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Data Science Weekly Newsletter
January 24, 2019

Editor's Picks

  • Let Curiosity Drive: Fostering Innovation in Data Science
    The real value of data science lies not in making existing processes incrementally more efficient but rather in the creation of new algorithmic capabilities that enable step-function changes in value. However, such capabilities are rarely asked for in a top-down fashion. Instead, they are discovered and revealed through curiosity-driven tinkering by data scientists. For companies ready to jump on the data science bandwagon I offer this advice: think less about how data science will support and execute your plans and think more about how to create an environment to empower your data scientists to come up with ideas you’ve never dreamed of...
  • This AI teaches robots to walk—by creating custom obstacle courses
    Before you run hurdles, you have to learn to crawl, and before you read William Shakespeare, you need to know the alphabet. Any educator knows the importance of a step-by-step lesson plan for mastering a task. Now, researchers at Uber AI Labs have designed an algorithm that comes up with its own curriculum for teaching simulated robots to cross difficult terrain, without falling flat on their faceless bodies. The algorithm might one day even help autonomous vehicles react in emergency situations...

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Data Science Articles & Videos

  • Obtaining Insights From Data: Optimizing an NBA Career
    Since the publication of Moneyball, people have started examining sports with more statistical scrutiny, so being a statistics-motivated sports fan, I wanted to solve an atypical basketball problem: How can we optimize a typical basketball player’s career in the NBA? The question itself seems open-ended, so in order to better scope this endeavor, I’m going to measure success by dollars earned...
  • Why chatbots haven’t (yet) lived up to their hype
    Chatbots haven't (yet) lived up to their hype. But, I still believe we’re at the very beginning of explosive, long-term growth. Let's look at what's preventing high-growth adoption of chatbots — the same analysis can be applied to any new or emerging tech...
  • Street art, not street art
    The project trains a model that detects whether an image is or is not street art. The model is trained on a image set gathered from hashtagged images for #streetart...
  • rstudio::conf 2019 takeaways
    As an attendee I found that three themes were showing up over and over throughout the event, which sheds some light on where the R community may be heading. A write-up seemed like a good way to not forget them, and if you were unable to attend hopefully this helps with the FOMO!...


  • Data Scientist - Uber for Business - NYC

    Uber for Business is on a path to revolutionize the way businesses manage their ground transportation needs. We need creative, quantitative thinkers with the ability to clearly synthesize and communicate insights from product data to accelerate us down this path. As a Data Scientist on Uber for Business you will work hand in hand with the Product, Marketing, Design, Sales and Engineering teams to keep product development data driven and informed. Candidates are expected to act with high levels of autonomy to guide their team’s roadmaps and build their data products....

Training & Resources

  • Deep Multi-Task Learning (MTL) – 3 Lessons Learned
    There are already quite a few posts about implementing MTL in a DL model. In this post I will share some specific points to consider when implementing MTL in a Neural Network (NN). I will also present simple TensorFlow solutions to overcome the discussed issues...


  • Math for Machine Learning:
    Open Doors to Data Science and Artificial Intelligence

    From self-driving cars and recommender systems to speech and face recognition, machine learning is the way of the future. Would you like to learn the mathematics behind machine learning to enter the exciting fields of data science and artificial intelligence? There aren't many resources out there that give simple detailed examples and that walk you through the topics step by step.
    This book not only explains what kind of math is involved and the confusing notation, it also introduces you directly to the foundational topics in machine learning. This book will get you started in machine learning in a smooth and natural way, preparing you for more advanced topics and dispelling the belief that machine learning is complicated, difficult, and intimidating.
    Praise from students
    "Your book is by far the best I’ve found for understanding the derivations of machine learning algorithms. I love that you don’t skip steps and that you provide clear examples."--Robert H"... Link to preview of first 2 chapters and table of contents available here<

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

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