Tawfiq Ammari
Assistant Professor of Library and Information Science
Coordinator of the Data Science Concentration
Data Science Concentration
With this concentration, you will learn about identifying data-driven problems and solving them using analytics approaches that include data/information retrieval, storage, analysis and visualization.
Goals and Objectives
There is a growing demand for addressing data-driven problems; this concentration will equip you with the tools, techniques and analytic thinking skills needed to meet this demand. Specifically, this concentration will prepare you to be a leader who will be able to
- Analyze value-driven data at scale
- Understand the socio-economic context
- Derive valuable insights for the betterment of individuals and the society
- Have a strong grounding on privacy and the ethical aspects surrounding such data
You can apply these skills to assume a wide range of professional responsibilities and opportunities available in areas that increasingly rely on the availability and use of data, including
- E-commerce
- Finance
- Healthcare
- Research and analytics
Requirements for Data Science
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36 credits, plus 3 zero-credit courses
Zero-credit Courses (required for all students)
- 17:610:501 Introduction to Library and Information Professions
- 17:610:502 Colloquium in Library and Information Studies
- 17:610:503 ePortfolio Capstone
Minimum Technology Requirement (3 credits)
Note: This course is required for Library & Information Science students. For the other concentrations, our program assumes that students have attained the competencies and objectives listed for 550. Please look at the course to determine if you meet the standards. If you do not, you must also take this course. If you have any questions, contact Associate Teaching Professor of Library and Information Science Anselm Spoerri.
Foundational Requirements (Select 2 course / 6 credits)
Note: Refer to the specific course study plans to see if a particular foundation course is required for your concentration.
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Concentration Requirements (Select 4 courses / 12 credits):
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Electives for Data Science* (Select 3 courses / 9 credits):
- 17:610:511 Research Methods
- 17:610:524 Metadata for Information Professional
- 17:610:535 Competitive Intelligence
- 17:610:554 Information Visualization and Presentation
- 17:610:559 Web Programming
- 17:610:564 Machine Learning for Data Science
- 17:610:566 Digital Curation
- 17:610:567 Information Security Management
- 17:610:577 Information and Communication Project Management Theories & Best Practices
- 17:610:587 Understanding, Designing, and Building Social Media
- 17:610:581 Social Informatics
- 17:610:582 Information Policy
- Other courses by permission of concentration coordinator
*You can also consider taking an Independent Study with a faculty member or finding another course that meets the approval of the Concentration Coordinator.
General Electives (Select 2-3 courses / 6-9 credits)
Choose any course offered in the MI program.
Building the Next Generation of Products at ADP
“[The MI Program] has a variety of technology as well as non-technology courses for me to choose from. This gave me flexibility in selecting the course load for each semester as well as the areas I would like to concentrate on. The MI program also has excellent faculty who have been recognized for their research by organizations like Amazon. This serves as a great motivator and encourages students to envision great things.”
-Anusha Muralidharan, MI'19
Have Questions?
We're here to help.
Lilia Pavlovsky
Teaching Professor of Library and Information Science
Director of the Master of Information Program
lilia.pavlovsky@rutgers.edu