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    Technology

    Data Science Imposters Podcast

    Explore data science, analytics, big data, machine learning as we discuss these topics

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    Copyright: © Data Science Imposters

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    Latest Episodes:
    The name of the IBM machine that beat Ken Jennings on live TV Mar 19, 2018
    Show notes

    ‘Who is Watson? ‘ … That’s correct for 200. In this episode, we talk about Watson and Jeopardy as we review “Final Jeopardy: Man vs. Machine and the Quest to Know Everything” by Stephen Baker. We also have some fun by throwing in a few Jeopardy answers throughout the show.

    Before we get into that, we talk about March Madness!!! It may be too late for this year, but next year we need to get those data science models in place to make our bracket picks. Is the stock market easier to predict than the NCAA championship?

    Antonio finds this article which explains information gain and entropy but has a hard time explaining it to Jordy. Check out the article and let us know if you could do better: saedsayad.com/decision_tree.htm


    Entrepreneurship has no roadmap – Interview with Jorge Nuñez Feb 25, 2018
    Show notes

    Jorge Nunez is an entrepreneur, investor and as Forbes puts it, an “idea man”. He shares his story of starting his company Remote Reactivation, from early days in event promotions to becoming a player in the world of dentistry. Entrepreneurship has no roadmap and Jorge combines technology, data, and his perseverance to succeed in an otherwise neglected field.

    He leverages relationships, and the benefits of networking. He describes keys to his success, such as understanding who the decision maker is, his belief in the 80-20 rule, and leveraging data to improve his client’s work-life balance.


    Story time: ATMs Spitting Cash, Crypto Paradise in Puerto Rico and Uber Making Deals Feb 12, 2018
    Show notes

    Stories about ATMs Spitting Cash, Crypto Paradise in Puerto Rico and Uber Making Deals

    Antonio reminisces about his high school days and attending 2600 meetings at the Citi Corp building while discussing the ATM hacks or jackpotting.

    Did you know the “whistle” provided in the Captain Crunch cereals could be used to make phone calls?

    Did Antonio find a glitch in the Starbucks Card method?

    …

    Last couple days have been tough on Crypto Currency, but didn’t stop the NYTimes from publishing some information on folks trying to make Puerto Rico a Crypto Utopia.

    Are these Crypto Currency folks going to the Rockefeller’s of our time?

    …

    Did Uber make the right call? They made a deal with a Hacker, and are paying the price for it. Maybe they should have just done a “bug bounty”


    Can you read and understand this better than Alibaba or Microsoft’s AI? If you get sick will you call 311 or Yelp first? Jan 28, 2018
    Show notes

    Microsoft outscores Alibaba which outscores humans in reading and answering questions! Well, the truth is that humans also outscore Microsoft and Alibaba. It depends if you are using the Exact Match (EM) or F1-score scoring methodology. While we discuss some of the technical aspects of this, we do not lose sight of the socio-economic impact that these technologies can have on society.

    In data science, we often try to use other data to gain more insight into a particular problem or situation. In our second segment, we spend some time exploring an article where they use Yelp as a proxy for identifying food borne illnesses in NYC.

    Sources (Segment 1 – Reading)

    • Squad data set – https://rajpurkar.github.io/SQuAD-explorer/
    • https://www.bloomberg.com/news/articles/2018-01-15/alibaba-s-ai-outgunned-humans-in-key-stanford-reading-test
    • https://www.usatoday.com/story/tech/news/2018/01/16/robots-better-reading-than-humans/1036420001/

    Sources (Segment 2 – Yelp as a Proxy)

    • https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocx093/4725036

    Rebooting … Same purpose, different format Jan 15, 2018
    Show notes

    We start the new year with a book review of Peter Thiel’s Zero to One. As always, we add some levity by reading the 1-start reviews on Goodreads. We ask ourselves if there’s a Zero to One idea within Data Science. We come up short. Antonio loves libraries and defends them as one of the best institutions in our country. Do you agree? We finish the episode with a discussion of David Robinsons blog post shared by one of our listeners – thanks Diane!

    Episode has been archived here: http://traffic.libsyn.com/datascienceimposters/DSI_Episode_33.mp3


    Are you misbehaving? Dec 10, 2017
    Show notes

    Imagine that you go into a sportswear store to buy a snowboard to join your friends for a trip in Vermont. You see a used snowboard for $100 and you know the salesperson from high school and they tell you that you can find the exact same thing 15 minutes down the road for $30 dollars less. What would you do? Imagine if the snowboard is $500 instead of $100; would you drive 15 minutes to save $30 dollars? Antonio spent one flight from Dublin to New York and the following week finishing ‘Misbehaving’ by Richard Thaler. Antonio shares what he gained with Jordy.


    Building a Machine Learning Platform – Interview with Dr. David Purdy Nov 26, 2017
    Show notes

    We start this episode addressing the ‘disease’ (our words, not his) of Impatient Data Science and how to cure it with a platform. We are excited to have David share his thoughts and practical experience building machine learning platforms.

    Dr. David Purdy is currently a Senior Data Science Manager at Uber. Most recently, David architected Uber’s Machine Learning Platform and its real-time spatiotemporal forecasting platform which are the basis for driving Uber’s competitive advantage. Throughout his career, David has led the architecture of five such platforms. David holds a PhD in Statistics from UC Berkeley, and his career in data science and machine learning spans multiple industries including: finance, personalized medicine, transportation, and web search.

    One idea that resonates well with us is the thought that you go from zero to something and iterate between something and the nth somethings until you get it right.

    The featured image is attributable to: https://en.wikipedia.org/wiki/User:Midnightblueowl

    If you are interested in more details about Uber’s Michelangelo Machine Learning Platform, you can visit here: https://eng.uber.com/michelangelo/ In addition, you can send us any questions that you may have.

    The image on the left comes from: http://www.accademia.org/explore-museum/artworks/michelangelos-david/


    Recalling the last 30 episodes Oct 31, 2017
    Show notes

    We invite a friend to join us this week (actually two weeks ago) to relive some of our favorite episodes. We invite you to join this casual conversation.

    Next week we are excited to have a special guest on the show to discuss his experience building Machine Learning Platforms.


    We got an IDEA, actually we got lots of ideas – Part II @RPI Oct 26, 2017
    Show notes

    In this episode, Dr. Bennett takes us back to school and teaches us a few things about machine learning, artificial intelligence, data analytics, and visualization. Along the way, we discuss how to incorporate teaching of these topics in colleges and high schools and some of the moral issues that may arise with artificial intelligence.

    ‘Dr. Kristin Bennett is the Associate Director of the Institute for Data Exploration and Application and a Professor in the Mathematical Sciences and Computer Science Departments at Rensselaer Polytechnic Institute. Her research focuses on extracting information from data using novel predictive or descriptive mathematical models and data visualizations … to support decision making … in science, engineering, public health and business. She has 25 years of experience and over 100 publications in these areas.’ Read more about Dr. Bennett here.


    On the road: @RPI Homecoming 2017 (Part I) Oct 22, 2017
    Show notes

    We return to Rensselaer Polytechnic Institute (RPI) for the 2017 Homecoming Weekend. We share our experience and reminisce about good ol’ RPI. This episode is less structured and less ‘data-sciencey’ than most of our other episodes. We hope you enjoy this casual episode and tune back to Part II when we jump back into the depths of data science …

    Alma Mater

    Here’s to old R.P.I. Her fame may never die,
    Here’s to old Rensselaer, she stands today without a peer,
    Here’s to those olden days,
    Here’s to those golden days,
    Here’s to the friends, we made at dear old R.P.I.


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