InfoSci Curriculum at Shady Grove
The Bachelor of Science in Information Science (BSIS) degree program, known as InfoSci, is offered at Shady Grove to transfer students who have completed a two-year Associate of Arts or Associate of Science degree in information science, information systems, computer science or related field, or have completed 60 college credits, including required benchmarks or their equivalents.
Qualified transfer students are admitted to the InfoSci at Shady Grove program as a cohort group. Students complete their degree over four consecutive semesters as full-time students, taking five 3-credit courses per semester, and graduate with a Bachelor of Science in Information Science degree. The InfoSci program at Shady Grove is a cohort program with a pre-set class schedule to ensure admitted students are able to complete their degree in four consecutive semesters.
InfoSci Benchmark Courses
All InfoSci @ Shady Grove students need to have succesfully completed (with a C- or better) all benchmark courses or their equivalents.
- MATH 115 (or higher) - Precalculus (3 credits)
- PSYC 100 - Introduction to Psychology (3 credits)
- STAT 100 - Elementary Statistics and Probability (3 credits)
- INST 126, CMSC106, CMSC122, etc. - Programming for non-CS majors (3 credits; CMSC 106 carries 4 credits)
Refer to the Course Transfer Database to acess the complete list of evaluated transfer courses from specific academic insitutions.
InfoSci @ Shady Grove Program Courses
This program requires the completion of twenty 3-credit courses. Students are expected to follow all course prerequisites, course sequences, and major requirements.
Ten Information Science (INST) Core Courses - 30 credits
In this course, you will examine the effects of new information technologies on how we conduct business, interact with friends, and go through our daily lives.
You will study methods and strategies for developing systems for storage, organization, and retrieval of information in a variety of organizational and institutional settings.
Students will select and evaluate various types of data to use in decision-making, and use statistical analyses to reach defensible data-driven-conclusions and decisions.
This course will encompass various aspects of object-oriented programming, including program design, testing, and implementation, as well as computational thinking approaches such as abstraction, decomposition, algorithmic design, and generalization.
Students will learn principles of relational databases, and how to design and administer them in languages such as SQL.
Team development and leadership principles and methods are covered with an emphasis on goal setting, motivation, problem solving, and conflict resolution.
Students will examine concepts of computer networking, including network topologies, architectures, and protocols as well as information architecture, security, and authentication.
Students will learn principles of information use, information behavior, and mental models of information retrieval as well as methods for determining information behavior and user needs.
In this course, you will learn how human-computer interaction (HCI) connects psychology, information systems, computer science, and human factors. You will study and apply major user experience research methods, such as user interviews, surveys, contextual analysis, etc.
This is a project-based course, where you will focus on solving real-world problems by applying skills and perspectives you learned in the InfoSci program.
Nine Information Science (INST) Electives - 27 credits
Students study the use of information in decision-making, including the roles of information professionals and information systems including the study of human decision-making behavior.
This course is an exploration of methods and tools for developing dynamic, database-driven web sites, including acquiring, installing, and running web servers, database servers, and script interpreters.
An exploration of current and effective approaches to getting answers from data; students will study clustering, classification, and regression techniques.
Students will study theoretical and practical aspects of data acquisition, collection, preparation, management, storage, retrieval, and analysis.
Simple tests of statistical hypotheses; applications to before-and-after and matched pair studies. Events, probability, combinations, independence. Binomial probabilities, confidence limits.
Professional Writing - 3 credits
Undergraduate Student Services @ Shady Grove
University of Maryland | College of Information Studies
9630 Gudelsky Drive | Rockville, Maryland 20850
(301) 738-6243 | usgInfoSci@umd.edu