5 Tips: How To Move Into An Analytics Career


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.

(Image: Olivier Le Moal/Shutterstock)

(Image: Olivier Le Moal/Shutterstock)

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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. Let us know in the comments.

Jessica Davis, Senior Editor, Enterprise Apps, Informationweek

Jessica Davis has spent a career covering the intersection of business and technology at titles including IDG's Infoworld, Ziff Davis Enterprise's eWeek and Channel Insider, and Penton Technology's MSPmentor. She's passionate about the practical use of business intelligence, predictive analytics, and big data for smarter business and a better world. In her spare time she enjoys playing Minecraft and other video games with her sons. She's also a student and performer of improvisational comedy. Follow her on Twitter: @jessicadavis.

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Kaggle analysis of Titanic data
  • 2/2/2017 5:27:42 PM
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..

So I followed the herd and had a look at the Kaggle site. Interesting indeed.

I liked the little video about the Titanic but missed Celine Dion.

I'm not involved in R programming but the analysis "Exploring Survival on the Titanic" by Megan Risdal is kinda interesting, especially the breakdown by family size that shows that the survival rate of single passengers was horrible compared with families of up to 4. But familes >4 also had a rotten rate. 

I guess a word to the wise would be: Don't travel alone on a 1912 ocean liner ...

..

Re: Kaggle
  • 2/2/2017 7:51:23 AM
NO RATINGS

@magneticnorth: Anonymizing the data (or, at the very least, the data source, the organization, and related salient points) helps, I tend to think.

In any case, I think it depends upon the nature of the data and the business (e.g., something bland like bounce rates on an unidentified website versus trade secrets).  Just as in regular conversation, there are some things you can talk about with outsiders (e.g., at a dinner party) when it comes to your work and your business, and some things you can't.  Your mileage may vary.

(Disclaimer: Not legal advice, of course.)

Re: Kaggle
  • 1/29/2017 11:54:39 AM
NO RATINGS

@kq4ym It is amazing, isn't it? I was under the impression that most data scientists' work would be under some non-disclosure agreement, but now there's a community where you could showcase your work. Kaggle is something I wouldn't have thought would emerge but here it is. I guess there's more collaboration to be had than I'd foreseen.

Re: Kaggle
  • 1/26/2017 11:16:33 AM
NO RATINGS

@Seth: Wow.  Thanks for this tip, Seth.  I've already advised a data analytics friend of mine to check this out...and now I'm going to reemphasize that fact (and possibly sign up on Kaggle myself).

Have you played with some of those government/obscure datasets?  Do you have any projects/examples you care to share or any such "war stories"?

Re: Kaggle
  • 1/22/2017 8:20:26 PM
NO RATINGS

Kaggle can also help users find free datasets from governments and other entities that might be hard to otherwise find.  You also create and upload your own datasets and share links to it for potential employers. 

Re: Kaggle
  • 1/18/2017 8:10:01 AM
NO RATINGS

The online communities is an except tip and I didn't know about Kaggle so that would certainly be a top stop for those folks wanting to get more experience and get feedback from other in the field.

Re: Kaggle
  • 1/16/2017 4:43:52 PM
NO RATINGS

@Joe  The Titantic tutorial does look interesting.   And Thank you Jessica for providing another source that can be combined with A2's great content !

Re: Kaggle
  • 1/11/2017 3:29:33 PM
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Glad it was helpful!

Kaggle
  • 1/11/2017 12:03:32 PM
NO RATINGS

Thanks for the tip about Kaggle, Jessica.  The Titanic tutorial seems pretty interesting.

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