcast and somehow Joel gets going on Harry Potter Please listen to it.
Episode 13: Back to School

Episode 13: Back to School


It's Back to School time at Adversarial Learning! topics of discussion include OUR SPONSOR: the Metis Demystifying Data Science Conference (at which Joel is speaking, please listen to it) Sudbury education John Holt times tables whether textbook piracy is the new stealing from the library Neil Tyson's "In School" cycle of tweets how to teach curiosity why math is a "hard" skill and people skils are "soft" skills when factorizing matrices is easy and dealing with people is hard whether and how our schools should be producing more data scientists Please listen to it.
Data scientist Vicki Boykis joins Joel and Andrew to variously debunk and rebunk common Data Science Myths. Is data the new oil? Do data scientists spend 80% of their time munging and cleaning data? What happens if you look in the mirror and say "data science" five times? And many, many, many more. Please listen to it.
Episode 11: Data Conferences

Episode 11: Data Conferences


Andrew and Joel come out of hiatus to discuss data conferences: when to attend them, how to get your talk accepted, how to network, optimal heckling strategies, where to stay, and so on. Somehow they also end up talking about fidget spinners, the "objective" section on resumes, the right way to use LinkedIn, why Andrew doesn't think much of data science bootcamps, and why Joel can't convince any data science bootcamps to sponsor the podcast. Please listen to it.
Friend of the podcast Tim Hopper joins us as we share stories of Very Bad Interviews we've been on. (As you probably expect, Joel has the most humiliating stories.) If you've ever gone on a terrible interview, listen and commiserate. If you've never gone on a terrible interview, listen and live vicariously. Halfway through, Andrew's Internet flakes out and his part stops getting recorded. Thanks to the magic of editing, you'll hardly even notice!
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