Undergraduate Majors That Fill Big Data Jobs - dummies

Undergraduate Majors That Fill Big Data Jobs

By Jason Williamson

Universities today recognize the growing demand for big data talent. They’re building programs and classes around analytics, business intelligence, database management, and the supporting computer programming classes. There are two primary undergraduate paths to landing that first job in big data:

  • You can find a major with a specific program in analytics and data science.

  • You can tailor a traditional major with certain courses.

Tailoring your path simply means adding those classes or electives that you need to round out your skills so that you can compete for big data jobs. It’s okay if your degree doesn’t say “analytics” if you have a course load that fills the needs of potential employers.

The foundation of these majors can be found in one of three primary degree paths: math and statistics, computer science and engineering, or business.

Math and statistics

Many people pursuing the traditional title of data scientist come from an educational background grounded in math and statistics.

Although you won’t typically find specific degrees in big data, you will find specialized research programs and degree tracks that are useful to building your college résumé for data science jobs after school. Within math and statistics, you’ll be required to have a deep foundation in probability theory, computational theory, and statistics.

These tracks tend to be cross-disciplinary in nature. This means that you often take courses outside the math department to build the required foundation for big data. You may find yourself taking programming classes from computer science and marketing classes from the business school.

In a recently funded initiative at the College of William & Mary, the department of mathematics received a grant from the National Science Foundation to build a program to help undergraduate students in the study and research of statistical theory and the analysis of very large datasets.

Students who participate in this program finish with a degree in math and still are taking linear algebra, data analysis, probability, and statistics. What’s different is that programs like the one at William & Mary bring the ideas of big data into the classroom.

Is a math degree from any university just as good as a math degree from any other university? Here’s the secret: Those universities that have invested real dollars in terms of both research and programs have a firm commitment to big data — not just in name only.

Any topic of research that a professor is researching makes its way into the classroom. Has the university you’re considering published any research on big data? Does the math department take part in joint programs across the university or with the private sector? If the department does, your chance of being exposed to big data topics during your educational experience greatly increases.

Computer science and engineering

A foundation in math and statistics is required for this field. Expect to be spending time in the math department. Don’t worry — you don’t have to go very deep into number theory and probability theory (you need cursory knowledge but you don’t have to take a whole course in these subjects). You do need an in-depth knowledge (the kind that comes from taking a class in) of the following subjects:

  • Artificial intelligence (AI): Artificial intelligence may conjure images of IBM’s Watson on Jeopardy! AI is a field of study that tries to mimic human ways of learning in software. It’s often used in text analytics and sentiment measurement.

  • Machine learning: Machine learning is a subcategory of AI focused on developing computer algorithms that improves a computer’s capability to process information through experience.

  • Data theory: Data theory is the study of optimizing how to store, organize, and retrieve data.

This path allows you to explore technologies that make big data possible. You may learn the necessary skills to drive technical innovations in the big data field.

When people think about big data and the types of programmers who have been innovating in this field, a natural assumption is that progress has been pushed by database developers. Technologies like MapReduce, NoSQL, and Hive don’t come from database people; instead, software engineers created it because they needed a way to manipulate massive datasets that traditional relational databases systems couldn’t provide. MapReduce is run on Google clusters every day.

Ask yourself these questions. Does the computer science department or engineering school invest in, publish, or research topics in big data? Do the professors of the computer science classes express interests in the field? Do your homework. Check out their websites, surf faculty pages, and see what they’re researching and publishing.

Many universities maintain their computer science degrees within the engineering school or the college of arts and sciences. In many cases, they reside in both. Furthermore, some schools offer both a bachelor of science and a bachelor of arts in the field of computer science, depending upon the school.


So, what is the difference between a degree in computer science from an engineering department and a degree in management information systems from a business school?

Much of the software engineering classes for both degrees are similar. An oversimplified explanation: In computer science, you learn how to make computers run fast; in management information systems or computer information systems or other technical business concentrations, you learn how to use computers to make money. Big data in the business school context is still technical in nature, but it’s focused on solving problems in marketing, product placement, and buying patterns.

Business schools offer a wide variety of specific degrees in analytics with a rich set of classes in business intelligence, predictive analytics, and cloud computing. Although the coursework is not as technically demanding as the math and engineering paths, you’ll be sure to get hands-on training in database design, analysis, and programming with statistics tools such as Hadoop and SAS.