Data Science Weekly Newsletter - Issue 173

Issue #173

March 16 2017

Editor Picks
 
  • Why Pi Matters
    So it’s fair to ask: Why do mathematicians care so much about pi? Is it some kind of weird circle fixation? Hardly. The beauty of pi, in part, is that it puts infinity within reach. Even young children get this. The digits of pi never end and never show a pattern. They go on forever, seemingly at random—except that they can’t possibly be random, because they embody the order inherent in a perfect circle. This tension between order and randomness is one of the most tantalizing aspects of pi...
  • Voice and the uncanny valley of AI
    Voice is a Big Deal in tech this year. Amazon has probably sold 10m Echos, you couldn't move for Alexa partnerships at CES, Google has made its own and, it seems, this is the new platform. There are a couple of different causes for this explosion, and, also, a couple of problems. To begin, the causes...
 
 

A Message from this week's Sponsor:

 

 
 

Data Science Articles & Videos

 
  • DeepMind’s New Blockchain-Style System Will Track Health-Care Records
    Alphabet’s artificial intelligence outfit, DeepMind, plans to build a blockchain-style system that will carefully track how every shred of patient data is used. The company, which is rapidly expanding its health-care initiatives, has announced that it will build a tool that it calls Verifiable Data Audit during the course of this year. The idea: allow hospitals, and potentially even patients, to see exactly who is using health-care records, and for what purpose...
  • Possession Sketches: Mapping NBA Strategies
    We present Possession Sketches, a new machine learning method for organizing and exploring a database of basketball player-tracks. Our method organizes basketball possessions by offensive structure. We ϐirst develop a model for populating a dictionary of short, repeated, and spatially registered actions. Each action corresponds to an interpretable type of player movement. We examine statistical patterns in these actions, and show how they can be used to describe individual player behavior...
  • SciPy’s new LowLevelCallable is a game-changer
    Higher-order functions, ie functions that take other functions as input, enable powerful, concise, elegant expressions of various algorithms. Unfortunately, these have been hampered in Python for large-scale data processing because of Python’s function call overhead. SciPy’s latest update goes a long way towards redressing this...
  • Neural Network Architectures
    Deep neural networks and Deep Learning are powerful and popular algorithms. And a lot of their success lays in the careful design of the neural network architecture. I wanted to revisit the history of neural network design in the last few years and in the context of Deep Learning....
  • ICLR 2017 vs arxiv-sanity
    I thought it would be fun to cross-reference the ICLR 2017 (a popular Deep Learning conference) decisions (which fall into 4 categories: oral, poster, workshop, reject) with the number of times each paper was added to someone’s library on arxiv-sanity...
  • Complexity and Strategy
    I struggled with how to think about complexity through much of my career, especially during the ten years I spent leading Office development. Modeling complexity impacted how we planned major releases, our technical strategy as we moved to new platforms, how we thought about the impact of new technologies, how we competed with Google Apps, how we thought about open source and throughout “frank and open” discussions with Bill Gates on our long term technical strategy for building the Office applications...
  • Bayesian Ranking for Rated Items
    Problem: You have a catalog of items with discrete ratings (thumbs up/thumbs down, or 5-star ratings, etc.), and you want to display them in the “right” order...
  • 2017 Data Visualization Survey Results
    A few weeks back, spurred by a conversation about the state of data visualization in industry, some folks who do data visualization (myself included) put together a survey to find out what doing data visualization professionally meant. Through a series of 45 questions, the respondents identified, among other things: what were the job titles associated with doing data visualization, the tools, the thought leaders, the problems, and some sense of the demographics of the people in those roles. It was open for from February 27th to March 8th and 981 people responded...
 
 

Jobs

   
 

Training & Resources

 
  • Self-Organising Maps: An Introduction
    If you want to learn about machine learning techniques, the internet has you covered. In this post I want to talk about a less prevalent algorithm, but one that I like and that can be useful for different purposes. It’s called a Self-Organising Map (SOM)...
 
 

Books

 

  • 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.
 
 
P.S. Interested in reaching fellow readers of this newsletter? Consider sponsoring! Email us for details :) - All the best, Hannah & Sebastian
 
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