Data Science Weekly Newsletter - Issue 115

Issue #115

February 4 2016

Editor Picks
 
  • What it feels like to drive a Tesla on autopilot
    I was in the driver’s seat of the Tesla Model S, but I wasn’t really driving. My hands weren’t on the steering wheel. My feet weren’t on the pedals. Software and sensors were doing the real work. I had been reduced to a back-up system. I monitored the city traffic mostly out of habit. I changed lanes with a flick of the blinker, my blind spot checked for me and the Tesla deciding when to move on its own...
 
 

A Message from this week's Sponsor:

 

 
  • Applied Data Science Webinar & Yhat Demo

    Yhat (pronounced Y-hat) provides an unparalleled platform for predictive analytics and decision management.

    Join Yhat Cofounder Austin Ogilvie next Thursday, February 11, at 1 PM EST, for a webinar about Applied Data Science. Austin will discuss the data science lifecycle from insight to prototype to production application. He’ll answer the question of what to do with predictive models once they’re built and show a live demo of the Yhat platform.

    Get your invite here!
     

 

Data Science Articles & Videos

 
  • Will Machines Eliminate Us?
    People who worry that we’re on course to invent dangerously intelligent machines are misunderstanding the state of computer science...
  • Google AI versus the Go grandmaster – who is the real winner?
    The achievement is being hailed as a breakthrough in understanding human intelligence, and a large step towards emulating it. However, so was Deep Blue’s achievement when it first beat chess world champion, Gary Kasparov, nearly 20 years ago. So where does this latest success really bring us?...
  • The role of model interpretability in data science
    In data science, models can involve abstract features in high dimensional spaces, or they can be more concrete, in lower dimensions, and more readily understood by humans; that is, they are interpretable. What’s the role of interpretable models in data science, especially when working with less technical partners from the business? When, and why, should we favor model interpretability?...
  • How convolutional neural networks see the world
    In this post, we take a look at what deep convolutional neural networks (convnets) really learn, and how they understand the images we feed them. We will use Keras to visualize inputs that maximize the activation of the filters in different layers of the VGG16 architecture, trained on ImageNet...
  • Machine Learning Meets Economics
    Machine learning techniques are increasingly being used to make such processes more efficient: image processing to flag bad parts, text analysis to surface good candidates, spam filtering to sort email, fraud detection to lower transaction costs etc. In this article, I show how you can take business factors into account when using machine learning to solve these kinds of problems with binary classifiers...
 
 

Jobs

 
  • Data Scientist - Meetup - New York

    Meetup’s Strategy Team partners with all teams throughout the company at all levels of management to identify and execute on the most impactful product and business opportunities that will help us achieve our mission of a Meetup Everywhere about Most Everything (MEME). We are looking for a data scientist who is passionate about data and analytics to contribute to building out our new reporting platform that is the foundation of our team’s efforts to supply data driven insights about almost everything at Meetup to everyone at Meetup...
 
 

Training & Resources

 
  • A Tutorial on Python
    Why use Python for Data Science? Python has surprising capabilities in data analysis and data visualization thanks to the new generation of packages being created. Here is a brief tutorial in Pythonic Data Science...
  • Using PostgresSQL in R: A quick how-to
    The combination of R plus SQL offers an attractive way to work with what we call medium-scale data: data that's perhaps too large to gracefully work with in its entirety within your favorite desktop analysis tool (whether that be R or Excel), but too small to justify the overhead of big data infrastructure...
 
 

Books

 

  • How to Measure Anything: Finding the Value of "Intangibles" in Business

    Explanation of how to measure those things in your own business that, until now, you may have considered "immeasurable"

    "An excellent read. It could be summed up as a "basic statistics for business" book, although it definitely goes beyond that in many aspects...."

    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
 
 
P.S. Interested in reaching fellow readers of this newsletter? Consider sponsoring! Email us for details :) - All the best, Hannah & Sebastian
 
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