Career Spotlight: Data Scientist

Data Scientist circling information on a screen

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Today “big data,” “analytics,” and the like are trending buzzwords. And for good reason.

Back in 2012, HBR named “data scientist” as the “sexiest job of the century.” But what does data science truly entail? And more importantly, how can you acquire the skills needed to call yourself a data scientist?

What Is Data Science?

Once upon a time, data scientists were mostly in the academic space. Now, with the upsurge of big data collection and the need for analysis, data scientists have become in high demand in a range of companies and industries, small and large.

Data science as a profession incorporates a range of skills within mathematics, statistics and computer programming. It is an industry dominated by men; estimates of women in data science are around 10%.

According to Glassdoor, the average national salary for data scientists is $113,436. Looking at compensation alone, data science is a lot more attractive than other similar careers.

Skills Needed to be a Data Scientist

Like all jobs, the specific skills required to fill data science positions depend on the individual company.

But there are certain skillsets/software tools that remain consistent.

  • Statistical programming languages, like R and SAS
  • Database querying language such as SQL
  • Basic statistics such as statistical tests, distributions, maximum likelihood estimators, and so forth
  • Machine learning methods such as k-Nearest neighbors, random forests, ensemble methods, etc.
  • Multivariable calculus and linear algebra
  • Data logging and development of new products that are data-driven
  • Familiarity with Hadoop platforming
  • Visualization tools such as Flare, HighCharts or AmCharts

How to Become a Data Scientist

Nowadays, there are three viable options for becoming a data scientist:

  • Self-study via programs like Udacity
  • Attending a data science boot camp
  • Going to graduate school for a master’s degree

Of course, there are pros and cons to each method.



  • Convenient: can be done on your own time in any environment and at any pace.
  • Affordable: could cost anywhere from $0-600.
  • Saves time: online courses can be completed within eight to 18 months.


  • Only receive a certificate after completion
  • No peer-to-peer or teacher-to-student involvement
  • No assistance with job hunting

Data Science Boot Camp


  • Little time commitment: can be completed in six weeks to three months
  • Relatively affordable, at least compared to getting a master’s degree (boot camps range from free - $16,000)
  • Ideal for those looking to change careers quickly
  • Many boot camps offer assistance in the job search process after completion


  • Only get a portfolio of projects - no "real" work experience 
  • A lot to learn in a short amount of time
  • Could be up to 40 hours a week of work (unlike self-study where you can go at your own pace and still work part- or full-time)

Master’s Degree


  • Diploma upon completion
  • Structured learning with professionally trained instructors
  • Real-world experience: many programs include internships that will add to experience and knowledge
  • Ample time to learn and absorb all of the information


  • Expensive: could cost between $20,000- $70,000 -- not including living expenses
  • Time-consuming: can also take the longest (nine to 20 months)