Data Science Weekly Newsletter - Issue 307

Issue #307

Oct 10 2019

Editor Picks

A Message from this week's Sponsor:


Become a Data Analyst with Thinkful

The Data Analytics program is for people who are starting from the very beginning. Learn how to scrape, collect and analyze data, use SQL and Tableau, and get an introduction to Python. We'll get you a job within six months of graduating or you'll get your tuition back.

Data Science Articles & Videos

  • AI Deserts
    Been thinking about AI in government with a bit of a contrary take. I'm worried that AI just widens the gap between the public and private sectors in ways we're not talking about enough...
  • Open Set Medical Diagnosis
    ML medical diagnosis models will be exposed to conditions they haven't been trained on when deployed. How do you address this? See our work to be presented at NeurIPS...
  • The dumb reason your fancy Computer Vision app isn’t working:
    Exif Orientation

    In my experience, there is one technical problem that trips people up more often than any other. No, it’s not a complicated theoretical issue or an issue with expensive GPUs. It’s the fact that almost everyone is loading their images into memory sideways without even knowing it. And computers are less than excellent at detecting objects or identifying faces in sideways images...
  • Machine learning edge devices: Benchmark report
    In this report, we’ll benchmark five novel edge devices, using different frameworks and models, to see which combinations perform best. In particular, we’ll focus on performance outcomes for machine learning on the edge...
  • The First Step To Take When Looking For A Data Science Job
    You have decided to look for a new job in the data science field and are finding it very hard. 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?...





In this webinar, Donald Miner - drawing upon his prior experience as a data scientist, engineer, and CTO - details the tracking of machine learning models in production to ensure model reliability, consistency, and performance into the future. Register here to attend or receive the recording.

*Sponsored post. If you want to be featured here, or as our main sponsor, contact us!



  • Principal Data Scientist - Next Caller - NYC

    Next Caller is searching for a data scientist with a contagious enthusiasm for data, a passion for exploratory problem solving, and a fascination with designing cutting-edge machine learning algorithms from scratch.

    As the Principal Data Scientist, you can expect to play an essential role in creating innovation and excellence in Next Caller’s Machine Learning-driven VeriCall platform for real-time call authentication and fraud prevention. In this role, you will be a core part of the Engineering Team, where you can expect trust, respect, collaboration, and humor as you engage in creative exploration within the department that is the lifeblood of our organization. The Next Caller Data Science Team uncovers unique insights that drive Next Caller’s real-time, API-based authentication services in a serverless AWS environment to clients in the financial services, telecommunications, healthcare, insurance, and travel and hospitality industries. We are seeking someone who is excited by the challenge of staying several steps ahead of fraudsters, and relishes the opportunity to tackle and solve problems that others think are just too difficult...

        Want to post a job here? Email us for details >>


Training & Resources




  • 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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