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How to Become a Data Scientist on a Shoestring

Big Data jobs are definitely sexy – just as Google’s chief economist Hal Varian predicted they would be, back in 2009. In the increasing number of organizations where data science is valued, data scientists know they are working on the cutting edge solving problems in ways no one has thought of before. They generally command very decent salaries, too.

As well as being a rewarding (both financially and in terms of job satisfaction) career path, it’s also one where you shouldn’t have to worry about work drying up in the near future. Businesses are consistently saying they have difficulty finding people with the analytical talents they need, and spending on analytics is predicted to continue increasing in all sectors.

All very well for people who have spent the last ten years preparing for it though, right? They’ve gone to school and university, probably spending thousands in the process, and are now set up with Masters degrees or PhDs and are ready to reap the rewards. But what about everyone else?

Well, employers I speak to who hire data scientists have all pretty much been in agreement on one thing – while formal academic qualifications and industry experience are nice, what really counts is having the skillset and the ability to think analytically in the right kind of way. And if you are smart, there are plenty of ways to develop those attributes without having to quit your day job and spend years, as well as your life savings, going back to school

Open learning

These days there are literally thousands of free, online courses which can teach you just about anything you need to know – including the basic (and even advanced) applications of data analysis and science. Some of the best courses from the world’s most prestigious universities are available to study online, at no cost whatsoever. Okay, you won’t be awarded a degree at the end of it, but if you apply yourself to studying you will finish with the exact same education as an enrolled student would receive, but save yourself a great deal of money. Whether you need to brush up on your core skills such as mathematics or statistics, or learn practical applications of data technology like SQL, Hadoop or Python, you will find everything you need online. Good places to start looking are:

Get help from academia

If you run a business already, or work for a business which is looking to move into data analytics, help is often available through partnering with universities and other academic institutions. Depending on where you are in the world, local colleges and universities may offer programs which pair up organizations with suitable students and professors looking to test their theoretical knowledge in real business scenarios. Even if they don’t have a formal program in place, many will be interested in hearing from you if you have particular challenges that their academics could help to solve. After all, the point of going to college is usually to get a job, so higher educational institutions are often keen to form bonds with industry. The benefits go both ways – you can take advantage of, and learn from, the academic expertise, and they get to show that their theorizing has practical uses in the real world.


Crowd-sourced competitions

Kaggle hosts regular competitions open to both amateur and professional data scientists. Businesses come to them with data challenges, and users of the site can simply register, download the data and take part. Employers including Walmart have even used the platform as a direct recruitment tool, taking on candidates for senior positions based on the ingenuity they have shown in their competition entries.  While it would seem natural to take this step after exploiting some of the opportunities previously discussed in this article, in order to be armed with the best chance of success, some of the competitions, such as this one to read text such as graffiti from Google Streetview images, are designed to teach the skills along the way.

 

Work Experience

This would probably be a later step along your path to becoming a data scientist on a shoestring. After you’ve learned the basics from taking advantages of some of the opportunities I’ve mentioned so far, it’s time to see how your skills stack up in the real world. Unpaid work experience has always been an invaluable means of building up real, practical experience, and adds the final necessary flourish your resume needs before it’s ready to land you a paid gig. The challenge here is to convince employers that you have the analytical mindset, as well as the on-paper skills, they need as part of their analytics and data masterplan. It will be a challenge – most employers will probably be more used to receiving work experience applications from students enrolled or recently graduated from school or college. But data analysis tends to teach those who practice it that innovation often comes in from unexpected quarters. Demonstrate that your passion for data, as well as your theoretical skillset, is up to the task, and there’s no reason doors won’t open for you.

If you’ve landed a career in data science without a conventional educational background, I’d love to hear from you! Why not leave a comment below? 

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Comment by Mark Nuppnau on January 15, 2016 at 8:56am
I'm involved in that process right now. I have my Master's in taxation, but I'm currently a reporting analyst writing SQL queries and analyzing data in Oracle BI Cloud Service. I went back to community college last Fall and took an intro to Java class. Got 100% in the class and decided to pursue a less formal education.

I wouldn't have finished Java 2 and data structures until December 2016. I found a Java program on Cousera which would allow me to finished both by April/May 2016. Then I can spend 3 months on Hadoop through SimpliLearn and then Scala/Spark to finish up 2016 as opposed to just taking two classes at the local community college. I feel the formal education isn't keeping ahead of the times.

Then, until I use that in my career, I plan to work on kaggle projects to keep my skills sharp. Also, in 2017, I'll have to brush up on my Math skills and maybe dive into Python.

Let me know what you think of my plan.


Great article and many thanks!

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