Take your data analysis skills to the next level

Regardless of what industry you work in, you are likely to be left behind if you lack basic analytical skills. Taking a proactive approach may end up saving you time -- and a hassle – further down the line.

In case you haven't heard, there is a shortage of skilled data professionals. The problem isn't just about a lack of data scientists at the Ph.D. level. Companies are having a hard time finding anyone to work in data-intensive roles, which are becoming more and more ubiquitous.

In order to do my part in addressing this problem, I have decided to take a class in data analysis. It's a MOOC (massive open online course) offered by Duke University. It will teach the basics of gathering data, cleaning up data sets, understanding correlations and even provide some basic training in the R programming language.

I was inspired by a presentation at the recent RapidMiner World user conference in Boston. Matthew North, professor of business and information technology at the College of Idaho, talked about how necessary data analysis is today for a wide range of jobs, even ones that aren't typically thought of as being stats-heavy. He also said that just about anyone can learn to do some fairly advanced analyses when taught the right way.

North's first point about data skills becoming more necessary is hard to argue against. As a reporter, I think back to my college classes and professional career and see very little focus on data. The only data analysis tip I remember receiving came from a professor who was trying to teach the class how to cover municipal budgets. He said the best story probably comes from the line item that changed the most from one year to the next. How's that for a deep insight?

But today I see people like Nate Silver of FiveThirtyEight, the news site he launched that specializes in data journalism, the success he's had telling stories that other people can't, and I feel like I need to step up my game in order to stay relevant in my profession.

As for North's second point -- that just about anyone can learn data analysis skills -- we'll see. I became a reporter rather than an engineer or architect precisely because math is not my strong suit. But if I am able to successfully learn the tricks of the trade, it's safe to say most people can.  

I don't know exactly what I want to get out of the class. It will certainly be helpful to have a deeper, more hands-on understanding of the field I cover. I also think it will be helpful professionally to keep my skills fresh and keep pace with those who are taking my industry in a more data-driven direction.

The point is that you don't need to have specific professional goals in mind to get motivated to learn data analysis skills. Whether you are a teacher, banker, social worker or retail store manager, data is creeping into your job, whether you know it or not. It would be best to proactively acquire the skills necessary for dealing with data now rather than wait for your employer to decide that these skills are a core competency of your job.

So this is my challenge to my readers: Ask not what an algorithm can do for you; ask what you can do with an algorithm.

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