Applied Data Science
Specialization in Applied Data Science (ADS) emphasizes users, tools, and applications in the Data Science Lifecycles. The ADS pathway prepares students for a career in Data Science with practical skills to solve real-world data problems at application levels, rather than computational level or system development level. It offers training of information science and knowledge organization principles and hands-on skills to solve data problems in application domains and use related tools and products effectively.
In addition to the M.L.I.S. core requirements, students in this pathway should add courses such as the following foundation courses to their elective requirements.
LIS 60510 Digital Technologies I: Data Fundamentals (1 credit)
LIS 60511 Digital Technologies II: Internet Fundamentals (1 credit)
LIS 60512 Digital Technologies III: Systems Fundamentals (1 credit)
LIS 50645 Database Fundamentals for Information Professionals (3 credits)
DSCI 64210 Data Science (3 credits)
DSCI 64010 Data Architecture (3 credits)
LIS 60636 Knowledge Organization Structures, Systems and Services (3 credits)
KM 60370 Semantic Analysis Methods (3 credits)
Download the full pathway document to see additional recommended courses, related competencies, sample job titles, professional associations, and journals.
ADS-Related Professional Associations
- American Society of Information Science & Technology (ASIST)
- Association of Computing Machinery (ACM):
- Special Interest Group (SIG): Knowledge Discovery and Data Mining (KDD);
- Special Interest Group (SIG): Artificial Intelligence (AI)
- Research Data Access & Preservation (RDAP)
- Research Data Alliance (RDA)
Sample Job Titles
Data Analyst ● Data and Information Visualization Librarian ● Data Curation Librarian ● Data Librarian ● Data Management Specialist ● Data Science Librarian ● Data Scientist ● Data Services Librarian ● Data Services Specialist ● Data Strategist ● Data Visualization/Data Analyst ● Digital Scholarship Librarian ● Digital Solutions Data Scientist ● Manager, Data Science and Analytics ● Research Data Librarian ● Research Data Manager ● The Digital Testing, Analytics & Optimization Manager