The information and data sciences disciplines are expanding rapidly thanks to an ever present demand for new innovations in information retrieval, storage, processing and analysis tools and techniques. The Master of Science in Information Science is a 33 credit hour program which is designed to give individuals the advanced knowledge, skills, and technologies for working with large amounts of complex data. This degree will also serve as a stepping towards pursuing other graduate degrees such as the PhD in Computer and Information Science.
The MS in Information Science can be completed mostly or wholly online. Class lectures are broadcast live in the internet using the Blackboard Collaborate software. Live webcast schedules coincide with the days and times listed for the on-campus courses. Most courses are scheduled one time per week starting at 2300 UTC (6:00 pm US CDT) and lasting for 2 hours and 40 minutes, but some classes may be scheduled at other times. Lectures for each course are recorded and can be viewed by students at a later time. The fall semester runs from about August 21 through December 15 and the spring semester from about January 5 to May 15. The exact dates for each semester vary and can be found on the calendar on the UA Little Rock homepage.
To learn more about Distance Learning, please check out the following link Distance Learning and Proctoring.
- 4 core courses. These also qualify you for the Graduate Certificate in Data Science, so you can get that certificate in addition to your master’s degree.
- IFSC 7320 Database Systems or CPSC 7351 Database Design: Database systems and data modeling, including entity-relationship model, relational data model, normalization, structured query language (SQL), transaction management, object-oriented databases, and basics of physical database design and query evaluation.
- IFSC 7370 Data Science and Technologies: This course provides a survey of the skills and concepts needed for managing, processing, and analyzing massive amounts of data in real time. Topics covered include data sourcing, storing and sharing, integration, and data mining strategies along with hands-on experience working with sample technologies selected from a complex ecosystem of tools and platforms.
- IFSC 5345 Information Visualization: The design and presentation of digital information. Use of graphics, animation, sound, visualization software, and hypermedia in presenting information to the user. Methods of presenting complex information to enhance comprehension and analysis. Incorporation of visualization techniques into human-computer interfaces.
- IFSC 7360 Data Protection and Privacy: Concepts and methods for creating technologies and related policies with provable guarantees of privacy protection while allowing society to collect and share person-specific information for necessary and worthy purposes. Methods include those related to the identifiability of data, record linkage, data profiling, data fusion, data anonymity, de-identification, policy specification and enforcement and privacy-preserving data mining.
- 5 courses of graduate coursework selected from the graduate catalog with particular emphasis placed on courses from Information Science, Technology Innovation, Computer Science, Business Information Systems, Statistics, and Systems Engineering. A limit of two courses may be selected from the Information Quality catalog. Some possible strategies include:
- A focus on data science & analytics
- BINS 5351 Data Analysis and Reporting
- BINS 7304 Business Applications for Decision Making
- BINS 7309 Cloud-Based Business Intelligence
- IFSC 5325 Data Mining
- IFSC 5360 Social Computing
- IFSC 7321 Information Science and Theory
- IFSC 7331 Network Sciences
- STAT 7340 Advanced Statistical Methods I
- STAT 7341 Advanced Statistical Methods II
- A focus on database systems design, development, and management
- CPSC 7352 Advanced Database Issues
- IFSC 5330 Database Security
- IFSC 7310 Information Systems Analysis
- INFQ 7303 Principles of Information Quality
- INFQ 7342 Information Quality Tools & Industry Landscape
- INFQ 7367 Information Quality Policy and Strategy
- A focus on emerging information technologies
- BINS 7352 Emerging Technologies and Strategic Issues
- CPSC 7373 Artificial Intelligence
- IFSC 5399 Special Topics in Augmented and Virtual Reality
- INFQ 7348 Entity Resolution and Information Quality
- TINV 5301 Strategies for Innovation
- TINV 5302/IFSC 5301 Information, Computing & the Future
- A focus on innovation (and a Graduate Certificate in Technology Innovation in addition to your master’s degree)
- TINV 5301 Strategies for Innovation
- TINV 5302 Information, Computing & the Future (or a relevant business course approved by the program coordinator)
- MGMT 5383 Entrepreneurial Perspectives
- TINV 5303 Applied Innovation Project (or IFSC 7386 with an appropriate project; see Project or thesis, next)
- A focus on data science & analytics
- Project or thesis. This must total at least 6 credit hours. The second digit of the course number gives the number of credits. See graduate catalog for official course descriptions. Students must divide the credits over two (or more) semesters. Trying to take all 6 credits in one semester is not permitted except under extenuating circumstances, which don’t happen very often.
