It may come as a surprise that the title of “data scientist” is relatively new—in fact, it was coined in 2008 by two data analytics professionals at LinkedIn and Facebook. Today, we know it as a fast-growing field, but the term and career really only took shape after the arrival of big tech and the corresponding opportunity for analysts to find trends and solutions within data.
How to become a data scientist: A guide to the education, skills, and necessary experienceBY Dawn RzeznikiewiczJanuary 25, 2022, 2:56 PM
“It’s a weird thing because it’s very vague,” says Maurizio Porfiri, a frequently published Institute Professor at New York University’s Tandon School of Engineering and an IEEE fellow. “I discovered after a while that I had become a data scientist: people just started to refer to me as such. So, now I kind of believe it,” he adds with a laugh.
With the data scientist career relatively new-to-the-scene, many working data scientists have taken somewhat indirect paths into this line of work. But because data science is becoming essential in almost every type of business, the academic offerings in this discipline continues to grow. In fact, Fortune’s first-ever ranking of the best online master’s in data science programs includes 15 schools.
Below is a step-by-step guide to the type of education, skills, and experience needed to become a data scientist.
1. Get a technical undergraduate degree
A majority of data scientists—or 68%—have a college-level degree, according to The State of Data Science 2021, a study conducted by Anaconda, a data science and machine learning platform. While the increase in online courses and certifications have made this step less of a requirement—the remaining 32% of respondents didn’t hold any type of college degree— a bachelor’s degree involving technical skills is still the most direct entry into the field.
Shray Mishra, a machine learning engineer at Tower Hill Insurance Group didn’t get his bachelor’s degree at a time when data science was an option. Even so, he suggests those people who are interested look into degrees that include programming and statistics. “In hindsight, if I had known at that time that I wanted to go into data science, I would have selected a degree more in line with computer science.”
2. Consider your area of specialization
Whether you’re currently in an undergraduate program or want to further your career in data science, it’s important to consider the type of problems you’re looking to solve using data. Porfiri recommends investigating some real-life examples of data science at work. “It’s critical not only to have the tools for analyzing the data, but to also get your hands dirty in real datasets, and understand how to move them forward,” he says.
Depending on where you are in your career, Porfiri suggests talking to your professors, getting involved in a research project, seeking an internship, or considering some of the available online certificates.
Mishra had been in the working world for about three years when he enrolled in a master’s program in data science, and knew he wanted to develop his skills for the areas he already had experience in: finance and insurance. “Basically, it boils down to your interests,” Mishra says. “I had friends who went on to work for industries they were not previously associated with. For example, one friend was interested in sport sciences, so he applied for jobs on different sports teams.”
3. Develop your skills with a master’s degree
A master’s degree is one way to explore different possibilities for a specialization in data science. According to the study by Anaconda, 24% of surveyed data scientists have a master’s degree and 10% have a doctorate. Whether you pursue an advanced program directly after your undergraduate or take a gap between degrees, a master’s degree will keep your skills up-to-date with this rapidly evolving field.
“Most master’s students want to get up to speed with the new techniques that are coming up, and be able to better develop their career,” says Porfiri.
If you’re pursuing a master’s degree in order to switch careers, some basic skills are needed in order to be successful. If you didn’t get a base-level knowledge of statistics, mathematics, and computer science in your undergraduate program or an early career, Mishra suggests spending 6 months self-teaching to ensure you’re ready to take on the coursework.
4. Showcase your work experience when applying for jobs
There are two major skill sets involved in all data science roles: the hard-skills learned in school and how these tools can be applied. “Machine learning and computer science are tools,” Mishra says. “It’s important to understand how you use them, and apply it for a particular organization or business.”
In terms of landing a job, it can help to bring examples of the real-life work you have completed into your interview. If you’ve yet to work in the industry, certificates are a way to showcase the work you are able to do, as well as your own initiative. “It gives you an edge,” says Mishra.
What salary can data scientists expect to earn?
Data scientists are a high-paying role. Glassdoor reports the average salary as anywhere in the range of $90,000 to $170,000 with an average of $117,212, depending on your level. Meanwhile, the U.S. Bureau of Labor Statistics reports the average salary for data scientists was $103,930 in 2020.