Big Data and Intelligent Systems

Course Requirements | Apply Today

The Graduate Academic Certificate (GAC) in Big Data and Intelligent Systems prepares students to build data-driven intelligent systems. Students will take courses with an emphasis on machine learning and knowledge discovery.

Courses for the GAC will be offered primarily online, with some courses offered in a hybrid format with face-to-face meetings at the UNT Frisco campus. Students will take four required courses (12 credit hours).

The Big Data and Intelligent Systems GAC is intended for Computing or Information Technology Professionals, and others in related fields who already have a bachelor's or master's degree, and want to develop or enhance their knowledge and skills in data science by taking graduate-level courses to receive an academic credential. 

All 12 hours of coursework taken for this Academic Certificate can be applied toward the Master of Science in Data Science Program in the Department of Information Science. 

Course Requirements
 

REQUIRED COURSES - Students will complete the following courses:

DTSC 5505 Applied Machine Learning for Data Scientists, 3 credit hours.
Introduction to concepts of machine learning and widely adopted machine learning algorithms including regression, clustering, support vector machine, and neural network. Defines complex modern machine learning architectures in Google TensorFlow and Keras frameworks using Python programming language. Introduces the applications of machine learning to computer vision with Convolution Neural Networks (CNN), natural language processing with Recurrent Neural Network (RNN), and information retrieval with RNN and CNN.

INFO 5810 Data Analysis and Knowledge Discovery, 3 credit hours.
Introduces the student to data analysis, data mining, text mining and knowledge discovery principles, concepts, theories and practices. Designed for the aspiring or practicing information professional and covers the basics of working with data from a hands-on and practical perspective. Incorporates lecture, discussion, practice of learned concepts, and readings.

DTSC 5565 Software Engineering for Data Scientists, 3 credit hours.
Introduction to major topics in software engineering for data scientists such as: requirements specification, analysis and design, testing, project management, and implementation. Additional topics such as software life cycle models, the Unified Modeling Language (UML), agile software development techniques, configuration management, change control and version control tools, object oriented design, and project documentation are discussed. Prerequisite(s): DTSC 5501

And one of:

DTSC 5501 Fundamentals of Data Analytics, 3 credit hours.
Teaches mathematical and statistical fundamentals for data science and focuses on the modern computational statistical modeling methods. Covers basic computational statistical inference, the properties and behavior of linear models, conditional probability, Bayesian models, inference by resampling and time series methods.

DTSC 5502 Principles and Techniques for Data Science, 3 credit hours.
Covers comprehensive and practical approaches to research, including specific methods of analysis for students to develop advanced research skills in the general areas of descriptive statistics, exploratory data analysis and confirmatory data analysis. Includes methods to better communicate results of the research. Successful students will have the skills to be useful participants in the data lifecycle from collection to management, analysis and visualization of results.

INFO 5707 Data Modeling for Information Professionals, 3 credit hours.
Designed to meet the needs of the information industry for data modeling and database design for text and multimedia applications. Focus on the application of data modeling technologies to library and information science practice and research. Class projects provide hands-on experience in designing and implementing database systems for information service-oriented organizations such as libraries, museums, publishers and bookstores.

INFO 5777: Virtual Reality and Its Applications, 3 credit hours.
Introduction to Virtual Reality (VR) hardware and software, with an opportunity to apply this knowledge to applications including information science, libraries/digital libraries, education, healthcare, visualization, and games. Applies cutting-edge VR technology currently available in academia and industry. Topics include input devices, output devices, computer graphics principles for VR, geometric modeling principles for VR, human factors in VR, data visualization in VR, and traditional and emerging applications in VR. Learn the value of visualization and how to best leverage visualization methods in VR. Design, model, and program the VR environment by developing a complete VR application as a group project.
Cross- List: DTSC 5777

Prerequisites

Some courses listed may have prerequisites. Students should consult with the program advisors prior to enrolling in the individual courses.

Please note: This Graduate Academic Certificate requires prior knowledge in at least one programming language as well as mathematic competency, specifically in: linear algebra, calculus II, probability & statistics. 

Ready to Apply?

Students are admitted following the holistic review of the application. Successful applicants will have demonstrated competence in mathematics/programming through a combination of prior training, coursework and/or relevant work experience. The steps to apply are below:

1. Apply to the University of North Texas Toulouse Graduate School via ApplyTexas or GradCAS application. 

•   Send transcripts from previous schools attended to the UNT Toulouse Graduate School.

2. Complete the Department of Information Science GAC Application Form and attach a current resume to the application. In your resume, please include information indicating your knowledge of programming languages (at least one language) and of your mathematics competency, specifically: linear algebra, calculus II, probability & statistics.

NOTE: Students who are awarded Academic Certificates and later apply for admission to the M.S. in Data Science program will be required to submit the additional materials needed for admission to the M.S. program.

Graduate Students seeking Concurrent Enrollment

If you are a current UNT student and you are applying for a GAC for Spring 2025, you will submit a new ApplyTexas application. If you are applying for concurrent enrollment in a degree and certificate program for Summer 2025 or later go through the UNT GradCAS system. 

If you do not complete an application before your graduating semester, the Toulouse Graduate School will not accept your request for the certificate.

Once You Are Admitted

Once admitted, you will be assigned an advisor who will assist you in getting enrolled for classes and beginning the Graduate Academic Certificate Program.

 

Contact Information: