Data Science Weekly Newsletter

Issue

January 2, 2014

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Editor's Picks

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A Non-Comprehensive List Of Awesome Things Other People Did This Year
I made this list off the top of my head and have surely missed awesome things people have done this year... I wrote this post because a blog often feels like a place to complain, but we started Simply Stats as a place to be pumped up about the stuff people were doing with data...

How Machine Learning Can Transform Online Dating: Kang Zhao Interview
We recently caught up with Kang Zhao, Assistant Professor at the Management Sciences department, Tippie College of Business, the University of Iowa. His work applying Machine Learning to the world of online dating has generated significant coverage (Forbes, MIT Technology Review, UPI, among others), so we wanted to know more!...

Playing Atari With Deep Reinforcement Learning
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them...

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Data Science Year In Review
We’re coming to the end of a very eventful year in the field of data science, one in which the NSA collected telephone metadata, wearable tech became the next big thing, and academic collaboration took center stage. As we gear up for more exciting changes in 2014, we wanted to pause to take a look back at some of the areas and topics that caught our attention in 2013...

Do You Know What Life Will Be Like In 5 Years? IBM's Top Scientist Does
The company's annual report of five predictions for the next five years is all about the benefits of big data: stores, cities, classrooms, and hospitals. Dr. Bernard Meyerson, IBM Fellow, and VP of Innovation shares more...

Why Is Machine Learning The Most Popular Course At Stanford?
“The largest class on campus this fall at Stanford was a graduate level machine-learning course covering both statistical and biological approaches, taught by the computer scientist Andrew Ng. More than 760 students enrolled.” What’s going on here? Simply put, machine learning is the part of artificial intelligence that actually works...

K-means Clustering 86 Single Malt Scotch Whiskies
The first time I had an Islay single malt, my mind was blown. In my first foray into the world of whiskies, I took the plunge into the smokiest, peatiest beast of them all — Laphroig... As an Islay fan, I wanted to investigate whether distilleries within a given region do in fact share taste characteristics. For this, I used a dataset profiling 86 distilleries based on 12 flavor categories...

The Brain’s Visual Data-Compression Algorithm
Researchers have assumed that visual information in the brain was transmitted almost in its entirety from its entry point, the primary visual cortex (V1). “However, we have now demonstrated that the visual cortex suppresses redundant information and saves energy by frequently forwarding image differences,”...

Geoff Hinton - Recent Developments in Deep Learning
Geoff Hinton presents as part of the UBC Department of Computer Science's Distinguished Lecture Series. Professor Hinton was awarded the 2011 Herzberg Canada Gold Medal for Science and Engineering, among many other prizes. He is also responsible for many technological advancements impacting many of us (better speech recognition, image search, etc.)...

Do Deep Nets Really Need to be Deep?
Currently, deep neural networks are the state of the art on problems such as speech recognition and computer vision. In this extended abstract, we show that shallow feed-forward networks can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models...Our success in training shallow neural nets to mimic deeper models suggests that there probably exist better algorithms for training shallow feed-forward nets than those currently available...

Data Science 101: Data Analysis MOOC Post-Mortem
The Coursera Data Analysis course recently completed its latest 8 week session. Professors Leek and Peng (Computing for Data Analysis class) provide an insightful post-mortem video that discusses the MOOC approach with respect to teaching data science...

LinkedIn’s Pete Skomoroch Discusses The Voltron Of Data Science
In this discussion, Skomoroch articulates what I believe should be the standardized definition for what a true data scientist is within the world of consumer Internet services and applications. Specifically, he believes data scientists are bring together generalist skills in the fields of business intelligence (with some product sense), mathematics and statistics (in order to construct and tune algorithms) and computer science (to write the actual code) to form a “Voltron” of data science...

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Data Scientist, NPR, Washington, DC
Do you have a passion for analytics and thirst to apply your knowledge to media? Do you want to be challenged by your job and be surrounded by passionate, dedicated, and creative people? Then this role is for you. You will extract insights from complex media usage data sets for product development, evaluate, and identify strategic opportunities. You will become the expert for digital metrics within NPR...

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How Do I Become A Data Scientist?
A compilation of insightful responses on Quora from a wide range of current Data Scientists ...

One Page R: A Survival Guide To Data Science With R
Welcome to One Page R. This compendium of modules weaves together a collection of tools for the data miner, data scientist, and decision scientist. The tools are all part of the R Statistical Software Suite...

Six Reasons Why I Recommend Scikit-Learn
I use a variety of tools for advanced analytics, most recently I’ve been using Spark (and MLlib), R, scikit-learn, and GraphLab. When I need to get something done quickly, I’ve been turning to scikit-learn for my first pass analysis. For access to high-quality, easy-to-use, implementations of popular algorithms, scikit-learn is a great place to start. So much so that I often encourage new and seasoned data scientists to try it whenever they’re faced with analytics projects that have short deadlines...

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