How to Become a Data Scientist: A Guide to Training, Skills, and Experience Needed

THROUGH Dawn RzeznikiewiczJanuary 25, 2022, 2:56 p.m.

A Starship food delivery robot delivers meals as students, faculty and others walk through the middle of a nearly empty University of California Irvine campus, as seen in January 2022. ( Allen J. Schaben—Los Angeles Times/Getty Images)

It may come as a surprise that the title “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 growing field, but the term and career only really took shape after the arrival of big technologies and the corresponding opportunity for analysts to find trends and solutions. in the data.

“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. “I discovered after a while that I had become a data scientist: people just started referring to me as such. So, now, I believe in it a little, ”he adds, laughing.

Since the data scientist career is relatively new to the scene, many working data scientists have taken somewhat indirect paths in this field of work. But because data science is becoming essential in almost every type of business, the academic offering in this discipline continues to grow. In reality, The wealth the first-ever ranking of the best online master’s programs in data science 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 an undergraduate technical degree

According to The State of Data Science 2021, a study conducted by Anaconda, a data science and machine learning platform, the majority of data scientists, 68%, have a college-level degree. Although the rise of online courses and certifications has made this step less mandatory – the remaining 32% of respondents did not hold any type of college degree – a bachelor’s degree involving technical skills remains 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 interested look into degrees that include programming and statistics. “In hindsight, if I had known at the time that I wanted to go into data science, I would have chosen a degree more related to computer science.”

2. Consider your area of ​​specialization

Whether you are currently in an undergraduate program or want to further your career in data science, it is important to consider the type of problems you seek to solve using data. Porfiri recommends investigating concrete examples of data science at work. “It’s critical not only to have the tools to analyze the data, but also to 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, looking for an internship, or considering some of the online certificates available.

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 in the areas in which he already had experience: finance and insurance. “Basically it comes down to your interests,” says Mishra. “I had friends who went on to work in industries they weren’t previously associated with. For example, a friend was interested in sports science, so he applied for jobs on different sports teams.

3. Build your skills with a master’s degree

A master’s degree is a way to explore different possibilities of specialization in data science. According to the Anaconda study, 24% of data scientists surveyed have a master’s degree and 10% a doctorate. Whether you’re pursuing an advanced program directly after your undergraduate or taking a gap between degrees, a master’s degree will keep your skills up-to-date with this rapidly changing field.

“Most master’s students want to catch up on new techniques that are coming in and be able to better develop their careers,” says Porfiri.

If you are pursuing a master’s degree in order to change careers, certain basic skills are necessary for success. If you didn’t acquire a basic knowledge of statistics, mathematics and computer science in your undergraduate or early career program, Mishra suggests spending 6 months in self-study to make sure you’re ready. to take the courses.

4. Showcase your work experience when applying for jobs

Two major sets of skills are involved in all data science roles: the technical skills learned in school and how these tools can be applied. “Machine learning and computing are tools,” says Mishra. “It’s important to understand how you use them and apply them to a particular organization or business.”

When it comes to getting a job, it can be helpful to bring examples of actual work you’ve done to your interview. If you haven’t worked in the industry before, certificates are a way to showcase the work you are capable of doing, as well as your own initiative. “That gives you an edge,” Mishra said.

What salary can data scientists expect to earn?

Data scientists are a well-paid role. Glassdoor reports that the average salary is between $90,000 and $170,000 with an average of $117,212, depending on your level. Meanwhile, the US Bureau of Labor Statistics reports that the average salary for data scientists was $103,930 in 2020.

Find out how the schools you’re considering landed in Fortune’s ranking of top business analytics programs, data science programs and part-time, executive, full-time, and online MBA programs.

Sean N. Ayres