In November 2015, students from Carnegie Mellon University’s Heinz College headed to Washington D.C. for a colloquium with panels on many different issues of public policy. One of the many sessions at the colloquium included a data analytics panel attended by data scientists, data analysts, and an economist. After each professional gave a brief introduction regarding what they do, they opened the floor for questions.
Listening to questions from interested students lent insight into these professions. Attendees asked questions about what skills are necessary for a career in data and what discerns the difference between analysts and scientists.
Many students expressed interest in what the panelists believed to be the best programming languages to learn. The data managers responses were similar across the board, and they shared who used python and who used SQL. However, they stressed their belief that instead of looking for specific technical skills, companies show more interest in the ability to learn new data languages. It is not about which program applicants have mastered, but rather their ability to think with a critical, technical mind.
When a student asked for the panelists to explain the difference in job descriptions between analysts and scientists, murmurs of agreement filled the room. While each professional gave their take on what each profession entails, they never came to a perfect understanding. The fluidity and relative newness of the profession lends itself to creative interpretation.
The most important insight I walked away with was the importance of communication; this was a topic that all panelists agreed upon. Using technical skills to understand data only takes them so far, but it is soft skills that have powered their careers. They emphasized the importance of finding ways to not only present the information in an understandable way, but also in a manner that allows for the achievement of organizational goals. This skill includes the ability to speak two languages: data and human.