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5 Tips: How to Move into An Analytics Career
Looking to jump start an analytics career? Here are five tips to make the move easier.
4 Min Read
<p>(Image: Olivier Le Moal/Shutterstock)</p>
Are you an aspiring data scientist? If so, you are not alone. Technology professionals that help turn data into meaningful and valuable insights are the rock stars of innovative organizations today.
Quantitative recruitment specialist Linda Burtch has said that the demand for top data pros is at the highest level she has seen over the course of her career. And McKinsey Global Institute lead researcher Michael Chui told me recently that the big rise in salaries for data scientists is an indication of just how demand has grown for these professionals.
[Looking for more analytics careers advice? Listen in to our podcast as we interview an expert from recruitment firm Robert Half Technology about how the role of data scientist has evolved in the job market. Evolution of the Data Scientist Role .]
And as demand and salaries rise, it's not a surprise that more people are looking at careers in data science and analytics. To meet the need, more educational programs have arrived. Companies like SAS (the sponsor of this site) have partnered with many universities to provide free software and other support for aspiring data analytics pros. So there are great options available for those entering school or maybe returning to school for an advanced degree.
But what about for people who already have a few years of experience in the workforce in coding or in business who are looking to pivot in their careers?
I recently got note from someone like this who was already enjoying an established technology career, but wanted to become one of the sought-after data science "unicorns" that had that unique combination of three skill sets -- programming, statistics, and business knowledge. He was studying machine learning and wanted to know what else he could do to further his career to become one of these data science unicorns.
It's actually a question I've been asking recruiters, practicing data scientists, and other experts in my travels over the last year. The following are some of the suggestions I've collected:
Attend local data science, machine learning, or other data-related Meet Ups. These are in-person events where you can meet other people who are interested in the same things. This is a great way to connect with like-minded people who may have job leads, suggestions, or who may even be recruiting data science talent.
Attend relevant conferences and conventions for particular vertical industries. For instance, if you are looking to get into an industry vertical like retail, the National Retail Federation's upcoming event in New York January 15 to January 17 would be a great place to learn about analytics for retail. I'll be at the event and will certainly report back on my experiences from the show, but if you are in the area and can make it to the event, it's time well spent to make the face-to-face connection. Plus, Virgin Group founder Richard Branson and Intel CEO Brian Krzanich are featured speakers at the event.
Showcase your work in online communities. Sites such as Kaggle offer a place for data scientists and aspiring data scientists to enter competitions and show off their work. It's also a great place to connect with other people. But don't stop there. Did you know that All Analytics has a LinkedIn community? That's another place where you can connect with others who are interested in analytics.
Consider a data science or analytics boot camp. These programs are typically about 12 weeks long and can quickly get you up to speed in an area where your skills may currently be falling short. Importantly, many of these programs also have connections to potential employers, giving you a foot in the door when the program is complete.
And don't forget about All Analytics. The community here will connect you with so many experts in the field. Be sure to sign up for our weekly email newsletters and attend our online Academy events, too.
Did I miss anything? What other suggestions do you have for career changers who are looking to get into the data analytics field?
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