Data Science Weekly Newsletter - Issue 310

Issue #310

Oct 31 2019

Editor Picks
 
  • Rising Seas Are Going to Drown Way More Cities Than We’d Thought: Study
    In a new study published by the journal Nature Communications, scientists affiliated with the organization Climate Central and Princeton University detail previous methodological problems, then use artificial intelligence to determine — and correct for — the previous literature’s error rate. Their research yields some eye-popping (or stomach turning) updates to our conventional understanding of what the next century has in store for our coastlines...
 
 

A Message from this week's Sponsor:

 

 
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Data Science Articles & Videos

 
  • A robot puppet can learn to walk if it’s hooked up to human legs
    Humans don’t need to have seen a set of stairs before in order to know what it is—or how to climb them. But for a robot, they can present an insurmountable problem. Getting robots to mimic how we manage to move around so effortlessly is one potential solution. That’s the premise of a study by researchers from the University of Illinois and MIT published in Science Robotics today...
  • Coding habits for data scientists
    In this article, we’ll share techniques for identifying bad habits that add to complexity in code as well as habits that can help us partition complexity...
  • Learning Data Manipulation for Augmentation and Weighting
    Manipulating data, such as weighting data examples or augmenting with new instances, has been increasingly used to improve model training. Previous work has studied various rule- or learning-based approaches designed for specific types of data manipulation. In this work, we propose a new method that supports learning different manipulation schemes with the same gradient-based algorithm...
  • The First Step To Take When Looking For A Data Science Job
    As someone new to the field, it all looks very chaotic and confusing to you. You want to find the right job for you and don't want to waste your time trying to apply to every single company advertising a job. Sadly, all the advice you are getting seems to be do a bunch of things that appear to be disconnected and sometimes even contradictory - network, blog, take these 4 MOOC's, talk to recruiters, don't talk to recruiters, participate in Kaggle competitions, do a bootcamp, join a data science fellowship program, etc... Given a limited amount of time and wanting to find the right opportunity, where do you start?...

 

Data Platform*

 

 
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Jobs

  • Data Scientist - Datadog - NYC

    At Datadog, we’re on a mission to build the best monitoring platform in the world. We operate at high scale—trillions of data points per day—and high availability, providing always-on alerting, visualization, and tracing for our customers' infrastructure and applications around the globe.

    Our engineering culture values pragmatism, honesty, and simplicity to solve hard problems the right way. We need you to design and build machine learning-powered products that help our customers learn from their data and make better decisions in real-time....

        Want to post a job here? Email us for details >> team@datascienceweekly.org
 

 

Training & Resources


  • μPlot: An exceptionally fast, tiny (~10 KB min) time series chart
    μPlot is a fast, memory-efficient time series chart based on Canvas 2D; from a cold start it can create an interactive chart containing 150,000 data points in 40ms. In addition to fast initial render, the zooming and cursor performance is by far the best of any similar charting lib; at ~10 KB, it's likely the smallest and fastest time series plotter that doesn't make use of WebGL shaders or WASM, both of which have much higher startup cost and code size...
  • TensorFlowLite
    A library for using TensorFlow Lite for Microcontrollers with Particle devices...

 

Books

 

  • The Lady Tasting Tea:
    How Statistics Revolutionized Science in the Twentieth Century

    An insightful, revealing history of how mathematics transformed our world...

    "I have taken courses in statistics, taught it many times and solved several statistical problems that have appeared in journals. But until I read this book, I never really thought about it in so deep and philosophical a manner..."


    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
     


    P.S., Enjoy the newsletter? Please forward it to your friends and colleagues - we'd love to have them onboard :) All the best, Hannah & Sebastian
 
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