How (and Why) to Become a Data Scientist

Hand holding ball with binary code written on it.

From helping Netflix guess what people will enjoy watching next, to creating safer self-driving cars, data can be made to do wonderful things. But it takes a data scientist to transform big data from meaningless figures to valuable information. And with more data being collected than ever before in the history of humanity, this field is exploding in popularity.

Along with the highly competitive pay, data science has become an increasingly popular career path for students and graduates alike. But how do you become a data scientist, and what does a data scientist’s career progression look like?

The Career Path of a Data Scientist

In order to become a data scientist, you’ll need to develop several interrelated skills. That includes a background in mathematics, communication skills, analytical ability, and more. Because you can obtain these skills through a variety of different degrees, jobs, and industries, there is no single path a data scientist must follow.

A degree in data science is certainly not necessary to become a data scientist, and there are typically no specific degree requirements for these positions. But regardless of how they acquire the necessary skills, a prospective data scientist will need to be familiar with a variety of disciplines. That includes computer science, mathematics, coding, and several other skills that you can start developing as an undergraduate.

Undergraduate Studies

Among the many skills needed for a data scientist, none are as important as their analytical abilities. Data scientists need to determine what is and isn’t relevant information, and how to analyze it. You can develop analytical ability from numerous kinds of college courses, from economics to philosophy.

Outside of analysis, data scientists need a background in applied mathematics, especially statistics. It’s equally important to develop hands-on experience working with data, and to become familiar with popular coding languages like Java and C++. Strong communication skills are also necessary to help communicate findings to a non-technical audience.

Advanced Degree

Just like earning a data science degree is not necessary to become a data scientist, earning an advanced degree is not necessary, either. However, there are reasons to consider pursuing an advanced degree in a related field.

In the short term, having an advanced degree in a related field can often make you a more competitive candidate. More education and experience is almost always to your benefit. In the long term, an advanced degree positions you for the possibility of promotion into a senior role.

Career Progression to Data Scientist

After obtaining your degree, the next step in a data science career path is to start gaining relevant experience. As with your education, gaining experience can be done in a variety of positions and industries. A junior data analyst position is usually the most straightforward starting point for a career in data science. But you can pursue any kind of position that allows you to develop firsthand experience working closely with data.

With the benefit of an entry-level position in the field, you can begin to develop as a professional and acquire the experience necessary to excel. Although data science career progression doesn’t follow a formula, as with many high-skilled careers, more experience does allow for more career growth.

Do you enjoy working with numbers, statistics, and data? Outside of learning how to be a data scientist, you can find a wide variety of data-driven careers in the insurance industry. Take a moment to learn about Data Science Insurance Careers or take our Career Wizard Tool to discover another risk management industry career that might be up your alley.