This is a time of change in information and library science and new careers are emerging. In response to recent developments, the Department of Information and Library Science, in partnership with other School of Informatics and Computing departments and the Department of Statistics, now offers two Specializations in Data Science. One is offered with the Master of Information Science and one with the Master of Library Science. Candidates in either specialization will receive both a master’s degree and the specialization (which will be noted on their transcripts.)
The formal proposals describe the specializations, which were approved by the IU Campus Curriculum Committee and the IU Trustees in fall 2013.
Rationale:
MIS Data Science
We are proposing this specialization because it is clear that the information professions have changed dramatically over the last decade and there is now a lucrative career path for people who can work with and manage big data projects in private and public sector organizations. These information professionals are expected to have high levels of digital literacy and the knowledge, skills and experiences that will allow them to manage big data projects of all sorts in all sorts of organizations. This specialization will prepare graduates for work on data science projects.
MLS and Data Science Specialization
We are proposing this specialization because it is clear that the information professions have changed dramatically over the last decade and there is now a niche in librarianship for people who can work with and manage big data projects, mostly in academic libraries. These librarians are expected to have high levels of digital literacy and the knowledge, skills and experiences that will allow them to manage big data projects of all sorts in their libraries. This specialization will prepare graduates to support the work of academic data scientists.
The world is awash in digital data from the web, a wide range of sensor technologies, a wider range of information and communication technologies including email and social media and an even wider range of public and private sector organizations. This is the message of the influential Microsoft book on the Fourth paradigm (Hey et al. 2010). Manyika et al. (2011; 4) note “big data has now reached every sector of the global economy. Like other essential factors of production … much of modern economic activity simply couldn’t take place without it.” An emerging grand challenge involves gathering, organizing, curating, managing, analyzing, visualizing and disseminating these heterogeneous data over the lifecycle of the data for such purposes such as scientific discovery, medical advances, entrepreneurial activity and public policy formulation. People in the public and private sectors are taking note of this development as are academics, who are exploring ways of dealing with big data, defined by the National Science Foundation (2012) as:
… large, diverse, complex, longitudinal, and/or distributed data sets generated from instruments, sensors, Internet transactions, email, video, click streams, and/or all other digital sources available today and in the future.
There is a growing demand for trained specialists who can work with big data and manage big data projects, which are becoming pervasive throughout the academy and the economy. The Specialization in Data Science is being proposed to meet this demand. SLIS is well positioned to provide graduate training in the knowledge and skills necessary for our students to become well-trained data scientists who will be able to work with big data in academic and public and private sector settings.
According to Groenfeldt (2012), the increasing pervasiveness of big data “is creating a demand for university graduates who can make it work for companies, and universities are gearing up with full-time programs and executive education.” Manyika et.al (2011; 103) argue, “a shortage of people with the skills necessary to take advantage of the insights that large datasets generate is one of the most important constraints on an organization’s ability to capture the potential from big data.” They (2011:104) describe three types of skilled big data employees who will be in demand in the next five years,