Many people and companies seem to think of "cognitive computing" as an area separate from analytics. Most large organizations today have significant analytical initiatives underway, but they think of the cognitive space as being an exotic science project. One executive told me, "We have no desire to win Jeopardy," an allusion, of course, to the IBM Watson project from 2011. But cognitive computing is not just about Watson, and it's not an exotic science project. In fact, I'd argue that cognitive computing is a logical extension of analytics work.

The web is full of good explanations of machine learning algorithms. And every second applicant for a data science position has finished the Coursera course on machine learning. Theory will not help you choose good values for the 16 parameters a standard implementation of a random forest takes. The default values are good to get started, but which parameters should you modify depending on your data? Choosing the right features, algorithms and parameters is an art.

Machine learning is not new. SAS has been doing it for over 20 years and some early machine learning papers date back to the 50's. So why is it one of the hottest topics at the Strata Hadoop World conference later this week? Clearly, Hadoop is playing a major role in the increased focus on machine learning. Powerful, low-cost distributive computing environments coupled with Hadoop give data scientists the ability to run iterative models (like neural networks) they may not have been able to in the past.

When we first began working on Leada, we sought to better understand the data science industry by interviewing professionals in the field. As students simply wanting to learn more about data science, we ultimately created a free resource to inform both undergraduates and professionals about the data science industry. We accomplished this by having Q & A interviews with experts such as Mike Olsen, Hal Varian, Tom Davenport, and data scientists at LinkedIn, Facebook, Yelp, and more. The Data Analytics Handbook was not only instrumental in giving us the understanding we needed to feel confident in what we were creating; but was downloaded over 25,000 times, gave us dozens of contacts, and an immediate group of early adopters. Some experts took longer to contact than others (I emailed Hal Varian over 8 times) but you would be surprised who you can get 25 minutes of time to help inform others.

I fell in love with Machine Learning during my Master degree in Telecommunications Engineering and Information Technology. Since then I could never live without it and I see the world with different eyes. I have always been very fascinated by math and statistics, by how sometimes a very simple equation will describe extremely complex phenomena, how we can squeeze nature into a formula; at the same time my mind has always been captured by those phenomena, often very simple and part of our daily life reasoning and acting, that can't be represented by any mathematical form, no matter how convoluted. The idea of seeing the world through numbers has always exercised a certain spell on me. Then I discovered Machine Learning.

Robots are going to school to take your job! Well…not really.

Very interesting collection of resources compiled by DistrictDataLabs, featuring books, online courses, articles across multiple categories: data science, probability and statistics, machine learning, R, Python, big data, DataViz, and NLP.

Wouldn't it be wonderful to travel to a foreign country without having to worry about the nuisance of communicating in a different language? In a recent Wall Street Journal article, technology policy expert Alec Ross argued that, within a decade or so, we'll be able to communicate with one another via small earpieces with built-in microphones. No more trying to remember your high school French when checking into a hotel in Paris. Your earpiece will automatically translate "Good evening, I have a reservation" to Bon soir, j'ai une réservation - while immediately translating the receptionist's unintelligible babble to "I am sorry, Sir, but your credit card has been declined." Ross argues that because technological progress is exponential, it's only a matter of time.

Joint state and parameter estimation is a core problem for dynamic Bayesian networks. Although modern probabilistic inference toolkits make it relatively easy to specify large and practically relevant probabilistic models, the silver bullet---an efficient and general online inference algorithm for such problems---remains elusive, forcing users to write special-purpose code for each application. We propose a novel blackbox algorithm -- a hybrid of particle filtering for state variables and assumed density filtering for parameter variables. It has following advantages: (a) it is efficient due to its online nature, and (b) it is applicable to both discrete and continuous parameter spaces . On a variety of toy and real models, our system is able to generate more accurate results within a fixed computation budget. This preliminary evidence indicates that the proposed approach is likely to be of practical use.

Julie Sobowale is a freelance journalist and lawyer based in Halifax, Nova Scotia, specializing in legal reporting. She writes about trends in the legal industry including legal technology, innovation, entrepreneurship, diversity and major shifts in legal culture. Her work has appeared in publications from the American Bar Association, the Canadian Bar Association, the Canadian Corporate Council Association, Canadian Lawyer and the Nova Scotia Barristers Society. She's also given presentations on legal trends, alternative careers and legal education. She graduated from the Dalhousie Schulich School of Law in 2012 and was the recipient of the Dalhousie Faculty of Law Leadership Award.