The Harvard Business Review labeled Data Science “The sexiest job of the 21st century,” catching the interest of everyone from high school students to technology veterans. But not everyone is suited to the field of data science, which requires a combination of talents that include mathematics, communication, creativity, and a thirst for unraveling complicated puzzles.
In his work as Vice President of Pricing Science and Research at PROS, Inc., veteran Data Scientist Neil Biehn, Ph.D., has the privilege of working with some of the best Data Analysts in the industry. For the past twelve years, Biehn has provided pricing, revenue, and profit research for clients in manufacturing, transportation, and services industries, among others. His work has been published in a variety of journals, including WIRED Innovation Insights and Analytics Magazine. He was also a contributing author to the book, Innovations for Determining Willingness-to-Pay for B2B.
Biehn recently offered his insight on the various tracks an aspiring Data Scientist can take in order to enter the field. As Big Data continues to emerge as a permanent fixture in every facet of modern life, Biehn is finding both professionals and students have questions about how they can enter this exciting field.
Data Science requires a set of both hard and soft skills to be successful in the field. Biehn believes that a desire to learn is the most important personality trait of anyone entering the field. A passion for statistics and mathematics is essential, as well as a craving to learn things.
“The technology is constantly changing, and there’s always a new problem to solve,” Biehn says. “The ability to change and second-guess yourself is essential. People who are stuck on one method who don’t like to change and think about new things will constantly struggle.”
Communication is an essential soft skill, according to Biehn. While introversion is a great personality trait for a Data Analyst, he has found that those who are most successful in the field are those who are extroverts, with a talent for interacting with others.
Biehn finds people are surprised that creativity is also an important personality trait in a Data Analyst. As he points out, there’s no set formula for Data Science. Professionals can’t simply open a how-to book and read the information they need to know to solve a particular problem.
“It takes an investigative, creative mind to find out what the data is really telling you,” Biehn says. “When you’re doing something that no one’s ever done before, it takes real creativity.”
However, Biehn acknowledges that of the skills required to excel in Data Science, most people will be strong in some areas and weak in others. An executive charged with putting together a Data Science team should stock that team with people whose skills complement one another.
While Data Science is a relatively new field, statisticians have been around for centuries. At the 2013 Joint Statistical Meeting, Nate Silver set off industry-wide discussion with one single quote: “Data Scientist is just a sexed-up term for statistician.” He then added, “Call yourself what you want. Just do good work.”
Whether experts agree or not, this illustrates the importance of statistics in a Data Scientist’s education. “Learning statistics is paramount,” Biehn says. “You have to know these algorithms, that data, as well as how to model a problem from the start. Statistics is definitely a must.”
While there are a few universities offering Big Data degrees, Biehn believes finding a school that specializes in statistics is more important. By combining this with a minor in computer science and taking classes that emphasize machine learning, database algorithms, and data design, students will position themselves to enter the field after graduation.
As important as statistics is, Biehn also cautions against an education that only instills theory in its students. Students should have a combination of theory, writing algorithms, specialized software, and machine learning to be most successful. Data Science has a very intense computer science component, Biehn has found.
In addition to these fields, Biehn recommends courses in operations research or decision sciences. Through these courses, students can learn to go beyond mining the data to learning to use the information they extract. Courses in linear algebra and chemistry are essential, as well. Biehn adds that non-technical courses such as writing can also benefit a future Data Scientist, not only because writing skills are a large part of any profession, but also because creative courses can help a budding Data Scientist learn to creatively identify, tackle, and solve problems.
Biehn speaks highly of the technology program at Stanford, which gives students a well-rounded education that prepares them well for the field of Data Science. While Stanford has a Data Mining and Applications graduate certificate, its computer science department also includes data classes. The Stanford Center for Professional Development also maintains partnerships with large corporations, increasing students’ chances of becoming part of the team at one of these organizations.
As part of the executive team that oversees interns at PROS, Inc., Biehn has personally witnessed the benefits of an internship at the beginning of a Data Scientist’s career. He has found that an internship is far more than a résumé-builder. It’s a valuable experience.
“PROS sponsors paid internships across multiple disciplines,” Biehn says. “If you have an internship, you can see the problems in the field, as well as learning how to work as part of a team.”
Biehn has also found that an internship is very beneficial in developing those soft skills necessary to a Data Analyst. Working as a team, learning to communicate, and practicing problem-solving are all part of the internship experience.
Bringing Existing Experience
The corporate world is already teeming with skilled, talented professionals whose abilities and knowledge would translate well to the field of Data Science. On a regular basis, Biehn and his colleagues encounter questions from these professionals, who are unsure of the best course of action when it comes to pursuing a career change. Should they return to school, complete an internship, or begin work supporting a Data Science team while they learn the ropes?
In response to the increased demand for data professionals, Biehn and others in the industry have recognized the benefits of creating two-year training programs for qualified professionals in order to provide the transition. While additional degrees can also provide a boost to an aspiring Data Scientist’s career, these training programs can also help them gain the skills necessary to begin a long, fruitful career in the industry.
Biehn believes the ideal candidate for a Data Science career is the person who loves to solve problems, especially if that person is the one everyone turns to when a problem needs to be solved. Preferably, that candidate would have an understanding of statistics, although a technology background can be a big help, as well.
“I think it’s a great career move, especially for someone who likes to solve problems,” Biehn says.
As the industry continues to aggressively seek talented, educated professionals to fill the many Data Scientist positions that have become available in recent years, students and professionals are interested in learning how they can help meet this demand. Biehn believes through offering training programs and internships, corporations can accomplish their goals. An entire generation of technology professionals and statisticians are defining this new role, starting a tradition that will be carried on for many decades.