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A common concern among aspiring data scientists is if they have to be good at math to study data science. Data science is one of the top careers in America today, so candidates interested in this career are scrambling about making sure they’re adequately qualified. Although data science is all about big data, does that necessarily mean it’s all about numbers and math? Here is some valuable information on data science and its dependence on math.

Related resource: 20 Best Data Science Bachelor’s Degree Programs

What is Data Science?

Data science is an interdisciplinary field that combines computer science, statistics, and mathematics with data at the core of the discipline. Data science works with data of all kinds and sizes but mostly deals with big data. Statisticians and mathematicians analyze data to create algorithms and solve problems whereas data scientists do so much more. Data scientists not only sift through data but determine where the data originated and what can be done with it. The data use is either structured or unstructured.

Structured data involves words and numbers. An example of structured might be a company’s balance sheet or expense report. Unstructured data involves more complicated things that typically cannot be sorted numerically. An example of unstructured data might be customer reviews, social media posts or similar things that need to be sorted and analyzed in different ways. We may not realize this, but data science is used in almost all areas of our lives and in every industry.

How Important is Math in Data Science?

To be truly successful in data science, an individual should have some background in math or at least be proficient in math because data science tends to rely on numbers. However, a person interested in data science doesn’t have to be a mathematician or doesn’t have to love math because there are some areas of data science that aren’t based solely on mathematics.

Big data is usually defined as something that cannot be solved or analyzed using traditional methods, but that doesn’t necessarily mean by using math. Machine learning is the perfect example. It uses statistical techniques to help computers learn things but doesn’t rely heavily on math. Data science may be analytical, but it’s more statistical than mathematical.

Career Opportunities in Data Science

The field of data science offers many career opportunities. Some of these careers require a good background in math while others rely more on computer science, computer programming or data analyzing. Although data science is still a relatively new field, U.S. News & World Report states that data scientists can work in a variety of settings, including government agencies, tech startups, research institutes and various companies. It also offers employees the flexibility to work with people from various backgrounds. Some jobs available in data science include:

• Data scientist

• Data analyst

• Data engineer

• Business analyst

• Machine learning engineer

• Engineering manager

Career Outlook in Data Science

Data scientists are very much in demand mostly because there are not near enough of these professionals needed to fill the many data science jobs. The U.S. Bureau of Labor Statistics projected a job growth of 19 percent for computer and information research scientists from 2016-2026. As of May 2017, these professionals earned a median wage of $114,520.

One of the great things about data science is that it offers a multitude of career opportunities in areas that are not totally math-related, such as data engineer, data analyst, and machine learning engineer. While it is quite beneficial for an individual to be good at math to study data science, aspiring data scientists can find excellent career opportunities within the field without being a mathematician.


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