Medical Library Education: Data Science: Degree or No Degree?

Universities are quickly working to capitalize on the need for data scientists by either improving their existing programs or creating new programs. Data scientists can be defined as the individuals who “have the: [s]kills to collect, process and extract value from giant and diverse data sets; [i]magination to understand, visualize and communicate their findings to non-data scientists; and [a]bility to create data-driven solutions that boost profits, reduce costs and even help save the world” [1]. The University of North Texas (UNT) Board of Regents recently approved three programs related to data: digital communication analytics, data science, and advanced data analytics. The data science program will be housed in the UNT Department of Information Science.

At MLA ’17, Lisa M. Federer, AHIP, NIH Library, National Institutes of Health, Bethesda, Maryland, presented a paper, “‘Databrarians’ and Beyond: Competencies, Education, and Requirements for an Emerging Role.” Federer used a mixed methods approach of examining job advertisements and surveying self-identified data librarians to identify the competencies and education required of data specialists. Interestingly, the top competencies that she identified from both the job advertisements and the survey related to instruction, communication, teamwork, and interpersonal skills. The development of data services and support for data management were two areas that were specific to data science. Federer’s findings regarding teamwork and interpersonal skills were echoed by Daniel Levine, who stated that “degree or no degree, don’t forget about the soft skills” [2].

Conducting a search on data science programs leads to online articles about reasons not to pursue a degree in this area. The authors of these articles discuss that because data science is a new field, you can mash-up your own combination of needed training and education to get the job that you want. In addition, they mention that by the time you would complete a formal degree program, you may be facing a more competitive job market [2–4].

If you are interested in pursuing a career in data science, be sure to examine your existing competencies and see how they compare with the career that you would like to have in this area. This will help you to determine whether a formal degree program is right for you or if you need continuing education or training in focused areas that would help you be successful in data science. Therefore, the question remains: “data science: degree or no degree?”

References

1. Masters in Data Science. The definition of a data scientist [Internet]. MastersinDataScience.org [cited 23 Jun 2017]. <http://www.mastersindatascience.org>.
2. Levine D. 5 things you should know before getting a degree in data science. RJMetrics [Internet]. 21 Sep 2015 [cited 23 Jun 2017]. <https://blog.stitchdata.com/5-things-you-should-know-before-getting-a-degree-in-data-science-40cddf44aac3>.
3. Brown MS. 4 reasons not to get that masters in data science. Forbes [Internet]. 29 Jul 2016 [cited 23 Jun 2017]. <https://www.forbes.com/sites/metabrown/2016/07/29/4-reasons-not-to-get-that-masters-in-data-science/#827be4a40c0b>.
4. Pensig C. Why I left my data science master’s program. LinkedIn [Internet]. 19 May 2015 [cited 23 Jun 2017]. <https://www.linkedin.com/pulse/why-i-left-my-masters-program-charles-pensig-1>.