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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:

• INFO 5501 or DTSC 5501 - Fundamentals of Data Analytics. 3 credit hours.
  (This course requires knowledge of college-level linear algebra, calculus II, probability, and statistics)
  This course provides an introduction to key concepts of data science, data analysis, data acquisition and management, statistical analysis software and programming, communicating and operationalizing analysis results, and data ethics. It covers the data lifecycle process and basic concepts required for data science and analytical tasks, including smart processing and technologies such as computational methods, data visualization, and other techniques to facilitate data analysis. Particular emphasis is on data management issues during the data lifecycle, from the observation of natural phenomena to the capture of raw data points to cleaning, organization and further treatments to make data useful for analysis.
• INFO 5502 or DTSC 5502 - Principles and Techniques for Data Science. 3 credit hours. 
  (This course requires knowledge of college level linear algebra, calculus II, probability and statistics)
  This course 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 5505 Applied Machine Learning for Data Science. 3 credit hours. 
  (This course requires knowledge of college level linear algebra, calculus II, probability and statistics)
  This course serves as an 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.

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 will be admitted to the Big Data and Intelligent Systems GAC beginning fall 2023. 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. 

•   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.

If you are a current UNT student and you are applying for a GAC, please complete the Application for Concurrent Graduate Academic Certificate Programs (EUID and UNT password login required) so that your academic certificate program will show up on your transcript. If you do not complete the form 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.

Academic Certificate Completion Form and Request to Receive Your Certificate

Once you complete your course work, please submit the Request for Graduate Academic Certificate of Completion form to receive your certificate.
 

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