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KM education: Data science takes the lead

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The term “data scientist” has been around for a decade, and the job function has existed even longer, but only recently has awareness really hit the mainstream. The primary reason for its growing relevance is the need to analyze large amounts of data. With a combination of heavy-duty technical skills, proficiency in analyzing big data and an orientation toward extracting value from complex data environments, data scientists are in a good position to pick and choose from a large number of job opportunities.

Demand for data scientists stems from a large range of complex business needs, greater volumes of data (especially customer-related data) and organizations’ belief that they should be getting more value from what is stored in their repositories. People who can connect the dots, grasping both the business issues and advanced analytical techniques, are in short supply. With job postings increasing up to 60 percent per year, the supply will lag demand for some time.

While relatively rare five years ago, graduate programs in data science have been springing up at a steady clip, and individuals who are seeking an academic background in the field have a growing range of options. Students often enter programs mid-career, to build on existing experience and provide new opportunities. Because they are often established in a geographical region and hold a job already, many students opt for a part-time, online program. The career paths of two such students at the data science program at Indiana University (indiana.edu) are described here.

Technical and practical

After majoring in biology and chemistry, Sara Marie Bigelow landed a job in a lab analyzing samples for an oil company. Wanting to advance her career, she began looking into certificate programs in data science. “I have always been interested in the results side of science and how to interpret data,” says Bigelow. “Data science seemed like a good avenue to pursue.” She found a program at the School of Informatics & Computing at Indiana University in Bloomington. While completing the certificate program, she was invited to apply for the newly developed master’s program in data science, which was about to be launched.

The master’s program was offered either on campus or online, and Bigelow’s situation was more compatible with the latter. Working full time and studying part time was demanding but manageable. Her courses included big data in drug discovery, visualization, scientific and clinical database management, data semantics related to the Internet of Things (IoT) and cloud computing. “I took a lot of technical courses but also wanted the practical side, so that I could make a business impact,” Bigelow says. She finished the 30-credit program in two years.

Healthcare seemed particularly appealing, and after completing an internship at Eli Lilly and Company, she was offered a job. Her work as an intern centered around process improvement and text analytics, directed toward action items to improve clinical trial processes. In her full-time position, she is a clinical data associate for biomedicines, data sciences and solutions at Lilly and provides leadership for data management activities in clinical trials.

She also continues to maintain a relationship with her department at Indiana University, where she has been a guest lecturer. “The program is fantastic, and I wanted to give back,” she explains. In 2016, she won an Innovator Award from Lilly for facilitating collaboration between Lilly’s data sciences department and Indiana University’s data sciences program.

Expanding existing skills

Laura Kahn is a current student in Indiana’s online data science master’s program and works full time in at the U.S. Patent and Trademark Office in Virginia. With a background in engineering and over a decade of work experience, she wanted to find out where a match might be between her interests and skills on the one hand and the needs in the employment market. Before choosing data science, she consulted with a wide variety of people in both her personal and professional life. “My hypothesis was that I would be a good data scientist because I have strong analytics skills, and I like to create order out of chaos,” Kahn explains.

After considering a number of online programs, Kahn chose Indiana based on its reputation and quality of faculty. She began the program in fall 2016 and is on track to graduate in spring 2018. The online experience includes watching material on demand, virtual meetings with the professor and group meetings with other students. “The group work is neat because it’s like the real world, meeting virtually with people all over the country,” Kahn says. “The program has also been a great experience in validating the type of work that I want to do, which involves both technical capabilities and domain expertise.”

The future holds a great deal of promise, from Kahn’s perspective. “Identifying business problems, figuring out what kind of data will solve them, obtaining the right data and analyzing it to attempt to predict the future is what data science is all about,” she says. “I’m very happy with the practical application side that Indiana emphasizes. It will allow me to advance in terms of being involved with a wide range of projects that require data-driven decisions.”

Program vision

The program had a big vision from the start, according to David Wild, director of the Data Sciences Program. “It was developed with the knowledge that data science is becoming critical to all disciplines and that the best way to serve our students and the industries that need them was to be out in the field. But it also encompasses a range of topics to provide a deep understanding of data science.” For example, in addition to the data mining and machine learning experts that one might expect, the program has anthropologists who look at how data has changed over the years.

Partnerships with other departments enrich the data sciences program. The Kelley School of Business, the School of Public and Environmental Affairs and the Indiana University Medical School are emerging partners in data science applied to the important domains of business, health and public policy and medicine. The schools are partnering in research and offering courses across programs. “It’s amazing what can happen when you get people in these domains together with data scientists and see what kind of problems they can solve,” Wild says.

A strong commitment was also made to making the curriculum relevant to industry. “As well as partnering with Eli Lilly, we have companies in Silicon Valley that work with us,” Wild adds. “We match master’s level students with industry or research projects or with those high-priority projects that never seem to get funded.” The curriculum is designed to be responsive to industry needs. “We ask them what kind of data scientists they want,” Wild says. “Sometimes they know and sometimes they don’t. Our curriculum is adaptive to new and changing needs.”

Wild has high praise for the students who enter the program. “They are some of the best we have ever worked with,” he says. “Their level of domain knowledge and expertise gives them an unusual level of adaptability, so they can fit in quickly with business or government environment.” But resources are also offered to less experienced individuals. “We have short mini-courses that help people get up to speed if they need to fill in some missing background,” Wild explains. “Data science is a field that provides opportunities to people in the early stages of their careers as well as mid-career.”

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