Data science employment opportunities of varied complexity and environment are popping up across the globe. Data science offers tons of jobs to prospective employees every day while traditional information science-based roles continue to decrease as budgets get cut across the U.S. Since data is related closely to information historically, this research will explore the education of library and information science professionals and compare it to traditional data science roles being advertised. Through a combination of latent semantic analysis of over 1600 job postings and iSchool LIS course documentation, it is our aim to explore the intersection of library and information science and data science. Hopefully these research findings will guide future directions for library and information science professionals into data science driven roles, while also examining and highlighting the data science techniques currently driven by the education of LIS professionals. . The results of this examination will potentially guide future directions of LIS students and professionals towards more cooperative data science roles and guide future research into the intersection between LIS and data science and possibilities for partnership.
Angel Durr– Marketing Data Scientist and Information Science PhD, Hoya Optical and University of North Texas