Causal Random Forests, by Mark White

I stumbled accros this incredibly interesting read by Mark White, who discusses the (academic) theory behind, inner workings, and example (R) applications of causal random forests: EXPLICITLY OPTIMIZING ON CAUSAL EFFECTS VIA THE CAUSAL RANDOM FOREST: A PRACTICAL INTRODUCTION AND TUTORIAL (By Mark White) These so-called “honest” forests seem a great technique to identify opportunities…

Putting R in Production, by Heather Nolis & Mark Sellors

It is often said that R is hard to put into production. Fortunately, there are numerous talks demonstrating the contrary. Here’s one by Heather Nolis, who productionizes R models at T-Mobile. Her teams even shares open-source version of some of their productionized Tensorflow models on github. Read more about that model here. There’s another great…

13 Data-Driven Insights to Improve Your Job Search

Talent.Works is back, elaborating on the applicant characteristics that relate to landing an interview. While the majority of applicants has a meager ~2% chance of getting invited to an interview, some applicants do way better! What accounts for their success? Analyzing 4000+ applicants, Talent.Works found 13 factors that related to getting an interview. There are…

The Magic Sudoku App

A few weeks ago, Magic Sudoku was released for iOS11. This app by a company named Hatchlings automatically solves sudoku puzzles using a combination of Computer Vision, Machine Learning, and Augmented Reality. The app works on iPad Pro’s and iPhone 6s or above and can be downloaded from the App Store. Magic Sudoku gives a magical experience…

Talent Works: Data Science to improve Job Application Chances

Searching and applying to jobs can be a costly activity, requiring many hours upon hours of perfecting your motivation letter and CV. Hence, it can be very frustrating to get ghosted (not receiving a reply) for a job. Luckily, Talent Works is able to give us some general tips when it comes to improving the…

Video: Human-Computer Interactions in Reinforcement Learning

Reinforcement learning is an area of machine learning inspired by behavioral psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward (Wikipedia, 2017). Normally, reinforcement learning occurs autonomously. Here, algorithms will seek to minimize/maximize a score that is estimated via predefined constraints. As such, algorithms can thus learn to perform the most effective actions (those that…

Coexisting Languages in Australia: An Interactive map

Jack Zhao from Small Multiples – a multidisciplinary team of data specialists, designers and developers – retrieved the Language Spoken at Home (LANP) data from the 2016 Census and turned it into a dot density map that vividly shows how people from different cultures coexist (or not) in ultra high resolution (using Python, englewood library, QGIS, Carto). Each colored…

IBM’s Watson for Oncology: A Biased and Unproven Recommendation System in Cancer Treatment?

The below reiterates and summarizes this Stat article. Recently, I addressed how bias may slip into Machine Learning applications and this weekend I came across another real-life example: IBM’s Watson, specifically Watson for Oncology. With a single machine, IBM intended to tackle humanity’s most vexing diseases and revolutionize medicine and they quickly zeroed in on a high-profile target:…

Where to look for your next job? An Interactive Map of the US Job Market

The people at Predictive Talent, Inc. took a sample of 23.4 million job postings from 5,200+ job boards and 1,800+ cities around the US.  They classified these jobs using the BLS Standard Occupational Classification tree and identified their primary work locations, primary job roles, estimated salaries, and 17 other job search-related characteristics. Next, they calculated five metrics for each role and city…