So, you’re interested in “the sexiest job of the 21st century“? That’s how the Harvard Business Review described “data scientist” in 2012, and it’s still accurate today—if you define “sexy” as “in demand” and “highly salaried“. Data science—essentially a mix of statistics, computer science, and mathematics—is a relatively new field, borne from the inundation of data in our modern lives. Since “big data” is not going anywhere, this looks to be a secure field well into the foreseeable future.

Data scientists gather and organize large amounts of data to solve process and strategy problems in business and in other enterprises (e.g., nonprofit organizations, academic institutions, government). The job requires expertise in computer programming and software applications, statistics, data analysis, data visualization, and strategy. Many roles in the field also require robust communication skills, since part of the job of a data scientist is conveying complex analyses to stakeholders who lack the proficiency to understand it unfiltered.

If this sounds like a dream job to you, this article can help you get started. In this guide to how to become a data scientist, we’ll cover:

• Pros and cons of becoming a data scientist
• Kinds of data scientist careers
• Educational commitment to become a data scientist
• Further accreditation or education for a data scientist
• Typical advancement path for a data scientist
• Resources for becoming a data scientist

Pros and cons of becoming a data scientist

Pros of becoming a data scientist

• Excellent job prospects: Data scientist is LinkedIn’s number one most-promising job, with a median base salary of $130,000 per year. Furthermore, LinkedIn also assigns data science a career advancement score of nine out of 10, meaning you’ll move up the ladder quickly. The Bureau of Labor Statistics projects a job growth rate of 16 percent for data scientists; that’s nearly three times the growth rate of the overall job market. No wonder Glassdoor calls data scientist “the best job in America.”
• Versatility: Data scientists can work in many different spheres of industry, including healthcare, e-commerce, banking, marketing, and consulting. They can also work in government, academics, nongovernmental organizations, and other nonprofits. Some specializations tie you to a specific business or function. The opposite is true with data science; it can be your ticket to any endeavor that uses data to drive decisions.
• Challenging work: Data science combines mathematics, statistics, computer programming, and strategy. This is not a job for people who want to turn off their brains, but if you like solving puzzles and other brain-teasers, this role will keep them coming. The types of problems data science addresses are quite varied, so it’s unlikely you’ll get stuck addressing the same questions over and over. The challenges should be as unique as they are formidable.

Cons of becoming a data scientist

• Privacy issues: As a data scientist, you’ll be at the center of one of the hottest controversies of the modern world: online privacy. The ethical issues surrounding the gathering and use of these data don’t seem likely to be resolved any time soon.
• Fast-changing landscape: This is a field that evolves rapidly, meaning that you’ll need to make a significant commitment to staying up-to-date with advances and best practices in your field to remain relevant and in-demand. This is true in all professions, but the burden is especially great in fields like data science, where changes can be rapid and dramatic.
• Bro culture?: With a seven-to-three male-female ratio, data science has better gender balance than some other computing and engineering fields, but it’s still a male-dominated profession. The upside of this is that opportunities await at companies that are looking to diversify. The downside is that entrenched cultures can create formidable barriers; you have to decide whether you want to be part of the fight to dismantle them.
• Generalist approach: The diversity of the work, mentioned above, has a drawback: as a data scientist, you probably won’t really delve too deeply into one topic. If you want to be the master of a particular field, data science might not be for you. It also means that there’s no industry-standard definition for what a data scientist does. Cassie Kozyrkov, Google’s Chief Decision Scientist, considers a data scientist an expert in applied machine learning engineering, statistics, and analytics, but admits that not everyone agrees with her on that.

Kinds of data scientist careers

There’s no fixed definition of “data scientist,” so data science roles can go by many names. Depending on whether you’re concerned with the stories data tell, the tools for leveraging data, or the infrastructure that houses data, you could be a:

• Business analyst
• Data analyst
• Data engineer
• Data science generalist
• Machine learning engineer
• Marketing analyst
• Statistician

You’ll need to set up a bunch of different keyword search terms at the various job listing sites when you become a data scientist.

Educational commitment to become a data scientist

The tech world offers many jobs you can get without even earning a bachelor’s degree. Not that it’s easy, but it is possible to train yourself—through online boot camps and other courses—in the programming, coding, and troubleshooting chops necessary to get a decent-paying job (e.g., desktop developer, web developer).

That’s not the case in data science, however. According to KD Nuggets, a data science resource, 88 percent of data scientists have at least a master’s degree and 46 percent have PhDs. Those degrees may be in data science or in data analytics, data engineering, applied statistics, business intelligence, or a similar discipline, but in the end they all circle back to gathering and interpreting large amounts of data. Top schools offering these degrees include:

• Boston University
• Carnegie Mellon University
• Columbia University
• Drexel University
• Georgia Institute of Technology – Main Campus
• Johns Hopkins University
• New York University
• Northwestern University
• Pennsylvania State University – World Campus
• University of Wisconsin – Madison

You’ll need to learn how to use special programs like Hadoop, a software utility used for storing and processing big data. You’ll also need to be fluent in at least some programming languages, such as R, Java, Python, and SQL.

Further accreditation or education for a data scientist

As previously mentioned, the learning never ends in data science. Earning a data science certification, either on-campus or online, can boost your know-how as well as your value. Certifications worth considering include:

• CAS Institute Predictive Analytics and Data Science
• Certified Analytics Professional
• Data Science Council of America Principal Data Scientist
• Data Science Council of America Senior Data Scientist
• Harvard University Professional Certificate in Data Science
• SAS Big Data Professional
• SAS Certified Data Scientist

Typical advancement path for a data scientist

You guessed it: there isn’t really a typical path. With their varied skill sets, data scientists can work at software companies, tech companies, business research and development departments, colleges and universities, even the federal government. Here’s a broad breakdown:

• Data scientists typically major in math, computer science, economics, or physics, and need knowledge of algorithms, statistics, math, and programming languages like R and Python. They work in fields like medicine, business, and academics.
• Data engineers typically major in computer science and engineering, and need an in-depth knowledge of data storage and SQL, as well as Hadoop. They can work in fields like software, IT, and aerospace.
• Data analysts typically major in statistics, business, and economics, and need knowledge of data manipulation and Excel. They can work in fields like banking, consulting, and healthcare.
  

  Resources for becoming a data scientist

The internet offers a broad variety of online resources for aspiring data scientists, including:

• Free public data sets to play with
• Tips for learning Python and R
• Data mining algorithms
• Information about the state of data scientist salaries in 2019
• Data science competitions hosted by Kaggle, a Google-owned online community of data scientists and machine learners.
• You can find more job boards at Datajobs and builtin

Should you become a data scientist?

Experts generally agree that demand for data scientists is strong and should remain that way into the future. As LinkedIn co-founder Allen Blue put it: “There are very few data scientists out there passing out their résumés. Data scientists are almost all already employed because they’re so much in demand.”

If the prospect of working with massive data sets excites you, there’s no reason not to pursue a career in data science. This isn’t buggy-whip-manufacturing; this business is not going away any time soon. One of the benefits of entering a generalist profession is that it provides more latitude to pivot with changing times and trends. What you’re doing as a data scientist 30 years from now will likely look nothing like what you’re doing today, but it will still be data science, and there should still be plenty of opportunities in the field.

Questions or feedback? Email editor@noodle.com