We often make important decisions when resources are scarce.

Many technological strides have been made over the last couple of decades. Advancement in technology is becoming faster and faster as each year passes by and many technologies to improve work efficiency, communication, and even financial services are being discovered and used every day. Technology has made the world smaller in a good way; we are now able to talk to family and friends from across the globe without having to spend too much money or wait too long for a reply. International financial transactions have also become more straightforward and hassle-free.

We here at Win-Vector LLC have some really big news we would please like the R-community’s help sharing.

This post is a short overview of a dozen Unix-like operating system command line tools which can be useful for data science tasks. The list does not include any general file management commands (pwd, ls, mkdir, rm, …) or remote session management tools (rsh, ssh, …), but is instead made up of utilities which would be useful from a data science perspective, generally those related to varying degrees of data inspection and processing. They are all included within a typical Unix-like operating system as well.

This post is co-authored by Mary Wahl, Data Scientist, Xiaoyong Zhu, Program Manager, Siyu Yang, Software Development Engineer, and Wee Hyong Tok, Principal Data Scientist Manager, at Microsoft.

In this blog post, I will learn you how you can mine opinions about companies from news articles. I will share how I scraped thousands of news articles in a few minutes and how one could classify the opinion expressed in the titles of the news articles. This information could be used for example to help with watching competitors of a company or to predict global trends.

What does this have to do with calculus? Well let’s convert $f(x)=x\cdot x \cdot x$ into a multi-linear function, i.e. $f(x,y,z) = x\cdot y \cdot z$, except that we know $x = y = z$ (or that they’re highly correlated). It is multi-linear because if we assumed data independence, then the function is linear with respect to each variable. For example, $f(5+3,y,z) = 8yz$ which is the same as $f(5,y,z) + f(3,y,z) = 5yz + 3yz = 8yz$. But since we know our data is not independent, technically this function isn’t linear because we can’t independently control for each variable. But let’s for a momement pretend we didn’t know our input data is correlated in this way.

Data are becoming the new raw material of business The Economist

Punchline: Go check out Deep Reinforcement Learning in Action