What you need to know to move forward

What is the ideal career path for a data scientist? Fortunately, there are plenty of ways to become a data scientist – many tech professionals even spend mid-career on a data scientist track.

There are many reasons to embark on a career as a data scientist. Researchers Thomas H. Davenport and DJ Patil have reaffirmed in two separate studies (the second in 2022) that data science is the “sexiest job of the 21st century,” with sustained growth for the foreseeable future. Organizations around the world need data scientists who can process huge, often messy datasets to gain crucial insights.

“In 2019, data scientist posts to Indeed grew 256%, and the U.S. Bureau of Labor Statistics predicts that data science will see more growth than almost any other field by 2029,” they wrote. writes Davenport and Patil. “The job sought is generally quite well paid; the median salary for an experienced data scientist in California approaches $200,000.

But how do you concretely embark on a career as a data scientist? We’ll take a look.

Start with the right Data Scientist skills

HackerEarth’s 2021 Developer Survey found data science to be “the most coveted field for both students and professional developers.” But to become one, it takes a lot of practice. You will need knowledge of critical programming languages ​​such as Python and SQL which are used for database management and data analysis, as well as specialized tools and skills. For example, you will need to become familiar with the art of data structures and analysis.

Here’s a handy list of “core” data science skills, taken from Lightcast (formerly Emsi Burning Glass), which collects and analyzes millions of job postings across the country. If you can learn them, you’re well on your way to a successful data science career:

But how do you actually learn these skills and concepts? Many schools offer comprehensive courses in data science. If you want to learn on your own, there are also plenty of online tutorials and other resources. Here is a small sample of freebies:

In addition to some resources that will cost you dearly, although these come with certifications and proof of completion that you can list when applying for jobs:

Once you’ve gained some degree of proficiency, you should consider getting certifications, which can help you in your job search. Having certifications on a resume will assure hiring managers and recruiters that you have the skills needed for the job. Some popular certifications include:

  • Certified Analytical Professional (CAP), $495 for INFORMS members, $695 for non-members, in person at designated testing centers, at your own pace
  • Senior Data Scientist (SDS), DASCA: Cost: $775, online, at your own pace
  • SAS Certified Data Scientist$180 per exam, online, at your own pace

You might also consider certifications in specific languages, tools, and disciplines, such as Python or machine learning; but it also depends heavily on the industries and disciplines you want to focus on. Keep in mind that no matter how many certifications you earn, employers will want to test your skills through technical interviews (here are some sample key questions), and they’ll want to know that you actually understand the fundamentals of data science. data.

Data Scientist Career Path

For those entering the data science industry early in their tech career, it’s common to take on a junior or associate data scientist role, where you’ll learn a lot of the ropes of data science. From there, many data scientists embark on a career progression that eventually leads to a role of data architect or senior data scientist, and from there to a role of data science manager.

Others use their data science skills to launch a career in an adjacent field, such as an AI engineer. Regardless of your level or background, learning the principles of data science, machine learning, and data analytics is always valuable.

Skills acquired in other disciplines are also transferable to the data scientist. For example, economists and mathematicians work a lot with data, as do financial analysts. Software engineers often learn the crucial tools for data scientists. In other words, a mid-career transition to data science is certainly possible; Here are some common roles that transition into a data science career:

Whichever career path you choose, keep in mind that you will need “soft skills” such as communication and empathy to effectively communicate your analysis and results to all stakeholders in your organization. and beyond. With the right mix of soft and hard skills, your chances of success as a data scientist will only increase.

Sean N. Ayres