What is a Data Scientist?
Who they are, what they do and why you want to be one
Data scientists are a new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved.
They’re part mathematician, part computer scientist and part trend-spotter. And, because they straddle both the business and IT worlds, they’re highly sought-after and well-paid. Who wouldn’t want to be one?
They’re also a sign of the times. Data scientists weren’t on many radars a decade ago, but their sudden popularity reflects how businesses now think about big data. That unwieldy mass of unstructured information can no longer be ignored and forgotten. It’s a virtual gold mine that helps boost revenue – as long as there’s someone who digs in and unearths business insights that no one thought to look for before. Enter the data scientist.
Where did they come from?
Many data scientists began their careers as statisticians or data analysts. But as big data (and big data storage and processing technologies such as Hadoop) began to grow and evolve, those roles evolved as well. Data is no longer just an afterthought for IT to handle. It’s key information that requires analysis, creative curiosity and a knack for translating high-tech ideas into new ways to turn a profit.
The data scientist role also has academic origins. A few years ago, universities began to recognize that employers wanted people who were programmers and team players. Professors tweaked their classes to accommodate this – and some programs, such as the Institute for Advanced Analytics at North Carolina State University, prepared to churn out the next generation of data scientists. There are now more than 60 similar programs in universities around the country.
“My days can be very similar but week-to-week work can vary greatly. For a few weeks I might be working on a text mining project, and after that I could be creating a predictive model around the customer. Mixed in are meetings with others about analytics and how it can help different parts of the business.”
Data scientist for a major US retailer
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Kirk Borne, PhD, Principal Data Scientist at Booz Allen Hamilton, addresses the misconception that data science is an IT function – and how data scientists can help in the new era of bigger, more complex data.
More on data scientists
- Read an interview with a university leader on Preparing a new generation for leadership in a big data world.
- Meet a data scientist: Interview with Kristin Carney.
- Find out more about data scientists in our data science Insights series.
- Download a white paper on Getting Value From Your Data Scientists.
Resources for getting started
- Learn the basics with free Programming 1 and Statistics 1 eLearning Courses, as well as how-to videos on YouTube
- Download SAS University Edition - free SAS software that allows students and learners to use analytics
Typical job duties for data scientists
There's not a definitive job description when it comes to a data scientist role. But here are a few things you'll likely be doing:
- Collecting large amounts of unruly data and transforming it into a more usable format.
- Solving business-related problems using data-driven techniques.
- Working with a variety of programming languages, including SAS, R and Python.
- Having a solid grasp of statistics, including statistical tests and distributions.
- Staying on top of analytical techniques such as machine learning, deep learning and text analytics.
- Communicating and collaborating with both IT and business.
- Looking for order and patterns in data, as well as spotting trends that can help a business’s bottom line.
What’s in a data scientist’s toolbox?
These terms and technologies are commonly used by data scientists:
- Data visualization: the presentation of data in a pictorial or graphical format so it can be easily analyzed.
- Machine learning: a branch of artificial intelligence based on mathematical algorithms and automation.
- Deep learning: an area of machine learning research that uses data to model complex abstractions.
- Pattern recognition: technology that recognizes patterns in data (often used interchangeably with machine learning).
- Data preparation: the process of converting raw data into another format so it can be more easily consumed.
- Text analytics: the process of examining unstructured data to glean key business insights.
“On a typical day, I brainstorm and problem solve how to answer questions that come from the business with my team, I review analysis and recommendations completed by my staff, and I attend a variety of meetings.”
Data scientist, World’s Foremost Bank
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How can you become a data scientist?
Positioning yourself for a career in data science could be a smart move. You’ll have plenty of job opportunities, plus it’s a chance to work in the technology field with room for experimentation and creativity. So what’s your strategy?
If you’re a student
Choosing a university that offers a data science degree – or at least one offering classes in data science and analytics – is an important first step. Oklahoma State University, University of Alabama, Kennesaw State University, Southern Methodist University, North Carolina State University and Texas A&M are all examples of schools with data science programs.
If you’re a professional who wants to shift careers
While most data scientists have backgrounds as data analysts or statisticians, others come from non-technical fields such as business or economics. How can professionals from such diverse backgrounds end up in the same field? It’s important to look at what they have in common: a knack for solving problems, the ability to communicate well and an insatiable curiosity about how things work. Learn how the SAS Academy for Data Science gives you the tools to become a certified data scientist.
Aside from those qualities, you’ll also need a solid understanding of:
- Statistics and machine learning.
- Coding languages such as SAS, R or Python.
- Databases such as MySQL and Postgres.
- Data visualization and reporting technologies.
- Hadoop and MapReduce.
If you don’t want to learn these skills on your own, take an online course or enroll in a bootcamp. And then, of course, you should network. Connect with other data scientists in your company, or find an online community. They’ll give you insider information into what data scientists do – and where you’ll find the best jobs.
When is a business ready to hire a data scientist?
Before you accept a data scientist position, there are a few things about the organization you should evaluate:
- Does it deal with large amounts of data and have complex issues that need to be solved? Organizations that truly need data scientists have two things in common: They manage massive amounts of data, and they face weighty issues on a day-to-day basis. They’re typically in industries such as finance, government and pharma.
- Does it value data? A company's culture has an impact on whether it should hire a data scientist. Does it have an environment that supports analytics? Does it have executive buy-in? If not, investing in a data scientist would be money down the drain.
- Is it ready to change? As a data scientist, you expect to be taken seriously, and part of that entails seeing your work come to fruition. You devote your time to finding ways your business can better function. In response, a business needs to be ready – and willing – to follow through with the results of your findings.
Hiring a data scientist to guide business decisions based on data can be a leap of faith for some organizations. Make sure the business you might be working for has the right mindset – and is ready to make some changes.
“I work for an agile company, which requires me to be flexible and adapt to circumstances. Last week, for example, I was doing several tasks, including improving recommendation scores; tuning the integration with the operational content management system; creating new transformed variables based on consumer behavior to be used for affinity models; and doing some refactoring of existing performance reports/analytical dashboards."
Data scientist for a midsize company in Heidelberg, Germany
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Technologies for the data scientist
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