What Is a Data Scientist? The Simple Answer You've Been Seeking

what is data scientist

You’re finally ready to make some moves professionally. You are curious, are analytical and have a knack for math—and you know there has to be a career out there that puts that talent to good use.

One career field that just might fit the bill is data science. You’ve probably heard snippets about “Big Data” and data science in the media and have heard talk about how it’s going to shape the world for the better. That all sounds great, but by now you’re probably wondering, “What is a data scientist, anyway? And what do they do?”

We connected with several data scientists working in the field to help you understand both the big picture behind this booming career option and what is expected in a data scientist’s daily work. Keep reading to find out if a data science career is a fit for you!

The emergence of data science

Data science is a growing career field that’s emerged thanks to the technology of the information age. Much of its growth can be traced back to two words: data collection. The internet and the growth of ever-present mobile devices has led to an incredible amount of data being stored and collected. Much of this data was seen as a kind of refuse–receipts, logs, check-ins and so forth–that served a narrow purpose, but it hasn’t taken long for businesses and organizations to realize this data may in fact be a gold mine of potentially valuable information if properly refined. That’s where the data science field comes in.

"Intuition and experience don't always lead companies in the best direction,” says Evelyn Hytopoulos, chief economist and data scientist of PolySwarm. “In recent years, companies have been turning to data to inform business decisions.” Hytopoulos explains that data-driven insights help companies make more efficient and effective choices.

That kind of information can save a lot of money. “In today’s hyper-competitive world, mistakes can be very costly,” says Prabhath Sirisena, co-founder of DonorDo. “To reduce mistakes in the decision-making process, the current norm is to base them on data.”

Data scientists have the unique ability to deliver these valuable data-driven insights, according to Hytopoulos. “We use a diverse set of skills that are claimed by many but mastered by few. The best have skills in data engineering, statistics, economics, programming and communication.” She explains that these skills combine to create professionals who can manipulate data, analyze trends and know where to look for the most powerful insights.

“Organizations today collect more data than ever,” Sirisena says. “And data scientists are required to analyze this data and build models to understand what they engender.”

What does a data scientist do?

A data scientist looks at all of that data and pulls out relevant information, spotting connections and insights that have an effect on the way their company does business. It sounds simple, but the daily job duties reveal that data scientists have a lot on their plates.

“Similar to the experience of many data scientists, I am constantly forecasting what questions I may need to answer in the future and devising ways to starting gathering relevant information now,” Hytopoulos says. “Using advanced statistical and econometric methods and pulling in both primary and secondary data sources, my job is to answer what happened, what is happening, what are the best maneuvers now—and what is coming.”

Some common daily tasks for data scientists include acquiring data from sources that may not always be obvious place to look, strategizing ideal methods of operating with data sets, manipulating, cleansing and standardizing data and analysis, according to Elijah Elazarov, data scientist at Exeq. After that process, data scientists will present their discoveries in ways that are clear, concise and actionable.

On top of measuring current analytics and forecasting future ones, data scientists can also become team members on projects relevant to their expertise. Sirisena is working as a machine-learning expert helping to build a new product. “I work on finding data sources that can help achieve a given objective,” Sirisena says. “I’m collecting lots of data (e.g., Twitter™ feeds), building models to understand the behaviors of entities involved (e.g., users, companies) and validating results.”

A data scientist’s job duties will largely depend on their company’s needs. Some companies may require them to do everything from data collection to data interpretation, while other companies will provide more specific roles, like focusing only on sales data.

One thing is clear: like the name implies, a successful data scientist needs to genuinely enjoy drilling down into data. But it’s not all statistics and programming. A large portion of a data scientist’s job is being able to explain complex ideas and findings to others who may not be familiar with the technical jargon. That means soft skills such as communication are essential.

Our experts pointed to these hard and soft skills as necessities for a successful data scientist:

Hard skills:

  • Structured Query Language (SQL)
  • Programming languages (especially Python, R and Java™)
  • Working knowledge of statistics, linear algebra and vector calculus
  • Focused training for your industry (Transaction data, social media data and contact/lead data)
  • Economic skills and knowledge base

Soft skills:

  • Communication (“data storytelling”)
  • Innovative thinking
  • Intuition
  • Precision and attention to detail
  • Patience with long tasks

The future of data science

It’s obvious data science is a hot career now, but where is it headed in the future?

“In years to come, I expect that the population of executives and industry leaders will be composed of more data scientists who focus on leading their teams in data-derived directions,” Hytopoulos says.

Data science is an interdisciplinary domain, according to Sirisena—who expects the reach of data science to continue to expand. Advances in technology will also push data science further. “As computers become faster, models will lean toward deep learning—a method that is loosely based on the organic neural network structure in the brain,” Sirisena explains.

As data science progresses, the scientists who push the boundaries of the job by creating tools, methods and unique practices with data will stand out from the crowd, according to Elazarov. “Paint a picture another data scientist or data analyst may not be able to depict.”

How do you become a data scientist?

Like with other relatively new or maturing career fields, the path to becoming a data scientist isn’t quite as structured or rigid as it’d be for someone looking to become, for example, a nurse or an accountant. Many current data science professionals have Bachelor’s degrees (and beyond) in subjects like Computer Science, Mathematics, Data Analytics or Statistics and have found their way into the field through informal training.

This somewhat tricky-to-navigate career path in data science is beginning to smooth out, though. While a Bachelor’s degree in any of the areas mentioned above still provides a great foundation, more schools and institutions are developing programs to provide training that is much more focused in the field of data science, specifically.

While it’s great that academic programs now exist for people interested in the field, there’s still the question of knowing what employers are seeking. To help answer this, we’ve used job-posting analysis software to see what employers expect of candidates for data scientist positions. In our analysis, we found nearly 57 percent of Data Scientist job postings were looking for candidates with a minimum of a Bachelor’s degree—and another 33 percent were looking for candidates with at least a Master’s degree.* This reflects some of the newness of the field—a lack of a graduate degree may not be a disqualifier with the right background and experience.

We also took a look at the level of experience employers seek in data scientists and found that just over 80 percent of job postings preferred candidates with three or more years of experience.* This indicates you’ll need to build your resume a bit with data science-adjacent roles and personal projects to fit the mold of an ideal candidate.

Is “data scientist” your next job title?

There’s a lot to like about the field of data science—it’s a career that’s been recently thrust into the spotlight, and businesses have taken a strong interest in investing in data capabilities in order to gain a competitive edge. That said, the road to becoming a data scientist will take significant time and training. If you’re convinced this is the field for you, then your first step will be building a foundation of programming and mathematical knowledge. The good news is that a Data Analytics bachelor's degree can help provide that foundation in the near term—and a Master of Science in Data Science degree will help you stand out as you build on that foundation.

*Burning-Glass.com (analysis of 23,837 Data Scientist job postings, June 6, 2017 – June 5, 2018)
EDITOR'S NOTE: This article was originally published in August 2015. It has since been updated to reflect information relevant to 2018.

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Brianna Flavin

Brianna is a content writer for Collegis Education who writes student focused articles on behalf of Rasmussen College. She earned her MFA in poetry and teaches as an adjunct English instructor. She loves to write, teach and talk about the power of effective communication.

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