Data Science Weekly Newsletter - Issue 215

Issue #215

Jan 4 2018

Editor Picks
  • Transitioning From Academia to Industry:
    Perspectives from Indeed’s Data Scientists

    The data scientists I interviewed come from a variety of backgrounds, some from Liberal Arts and others from STEM. For some, Indeed was their first job after leaving academia, while others have been in industry for nearly 10 years. They left at various points in their academic careers, some while still ABD (“all but dissertation”), others while serving as assistant professors. Some went on the academic job market first, others “didn’t even bother,” still others never intended to. It’s my hope that this blog post offers some guidance and reassurance for academics considering making the leap to industry...
  • Predicting Crime in SF- a toy WMD
    I started this project to show the potential ethical conflicts created by our new algorithms. In every conceivable field, algorithms are being used to filter people. In many cases, the algorithms are obscure, unchallenged, and self-perpetuating. This is what O'Neil refers to as Weapons of Math Destruction - WMDs. They are unfair by design: they are our biases turned into code and let loose. Worst of all, they create feedback loops that reinforce said models. I decided to create a WMD for illustration purposes
  • AI and Deep Learning in 2017 – A Year in Review
    The year is coming to an end. I did not write nearly as much as I had planned to. But I’m hoping to change that next year, with more tutorials around Reinforcement Learning, Evolution, and Bayesian Methods coming to WildML! And what better way to start than with a summary of all the amazing things that happened in 2017? Looking back through my Twitter history and the WildML newsletter, the following topics repeatedly came up...

A Message from this week's Sponsor:


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Data Science Articles & Videos

  • Most Read Data Science Articles of 2017
    As the year end approaches we decided to dig through the 2017 DSW archives to figure out what were the most read articles of the year. It is an interesting mix - deep-dives into what’s going on in Data Science at top tech companies, interesting applications of data science, advice on how best to get into the field, unique explorations (or even debates) around some of the core concepts / techniques; and some helpful practical posts. Plus some emerging new sites that are already proving very popular...
  • [For next Holiday!] How to simplify your holiday festive meal planning
    Approach helps categorize recipes into different levels of complexity so that one could experience less stress and more enjoyment in coordinating the preparation of various dishes for e.g. a dinner party. Importantly, because of how it is trained to categorize recipes for relative preparation difficulty, one could further associate recipes based on the degree of similarity between their ingredients. This becomes handy if you wish to pivot between difficulty levels but also use similar ingredients that you may already have available...
  • How an A.I. ‘Cat-and-Mouse Game’ Generates Believable Fake Photos
    At a lab in Finland, a small team of Nvidia researchers recently built a system that can analyze thousands of (real) celebrity snapshots, recognize common patterns, and create new images that look much the same — but are still a little different. The system can also generate realistic images of horses, buses, bicycles, plants and many other common objects...
  • How Do You Vote? 50 Million Google Images Give a Clue
    What vehicle is most strongly associated with Republican voting districts? Extended-cab pickup trucks. For Democratic districts? Sedans. Those conclusions may not be particularly surprising. But what is surprising is how researchers working on an ambitious project based at Stanford University reached those conclusions: by analyzing 50 million images and location data from Google Street View, the street-scene feature of the online giant’s mapping service...
  • Analyse One Year of Radio Station Songs Aired with Apache Spark, Spark SQL, Spotify, and Databricks
    This article will present the year 2016 for 4 main french radio stations through fun SQL queries, then we will connect each song to the Spotify API to create the radio stations’ musical profile. We will use the Databricks community version to visualize our data. All SQL queries and all results are available on this notebook. It’s the “backstage” of this article, where the magic happens if we can say...
  • Can I Become a Data Scientist: Research into 1,001 Data Scientist Profiles
    We gathered data from 1,001 publicly listed LinkedIn profiles of data scientists. Obviously, the underlying assumption that drove our research is that one’s LinkedIn profile is an unbiased estimator of one’s CV. Therefore, with a ‘reasonable degree of scientific certainty’, we found some amazing insights. Here are our findings...



  • Data Scientist - - Oakland, CA
    As the world’s leading digital English dictionary and thesaurus, helps more than 70 million people use the English language with confidence every month. Today, is hard at work expanding our product offering to empower users to improve and enrich their language skills in the connected world.

    The team is passionate about building great products and creating a collaborative culture of data driven, user focused innovation. We are searching for a motivated Data Scientist to build data products and perform critical analyses that will power decision-making at all levels of the company...

Training & Resources

  • Use TensorFlow reshape To Convert A Tensor To A Vector
    This screencast and transcript explain how to use TensorFlow reshape to convert a tensor to a vector by understanding the two arguments you must pass to the reshape operation and how the special value of negative one flattens the input tensor....



  • Concrete Mathematics: A Foundation for Computer Science

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

    "This is fun stuff. It's an interesting take on discrete math. In fact, it's really not discrete math; in includes discrete math but also includes other elements. I think this is especially good for the CS people, which is actually the intended audience..."

    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
P.S., Want to reach our audience / fellow readers? Consider sponsoring - grab a spot now; first come first served! All the best, Hannah & Sebastian
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