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Data Science Weekly Newsletter
November 15, 2018

Editor's Picks

  • ML Resources
    As a budding student of ML, I often find myself re-googling things I've learned/forgotten many times. This afternoon, I decided to toss some favorite resources into one doc for speedy reference. Making it for my own use, but figured why not share the link...
  • How to deal with the seasonality of a market?
    Lyft has built many tools and bonuses to incentivize drivers and passengers to use Lyft more often or at specific times. But can we predict a few weeks in advance when we will need to launch this machinery, and if it will be enough to close the gap between drivers and passengers?...
  • From Graduate Student to Data Scientist:
    My Two Cents on Making a Successful Transition

    Getting that first data science job for someone like me, having no prior work experience and an undergraduate degree in Mechanical Engineering, wasn’t a piece of cake. Nevertheless, just as with most things in life, things have a magical way of falling into place. Looking back, it’s evident that all those stressful moments, rejections and setbacks were just guideways directing me to the right path. This path has me currently working as a Senior Data Scientist at eHealth in San Francisco...

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Data Science Articles & Videos

  • Fun with NFL Stats, Bokeh, and Pandas
    Cruising through Kaggle last week, I found a CSV of NFL play-by-play statistics. I get particularly excited about sports data so I started digging into this one right away...
  • WaveGlow: A Flow-based Generative Network for Speech Synthesis
    WaveGlow combines insights from Glow and WaveNet in order to provide fast, efficient and high-quality audio synthesis, without the need for auto-regression. WaveGlow is implemented using only a single network, trained using only a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable...
  • Introduction to Amazon SageMaker Object2Vec
    In this blog post, we’re introducing the Amazon SageMaker Object2Vec algorithm, a new highly customizable multi-purpose algorithm that can learn low dimensional dense embeddings of high dimensional objects...


  • Data Scientist, Content Science - HBO - NYC

    As a Data Scientist on the Data Science Solutions team, this individual will be responsible for building advance data science and analytical solutions that help HBO better understand and grow its best in class television and film library. The data products this individual develops will have a wide impact across the business, from helping HBO audiences discover new content to finding new hit television shows. The Data Scientist will work closely with engineering teams to ensure that their products and insights are properly moved into a production environment, where they can be used by the wider analytics team to drive business strategies...

Training & Resources

  • Variational Autoencoders Explained in Detail
    In this post I explain how to implement VAE - including simple to understand tensorflow code, using MNIST. I also explain a cool trick of how you can generate an image of a digit conditioned on the digit. This is something a vanilla VAE doesn't allow you...
  • Non-negative matrix factorization for recommendation systems
    Have you ever thought how do recommendation systems work, how to prepare an interpretable segmentation or optimize your marketing campaign target group? I have good news for you! After reading this article, you will know the answer to all of these questions on a fundamental level. Let me introduce you to Non-negative matrix factorization (NMF) algorithm...


  • Data Smart: Using Data Science to Transform Information into Insight
    "The best single book on Data Science today. I handle the data analysis and BI for the delivery side of a huge internet-based retail company, and have been a fan of Foreman's since his "Analytics Made Skeezy" blog days. His explanations are clear, his examples are to the point, and throughout it all, he is results-oriented."...

    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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