Cloud Computing Economics - There Is No Free Service

Cloudonomics Journal

Subscribe to Cloudonomics Journal: eMailAlertsEmail Alerts newslettersWeekly Newsletters
Get Cloudonomics Journal: homepageHomepage mobileMobile rssRSS facebookFacebook twitterTwitter linkedinLinkedIn

Cloudonomics Authors: Lori MacVittie, Skytap Blog, David H Deans, Shelly Palmer, Tim Crawford

Related Topics: Cloud Computing, Cloudonomics Journal, CIO/CTO Update, Telecom Innovation, Java in the Cloud, Big Data on Ulitzer

Blog Feed Post

Entering the field as a data scientist with certification

By Neera Talbert, VP Services and Ben Wiley, R Programmer at Revolution Analytics By now, everyone should be familiar with the data scientist boom. Simply logging onto LinkedIn reveals a seemingly infinite number of people with words and phrases like “Data Scientist”, “Big Data Specialist”, and “Analytics” in their title. A few weeks ago, an article floated around the internet about how R programmers are the highest paid software engineers in industry. But the career of a data scientist is hot not only because it’s highly lucrative; drawing conclusions from data is itself a rewarding process, since these conclusions often shape our future. As anyone would expect in such an attractive new and emerging field, a lot of people are noticing. So how do you distinguish yourself in a job application to an analytics position? Or, from a company’s perspective, how can you sift through the numerous applications of individuals with analytics backgrounds and choose the one that is best suited to finish the project? One of the tough aspects of the data scientist is that the definition is extremely broad. Upon a closer inspection of LinkedIn profiles with analytics positions, backgrounds include a variety of fields like applied and computational math, statistics, computer science, and so forth. With analytical aspects, even fields like biology or political science appear in these searches. In other words, having one particular background cannot bar you from being a data scientist. At the same time, however, this in no way implies that the path to a data scientist is easy. In fact, the loosely defined background requirements do more to attract top talent from many fields, rather than attracting only the talent from one. Having expertise in statistics and computer science no doubt helps quite a bit, but sometimes this is not enough to distinguish yourself on an application. There are many popular programming languages used for data analysis, and because these are often new and emerging, it can be difficult to assess one’s understanding of a particular language. Certifications can be one effective way to convey to an employer that you truly know and understand a program or concept. Revolution Analytics now offers a professional certification that tests the most sought after R analytics skills for the enterprise, and can be an effective way to assess which applicants possess the necessarily backgrounds in R and ScaleR programming. With R being the most widely used statistical language today, the Revolution R Enterprise Professional Certification can be a sure way to attract attention in the job market. Another option for standing out as a data scientist is to attend graduate school. Of course, this path is much longer than obtaining a certification, but the effects can be lucrative as well. While selecting a good data science grad program is a blog post in and of itself, the obviously attractive fields for a data scientist are statistics and computer science. (There are now also a number of graduate programs devoted specifically to data science.) That’s not to say that other fields aren’t good options either – fields like genomics, biology, physics, economics, and others that heavily rely on data can be attractive paths for the prospective data scientist as well. The only concern to consider is again verifying that the skills gained in a grad program reflect industry’s expectations. Finally, experience also helps. Having multiple years in an analytics position is a great way to convey one’s understanding of data science to employers, and is often a substantial consideration in a company’s evaluation of a candidate. Having a background as a programmer or analyst can be good ways to step into the data science position. Oftentimes a lack of experience is the greatest hurdle to entering the analytics profession, and so not everyone has the background described above. Despite the difficulty in attracting an employer’s attention, entering the field of data science is totally worth it. With articles about “Big Data” and “Cloud Computing” emerging everyday on the internet, being a data scientist no doubt puts you at the edge of modern-day technological development, and gives you the ability to make a substantial contribution to society. Plus there’s the pay… Revolution Analytics AcademyR: Revolution R Enterprise Professional Certification

Read the original blog entry...

More Stories By David Smith

David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid