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Zipfian Academy: Data Science Dos & Don'ts

Data Science is a young and effervescent field that holds much promise for solving the 21st century's biggest problems.  While more people are starting to recognize the power of data driven analyses, much of the insight of how to build scalable, consistent, and repeatable systems remains siloed in large organizations.  In this whirlwind tour de force of data science, Jonathan will highlight data science best practices and the gotchas to avoid by walking you through building a robust machine learning pipeline.  He will cover how (and why) to measure everything, choosing the right ML algorithm for the question at hand (and the tradeoffs of each), scaling algorithms with MapReduce to run on all your data, and how to productionize your model to get predictions in realtime.  After all... there is no need to invent the future if you can predict it.


About the Speaker:

Jonathan is the co-founder of Zipfian Academy, an immersive 12-week training program aimed at creating the next generation of data scientists through a hands-on project based curriculum.  He first discovered his love of all things data while studying Computer Science and Physics at UC Berkeley. In a former life, he worked for Alpine Data Labs developing distributed machine learning algorithms for predictive analytics on Hadoop.

Jonathan has always had a passion for sharing the things he has learned in the most creative ways he can. He has been a mentor at Dev Bootcamp, taught classes at General Assembly, and was an instructor/curriculum at Hack Reactor where he combined his two favorite things: humans and code.


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  • Declan H.

    Are the slides available ?

    August 16, 2013

  • Mathias P.

    The talk teemed of buzzwords but lacked actual content. Even worse: some of the statements where simply wrong. E.g. performance and scalability are NOT the same thing and it is NOT ok, to just change the ML algorithm instead of understanding what's in the data. Mentioning frameworks like Hadoop, Spark etc. or techniques like using unit tests just made no sense (btw. I'm a TDD person). My Advice: focus on a simple but common problem, show "do's & don'ts" there and tell something. I didn't mean to flame and tried to focus on objective things, so don't take it personal, please.

    2 · August 19, 2013

    • A former member
      A former member

      Thanks for the feedback Mathias, it really helps. With a topic as broad as Data Science, I often struggle with how technical each aspect should be and how much detail to go into for presentations. And every audience is different, but this seemed to be a very knowledgable crowd and I apologize if the content was very high level. I am happy to discuss anything I covered in the presentation with anyone or go into much more depth. Thanks to Open Table for hosting and if any one has any questions on anything covered feel free to reach out over email: [masked]

      2 · August 24, 2013

    • Matt J.

      My general feeling on depth for these talks: go deep and let people catch up or understand on their own time. I'm not immersed in data science work on a daily basis, and I definitely prefer talks that are over my head rather than high level. Technical is good.

      August 24, 2013

  • Gabriela de Q.

    Thanks Open Table for hosting. Thanks Arshak for organizing it and for inviting Jonathan. The presentation was very good!! I always like to learn something new.

    August 19, 2013

  • jing

    Thanks to Open Table for hosting. Awesome space.

    August 17, 2013

  • Frank C.

    Good presentation, Johnathan. Seems like from the talk that doing good data science is much like doing good software engineering, abstraction, modular, agile, iteration, problem solving..., etc. Looking forward for someone to write The Mythical Man-Month of Data Science of a book. Thank you Open Table for hosting it on a Friday night!

    August 17, 2013

  • Zach

    Thanks to Open Table for generously hosting the event.

    1 · August 17, 2013

  • Edward M.

    Talk was a little longer than needed, in my opinion

    August 16, 2013

  • Yuri

    Will the talk start at 6:30, or will there be some networking time before the talk itself starts?

    1 · August 13, 2013

    • Jason R.

      I am also interested in the answer to this question

      August 14, 2013

    • Arshak N.

      We usually allow 30 mins for food, network and start at 7

      August 16, 2013

  • Var

    Any plans for Live streaming or recording?

    2 · August 15, 2013

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