Microsoft Corp. snapped up LinkedIn Corp. for $26.2 billion in the largest acquisition in its history, betting the professional social network can rev up the tech titan’s software offerings despite recent struggles by both companies.The deal is Chief Executive Satya Nadella’s latest effort to revitalize Microsoft, which was viewed not long ago as left behind by shifts in technology. Mr. Nadella hopes the deal will open new horizons for Microsoft’s Office suite as well as LinkedIn, both of which have saturated their markets, and generally bolster Microsoft’s revenue and competitive position.
Many of those who call themselves statisticians just won't admit that data science heavily relies on and uses (heretical, rule-breaking) statistical science, or they don't recognize the true statistical nature of these data science techniques (some are 15-year old), or are opposed to the modernization of their statistical arsenal. They already missed the train when machine learning became a popular discipline (also heavily based on statistics) more than 15 years ago. Now machine learning professionals, who are statistical practitioners working on problems such as clustering, far outnumber statisticians.
Web apps are now more interactive than ever. Getting that last drop of performance can do a great deal to improve your end-users' experience. Read the following tips and learn if there is anything more you can do to improve latency, render times and general performance!
Rising alongside the relatively new technology of big data is the new job title data scientist. While not tied exclusively to big data projects, the data scientist role does complement them because of the increased breadth and depth of data being examined, as compared to traditional roles.
So what does a data scientist do?
A data scientist represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytics and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization.
Over the years, working as a data modeler and database architect, I have noticed that there are a couple rules that should be followed during data modeling and development. Here I describe some tips in the hope that they might help you. I have listed the tips in the order that they occur during the project lifecycle rather than listing them by importance or by how common they are.
The Protocols and Structures for Inference (PSI) project has developed an architecture for presenting machine learning algorithms, their inputs (data) and outputs (predictors) as resource-oriented RESTful web services in order to make machine learning technology accessible to a broader range of people than just machine learning researchers.