We are at the beginning of the multicore era. Computers will have increasingly many cores (processors), but there is still no good programming framework for these architectures, and thus no simple and unified way for machine learning to take advantage of the potential speed up. In this paper, we develop a broadly applicable parallel programming method, one that is easily applied to many different learning algorithms. Our work is in distinct contrast to the tradition in machine learning of designing (often ingenious) ways to speed up a single algorithm at a time. Specifically, we show that algorithms that fit the Statistical Query model [15] can be written in a certain "summation form," which allows them to be easily parallelized on multicore computers.

What are lift, KPI, robustness, model fitting, design of experiments, and the 80/20 rule? Answer: KPI stands for Key Performance Indicator, or metric, sometimes called feature. A robust model is one that is not sensitive to changes in the data. Design of experiments or experimental design is the initial process used (before data is collected) to split your data, sample and set up a data set for statistical analysis, for instance in A/B testing frameworks or clinical trials. The 80/20 rules means that 80 percent of your income (or results) comes from 20 percent of your clients (or efforts). What are collaborative filtering, n-grams, Map Reduce, and cosine distance?

My selection of articles and resources recently posted in various news outlets - mostly from specialized publishers dealing with big data, machine learning, visualization and related topics. The picture below is from the first article. Best Metrics For Digital Marketing: Rock Your Own And Rent Strategies Beautiful versus useful visualizations (in French, but interesting) Learning and Teaching Machine Learning: A Personal Journey The Sexiest Job of the 21st Century is Tedious, and that Needs to C... (*) The Sexiest Job of the 21st Century is Tedious, and that Needs to C... (*) Senior data scientists work on high level data from various sources, use automated processes for EDA (exploratory analysis) and spend little to no time in tedious, routine, mundane tasks (less than 5% of my time, in my case). I also use robust techniques that work well on relatively dirty data, and ... I create and design the data myself in many cases.

Click here to view more details about the book. This book is a type of "handbook" on data science and data scientists, and contains information not found in traditional statistical, programming, or computer science textbooks. The author has compiled what he considers some of the most important information you will need for a career in data science, based on his 20 years as a leader in the field. Much of the text was initially published on the Data Science Central website over the last three years, which is read by millions of website visitors. The book shows how data science is different from related fields and the value it brings to organizations using big data.