Incoming Freshman: Take A Computer Science and Statistics Course Regardless of Major!
As my senior year at Dartmouth College progresses, many people have asked me, “What piece of advice would you give an incoming college freshman?”
Upon reflection I settled on this “…regardless of your major [and if the opportunity presents itself] take a Computer Science and a Statistics course.”
Computer Science has impacted every industry you can think of, finance, technology, engineering, energy, oil & gas, etc. As society progresses, so does technology. Programming is a super-power. Someone can have the next “Facebook” or “Snapchat,” but without the necessary tools to create the end-product, that idea stays as just that– an idea.
People may argue over which language is more beginner friendly, but when you taking a Computer Science course the biggest take away [the meta skill] is “learning to problem solve.”
But what language?
From my experience in college and industry, programming languages come and go, but problem-solving is here to stay. Take a company like Netflix for example. In its early days, Netflix utilized Java, but as times changed and technology evolved, Netflix began to use NodeJS. NodeJS allowed Netflix to have a common language for both the server-side [back-end] and browser side [front-end], allowing their developers to work more efficiently, versus the added complexity of switching between different languages. Another benefit of NodeJS is that it has a plethora of modules that are mostly open source.
All of this is to say that languages will come and go [and will change depending on the company or product], but the underlying problem-solving skills programming teaches you will remain relevant and allow you to adapt. No one can take that away.
You don’t even have to major in Computer Science or Computer Engineering. Just take a programming course and learn how to attack a problem.
Likewise, the next advice I would give to incoming college first-year students is to take a Statistics course. Now you’re probably thinking, “why Statistics?” “why not something cool like Machine Learning?” Well, this is because Machine Learning and Statistics are very similar. So much so that to Statisticians, Machine Learning is “Applied Statistics” or “Statistical Learning.” Fundamental Statistics concepts such as hypothesis testing(p-values, test statistics), variance, correlation, and simple linear regression are all essential requisites for Machine Learning. Suffice to say, if you want to get into Machine Learning, prior knowledge in Statistics is a must. Additionally, beyond buzzwords like Machine Learning, Statistics is a skill that significantly augments quantitive reasoning. In this day and age, data drives decisions.
What’s the data saying?
Consequently, those who have the skills to derive relationships from data will be in high demand. After taking a Statistics course at the Thayer School of Engineering, I was able to view data differently. Whenever I’d have a problem, I’d always ask myself, “what does the data behind it say?” Subsequently, when I analyzed and better understood the data using Statistics, I would have a deeper insight as to what the best solution should be.
In summation, any incoming Freshman regardless of major should take these two courses at some point in their college career because of their immense benefits and real-world application