Now we’ve recovered from over indulging in Boston’s culinary delights, we’re ready to share our highlights from this year’s EARL Boston Conference.
Day 1 highlights
Stack Overflow’s David Robinson kicked off the Conference, using Stack Overflow data to perform all sorts of interesting analyses. Highlights included trends in questions mentioning specific R packages over time, leading to the identification of rival R packages. We found that R is the least disliked language (because it’s the best obviously!); although David cautioned that often people who haven’t used R before haven’t heard of it either.
Richie Cotton’s talk on how DataCamp is a ‘data-inspired’ organisation was particularly entertaining and he was a really engaging speaker. It was also great to hear from Emily Riederer about tidycf; she shared a really good example of the type of data-driven revolution taking place in many financial institutions.
We also enjoyed Patrick Turgeon’s presentation on Quantitative Trading with R. His presentation portrayed quantitative trading as a scientific problem to be investigated using diverse sources of information, open source code and hard work. Free from jargon, Patrick demonstrated that placing bets on the markets does not have to be some mysterious art, but an analytic puzzle like any other.
A brilliant first day was rounded out with an evening reception overlooking the Charles River, where we enjoyed drinks and a chance to catch up with everyone at the Conference. It was a great opportunity to chat with all the attendees to find out what talks they enjoyed and what ones they wanted to catch on day two.
Day 2 highlights
Mara Averick got things moving on day two with a witty and humble keynote talk on the importance of good communication in data science. She may have also confessed to getting R banned in her fantasy basketball league. From having to argue with the internet that Krieger’s most common distinctive phrase is “yep yep”, to always having the correct word for any situation, she gave a fantastic presentation; a key skill for any data scientist (even if she says she isn’t one!).
Keeping up the theme of great communication in data science, Ali Zaidi gave a really clear rundown of what deep learning is, and how existing models can be reused to make pattern recognition practicably applicable even with modest hardware.
Other highlights included both Alex Albright’s and Monika Wahi’s talks. Alex showed us lots of enjoyable small analyses and suggested ideas for finding fun datasets and encouraged us all to share our findings when experimenting. Monika Wahi discussed why SAS still dominates in healthcare and how we can convert people to R. She talked about how R is easier and nicer to read and showed us equivalent code in SAS and R to illustrate her point.
It was tough, but we picked just a few of the many highlights from both days at EARL. Please tweet @EARLConf any of your EARL highlights so we can share them – we would love to see what people enjoyed the most.
We’d like to thank all of our attendees for joining us, our fantastic speakers, and our generous sponsors for making EARL Boston the success it has been.
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You can now find speaker slides (where available) on the EARL website – just click on the speaker profile and download the file.
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