It’s that time of year, the snow has melted, flowers are starting to blossom, and the country is consumed by the fever of March Madness. Today, March 15th, marks the true kick-off of the 63-match tournament, famous for its thrilling competitive play and heart-stopping upsets. The tourney has a... Read more
Prediction in the public sector: Why the government needs predictive analytics
Data can appear lifeless and dull on the surface – especially government data – but the thought of it should actually get you excited. Data is a very interesting and powerful thing. First off, data is exactly the stuff we bother to write down – and for good reason.... Read more
What’s the healthiest city in the US? (and what does that even mean?)
(Spoiler alert: it’s not Detroit, and it’d be a relatively simple question if it weren’t for cancer.) The human body has multiple ways of not working well; at its broadest, the CDC’s 500 Cities project data set lists fourteen different health outcomes, from arthritis, to stroke, to the wonderfully... Read more
The endlessly fun NBA All-Star Weekend just wrapped up, culminating in a 148-145 triumph for Team Lebron over Team Steph. But now that the fun is over, it’s time to turn our attention to more a serious matter this NBA season, and that is the MVP race. With the... Read more
Jack Kwok is a Software Engineer with 15 years of professional experience. At Insight, he built a Deep Learning solution to automatically detect flooded roads during natural disasters. He is now a Software Engineer at Lyft working with Machine Learning and Deep Learning. Want to learn applied Artificial Intelligence... Read more
Machine Learning vs. Statistics
This was originally posted on the Silicon Valley Data Science blog was co-written by Drew Hardin   The Texas Death Match of Data Science. Throughout its history, Machine Learning (ML) has coexisted with Statistics uneasily, like an ex-boyfriend accidentally seated with the groom’s family at a wedding reception: both uncertain where to lead... Read more
Ethics for powerful algorithms (3 of 4)
(Hi, all! Apologies for the long radio silence — my day job has been all-consuming. For those of you joining us for the first time, this series is about the controversies/risks/concerns around using algorithms in the criminal justice system. You might want to check out my first post here, and the second post... Read more
In a recent post, I offered a definition of the distinction between data science and machine learning: that data science is focused on extracting insights, while machine learning is interested in making predictions. I also noted that the two fields greatly overlap: I use both machine learning and data science... Read more
What’s the difference between data science, machine learning, and artificial intelligence?
When I introduce myself as a data scientist, I often get questions like “What’s the difference between that and machine learning?” or “Does that mean you work on artificial intelligence?” I’ve responded enough times that my answer easily qualifies for my “rule of three”: When you’ve written the same... Read more
Ethics for powerful algorithms (2 of 4)
This is the second of four articles on the ethics of powerful algorithms, taking COMPAS as a case study. Our story so far: COMPAS is an algorithm used widely today to predict which criminals are most likely to commit future crimes. Investigative journalists at ProPublica recently published a study claiming that COMPAS... Read more