Graduate Certificate in Data Science

The information and data sciences disciplines are expanding rapidly thanks to an ever present demand for new innovations in information retrieval, storage, and analysis tools and techniques. The Graduate Certificate in Data Science will help to fill this demand by giving interested professionals the opportunity to get recognized for some basic training in the data and information science disciplines as well as giving them a stepping stone towards pursuing other graduate degrees such as the MS in Information Science  and our revised PhD in Integrated Computing – Information Science track that our Department is offering.

Program Delivery

The GC in Data Science is a 12 credit hour program and can be completed online. Class lectures are broadcasted live by Internet using Blackboard Collaborate technology. 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 UALR homepage.

Course Requirements

  • 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 (New Course): 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.

Admission Requirements

Anyone with a baccalaureate from an accredited school can apply for admission to the Data Science Certificate 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
  • 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

To Apply

Please visit our UALR Graduate Admissions Portal.

Going on to a Master’s Degree

Here are some options.

 

For More Information

Please contact Dr. Daniel Berleant at jdberleant@ualr.edu.