Unfortunately for you, the feeling of triumph doesn’t linger. It’s like staring at a blinking cursor in an empty Word document; you’re unsure of what’s next. Hours, weeks, or months later, you feel slighted. Why did you even bother to learn to code anyways? Where’s the free food? Where are the rainbow-colored bikes? Where’s that high-paying job?
Learning to code has become a mainstream fascination, but all the evangelization has been misleading. The problem in our Chris-Bosh-codes-so-should-you society is that people learn to code without first asking “for what purpose do you want to use code?” What in your day-to-day work could you actually automate using code? Let’s face it, your odds of creating the next hot iPhone app aren’t great, but the spreadsheets you look at everyday or the strategic business decisions you or your company makes? Coding can help you with those. Coding to better understand data would help everyone.
The best reason why you and the rest of the world should, or rather, needs, to learn how to code is not for building websites or mobile apps, but for the purposes of understanding and making use of all the data surrounding us. It’s in data analysis, and more recently, in data science, where the need for coding goes beyond the normal scope of an engineer and into a marketer, a sales professional, or a manager.
The point: Learning to code for the purpose of analyzing data is a more practical and employable application of coding skills for the majority of those interested in learning to code.
Why should you learn to code to analyze data? Because data analysts are in desperate demand in every industry. The demand for this type of skill has transcended even the demand for software engineers. McKinsey predicts that by 2018, there will be 1.5 million unfilled data analysis jobs in the U.S. alone. Based on its own study of the job market, Linkedin found that statistical analysis and data mining was the #1 skill-set to get you hired in 2014. The demand for data skill is simply common sense. Chief Economist at Google, Hal Varian phrased it best when he said:
If you are looking for a career where your services will be in high demand, you should find something where you provide a scarce, complementary service to something that is getting ubiquitous and cheap. So what’s getting ubiquitous and cheap? Data. And what is complementary to data? Analysis. So my recommendation is to take lots of courses about how to manipulate and analyze data.
The world simply needs more people who are data literate. Individuals who can analyze, visualize, and communicate decisions from data, and this is most flexibly done through code. When was the last time you made a decision based purely on intuition? Wouldn’t it be great to support that decision with facts and data? Data-driven decision making is real and possible for everyone.
This is where I believe initiatives like Code.org should be directed because becoming a software engineer is definitely not for everyone. In contrast, everyone works with data. Realize that learning to code is only half the battle. Being able to apply statistics to make informed decisions is the other half of data analysis. Leada can help you do both.
Special thanks to Andrea Liou, Derek Steer, Caroline Winnett, and Carl Shan for reading drafts!
 What is R? It’s the industry standard for data science & analytics. It’s also free. R is especially valuable in terms of data analysis because it’s heavily intertwined with analysis methods (statistical packages) so you can make conclusions about the data you are working with. Check out Swirl, a tool to learn R in R!