So many data scientists select an analytic technique in hopes of achieving a magical solution, but in the end, the solution simply may not even be possible due to other limiting factors. It is important for organizations working with analytic capabilities to understand the various constraints of implementation most real-world applications will encounter. When developing
In the past decade, Big Data solutions have become the most impactful breakthrough in the data science industry. There has never been a better time to pursue a data career to help organizations wade through the Big...
The world is churning out 2.5 quintillion bytes of data every day, with 90% of it created in the past two years. Most of this staggering volume comes from apps, social media sites, YouTube and other video...
2015 will be the year that predictive analytics obtains significant market gains in everything from the industrial Internet to consumer devices. Here’s what Simon Arkell, CEO, Predixion Software, expects predictive analytics to look like in 2015: Predictive...
What does it mean today to say your are—or want to be, or want to hire—a “data scientist?” Not much, unfortunately. The job title has almost as much ambiguity as the term “Big Data.” If you really...
The business/data analyst role is evolving into a new role due in part to the new technology of big data. The data scientist role has emerged because of the increase in breadth and depth of data being...
The rapidly rising term “Data Scientist” caught up with “Statistician” and surpassed “Data Miner” on Google Trends. However, Statistics remains a lot more popular than “Data Science”, which begs the question: What do Data Scientists do? Clearly,...
R remains popular with the PhDs of data science, but as data moves mainstream, Python is taking over. While R has traditionally been the programming language of choice for data scientists, it is quickly ceding ground...
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