- IFSC 7186/7286/7386/7486/7586 Graduate Project
- IFSC 7198/7298/7398/7498/7598 Graduate Thesis
Here are some more details on how to choose between a project and a thesis and what procedure to follow to succeed.
Master’s Project or Master’s Thesis
How should I fulfill the thesis or project requirement?
The first steps are to identify a project or thesis topic and an advisor. You can choose any faculty member you like, or in the case of certain topics you can choose an external community member who is qualified to advise with the approval of the MSIS program coordinator. Then submit this form. For a topic, one option is for the advisor to suggest the topic. In fact, if a faculty member needs research work done in his or her lab, this can be a reason for the faculty member to agree to serve as your advisor. A second option is to define a topic yourself and then find a faculty member or qualified community member who is willing to serve as advisor for that topic. This can be something you simply want to do, or you can find possible topics by checking sites like kaggle.com, innocentive.com, or other sites that provide data challenges or similar ideas. A third option is to ask the program coordinator for a list of suggestions for topics. We have had students who were able to use their MS Project as a bridge to finding employment. Consequently, give some thought as to what you would like to do career wise and select a project that will help you along the way.
The next steps are to decide on some details:
(A) If the topic will lead to some advance in human knowledge (for example, it might lead to a publishable paper), it would be most likely suitable as a thesis, and you would register for thesis hours. If it is a learning experience but not intended to advance the field of information science, it would most likely be suitable for a project, and you would register for project hours. Whether thesis or project, a total of six credit hours of work on it is required. It is recommended in most cases that this be spread out over more than one semester.
(B) Although the advisor is your primary intellectual resource, you also need two others to be on your advisory committee. The advisor can help suggest or recruit these others. Sometimes an advisor or committee member does not have formal permission from the university to serve, in which case they will need to apply for it. The coordinator can help with this.
The remaining steps involve completing the thesis or project:
First, the committee needs to approve the project or thesis. Your advisor may want you to present your proposed work to the committee during a meeting. However committee approval is obtained, the coordinator will need a signed statement (or emails) that they approve your thesis or project proposal.
Second, you will need to do the work you proposed. In the case of a thesis, this will include investigating the background literature, which your advisor will guide you through. Then there is the technical work involved. Finally there is the writeup. A thesis will need to conform in format to graduate school specifications so it can be made available to others. A project will need to conform to various valid guidelines widely available by, for example, a web search engine query like:
how to write a project report
Finally, you will need to gain approval of your committee for the results of your work and the thesis or project report document. Start by sending your thesis or project report to the committee members. At around that time, communicate with them to schedule a meeting in which you present your work to them for their approval. This is often termed the “defense.” It is best to work with your advisor to get that person’s suggestions and help as you prepare your presentation for your defense. After the defense, the committee will often request changes to the writeup and/or additional technical advances before they approve it. Ultimately, the program coordinator will need a signed form or statement, or emails from the committee members, indicating their approval. At that point, you must take the bureaucratic step of applying for graduation, so the university registrar will put you on the graduation list.
- For the Graduate Catalog (strictly speaking, the one corresponding to the year you were admitted), please click here.
- For the Dissertation and Thesis Guidelines Handbook, please click here.
- For the Graduate Student Handbook, please click here.
Anyone with a baccalaureate from an accredited school can apply for admission to the MS in Information Science program. Candidates who have a background in computer programming, database concepts, and applied statistics, or who have professional experience in an information analysis role will be the most prepared to enter and successfully complete the program.
Requirements for Regular Admission
- Baccalaureate degree from an accredited institution
- Cumulative grade point average of at least 3.0 on a 4.0 scale
- Graduate Record Examination general test section (GRE) or Graduate Management Admission Test (GMAT) scores, or waiver form
- Completion of any remedial course work that may be specified by the department. Students seeking regular admission to the program are expected to have completed (with a grade of B or better in each course) course work or have professional experience equivalent to the following UALR courses
- IFSC 2300 Object-Oriented Technology
- IFSC 3320 Database Concepts
- STAT 2350 Intro to Statistical Methods
For more information, please visit the FAQ page, or contact Dr. Daniel Berleant at email@example.com.
To apply, please visit our UALR Graduate Admissions Portal.