Fitness data: if you're a user of apps like Endomondo, Nike, Adidas MyCoach,MapMyRun or you wear things like a Jawbone, Misfit, Fitbit or Garmin and a lot more, you have data to study. Steep counter, distance, speed, pace, carbs lost and heart rate are some of the dimensions that you can analyze to get a deep understanding of your exercise and improve on it. In some way you can become your own coach by establishing realistic goals, scheduling exercise sessions in a more efficient way and planning rest days when you need them. Personal Schedule: maybe this tool doesn't sounds as awesome as fit bands or greatest gadgets but it has data that could help you a lot. If you are a person that is a high user of agendas and schedules you can analyze it to even predict your future.
Some time back, I wrote an article on "How to start a career in Business Analytics?". The article was well received by people who want to enter Business Analytics. It is still one of the most popular articles on Analytics Vidhya. In response to this article, I received a lot of queries about career in Analytics. While some of them were good queries, some of them were recurring myths.
Websays is the result of 15 years of scientific investigations in Web Crawling, Automatic Learning and Text Analytics. Dr. Hugo Zaragoza, Websays' founder, is a worldwide expert in those technologies. He has worked more than 10 years as a lead researcher in Microsoft and Yahoo! in the United States, England and Spain. In 2010 Dr. Zaragoza founded Websays with the objective of applying the most cutting edge technology in information retrieval and data analytics, including various new patent pending technologies developed by Websays. Websays services focus on online reputation monitoring and social media marketing.
The world of big data analytics is incredibly diverse, and people are coming up with new analytic tools and techniques every day. But one particularly productive combination that should not be overlooked involves the use of text analytics and machine learning. Tom Sabo, principal solutions architect at analytics giant SAS, says the one-two punch of predictive modeling on structured data, and text mining with unstructured data, can deliver insights that are more than the sum of their analytic parts. "They really run side by side," Sabo tells Datanami. "Let's say somebody has predictive models in place against whether customer will churn or to maximize profit, for instance.
Tim McFarland challenged me recently to ideate some small but potent technology primers for the members of the forums at Elevate Performance. My advice there may end up being more broadly generalized, but it had me thinking about just how many CEOs are currently making decisions pertaining to the field of Data Science. That's a tricky thing to do if your organization is not a mathematically or technologically focused one, since the business community is inundated with buzzword heavy sales pitches and impress-you-with-jargon marketing materials which ultimately cloud understanding. For the CEO, it's important to have an understanding of the terminology of the field so that initiatives can be communicated effectively. If for no other reason, understanding the terminology serves as a defense from having the same terminology used against the CEO--either by an eager consultant in a sales pitch or as a hand-waving technique from a colleague.