Why Are YOU Learning How to Code?


Why Are YOU Learning How to Code?

By Brian Liou     Mar 6, 2015

Why Are YOU Learning How to Code?

This article is part of the collection: Give Your Kids a Most Excellent Coding Adventure.

You just finished the 10-hour JavaScript lesson on Codecademy, and you’re feeling pretty accomplished. You learned how control structures work, did some object-oriented programming, maybe even built a simple to-do list.

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 the evangelization has been somewhat misleading. The problem in our "Chris-Bosh-codes-and-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 may not be 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 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.

So to all non-technical professionals looking to get technical: If you want to become a software engineer, by all means learn Ruby or go through the Javascript tutorials on Codecademy. But if you’re simply a business professional looking to gain an edge on your peers, trust me, you are much better off learning a more practical language like R, an industry standard for data science and analytics.

Brian Liou is CEO of Leada. The post was first published on Medium.

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