By Kaylin Li, Press and Written Media Staff
You’ve probably heard that the amount of data in our world is rapidly increasing, with new digital content being created and information stored every day. Perhaps you’ve even heard of “big data,” a term that refers to vast sets of data collected by different fields and the analyses that can be performed on it.
With this increase in data comes an increased need for people to make sense of that data, which is why a fairly new major called data science has been on the rise. Whether your interests lie in the humanities or STEM, data science is a good major to consider because just about every field and industry collects and analyzes data, whether it be art history, the stock trading industry or genetic laboratories. Here’s a quick breakdown of the major:
1. Data science is a broad field with a lot of different career paths
With so much data in the world needing analysis and data science being such an interdisciplinary field, graduates go into many different industries. Here are some professions that data science majors often go into:
Data analysts: Data analysts collect and handle high quantities of data and work in places ranging from nonprofits to companies to universities. They analyze political campaigns, income disparities, the efficacy of medical products, prices in the stock market and a plethora of other information. They spend a lot of time cleaning up messy data and drawing conclusions through programming. They also can focus on data visualization as well, creating charts and infographics that help those unfamiliar with data understand their conclusions.
Machine learning and artificial intelligence engineers: Machine learning engineers create computer models for predicting behavior and train those models to become more accurate. Machine learning is used for everything from facial recognition to personalized Google search results. Artificial intelligence is essentially machine learning except the model aims to mimic human intelligence, so the model is learning by itself.
Software developers and engineers: Since data science majors learn so much programming, many of them are hired as software developers and engineers as long as they have the adequate skillset.
2. Programming is essential to data science
It’s hard to analyze large amounts of data if you don’t have code to help. What programming technologies and how familiar you need to be with it depends on what you plan on doing after college and how you want to use your skill set. If you are going for more of a data analysis and data visualization path, just knowing Python or R can get you pretty far. If you are going for more involved programming roles, analyzing big data and generating models for predictive behavior like machine learning or artificial intelligence, you’ll have to learn more programming languages like Java and C++/C#/C, and database related technologies such as SQL, MySQL and MongoDB.
3. You’re also going to need math skills.
Specifically, you’re going to have to learn statistics, a field in math in which you use mathematical models to draw conclusions and predict potential outcomes. Since one of the most central aspects and uses of data science is predicting behavior, statistics is pretty important and you should expect to take a substantial amount of math courses. Additionally, computer science and technology-related majors typically require a lot of math coursework, including algebra, geometry, calculus and sometimes proof-based math. If you’re not that into math, that’s OK! But if you don’t plan on looking at equations ever again, then maybe data science isn’t for you.
Should I apply to be a data science major?
If you’re a high schooler with some interest in STEM, you should definitely consider data science. It’s best if you have some basic familiarity with a programming language and a good track record in math. But it’s also beneficial if you are interested in subjects outside of STEM, such as history, literature or even music, since you can use data science in these fields to draw helpful conclusions.
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