The information science discipline is expanding rapidly thanks to an ever-present demand for new innovations in information retrieval, storage, processing and analysis tools and techniques. Ranked among the top information science programs by both U.S. News & World Report and Intelligent.com, UA Little Rock’s Master of Science in information science is designed to familiarize individuals with the advanced knowledge, skills, and technologies for working with large amounts of complex data.
The curriculum balances theory and industry best practices using state-of-the-art tools and technology. It is based on the Model Curriculum and Guidelines for Graduate Degree Programs in Information Systems endorsed by the Association for Computing Machinery (ACM) and Association for Information Systems (AIS), with a data science perspective.
All required courses are offered online via live webcast and recorded lectures. Online campus students are not required to attend live webcasts but are welcome to do so when they wish. Courses are taught by highly qualified doctoral-level faculty with extensive experience in the field. Students will complete a 6-credit project (or thesis) which is often related to their workplace environment.
Candidates who have a background in computer programming, database concepts, and applied statistics, or who have professional experience in an information analysis or other relevant computing related role will be the most prepared to enter the program.
Requirements for Regular Admission without Conditions • Baccalaureate degree from an accredited institution • Cumulative grade point average of at least 3.0 on a 4.0 scale • Graduate Record Examination (GRE), Graduate Management Admission Test (GMAT), or any other standardized test scores available • Students may be required to take certain courses instead of elective courses if they have not had the equivalents (with a grade of B or better), or employment experience equivalent, to: IFSC 2300 Object-Oriented Technology IFSC 3320 Database Concepts STAT 2350 Intro to Statistical Methods
For more information, please contact Dr. Daniel Berleant at firstname.lastname@example.org.