Data Science Graduate Academic Certificate

The Data Science Graduate Academic Certificate (GAC) prepares individuals to leverage their knowledge to manipulate, organize and analyze data to help drive decision making within organizations. The courses provide a theoretical foundation and exposure to data analytics tools and technologies through structured learning experiences. 

Courses for the Data Science 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 three required courses and one elective for a total of four courses (12 credit hours).

The Data Science 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 5501 or INFO 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.
• DTSC 5502 or INFO 5502 Analytic Tools, Techniques and Methods. 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
• DTSC 5505 or 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.

ELECTIVE COURSES - Students will take one (1) course from the following list:

• INFO 5307 Knowledge Management Tools and Techniques. 3 credit hours. 
  Introduction to knowledge management technologies; Internet and web technologies; knowledge management processes and corresponding technologies; collaboration tools and technologies; meta data and ontologies; information and knowledge portals; KM readiness and IT infrastructure; evaluation and selection criteria for knowledge management tools.
• INFO 5504 Advanced Principles and Techniques of Data Science. 3 credit hours. 
  (This course requires knowledge of college level linear algebra, calculus II, probability and statistics) 
  Introduces fundamental principles of data science, advanced techniques and enterprise tools for building effective data science solutions in information science. Addresses various facets of data science practice, including data collection and fusion, exploratory data analysis, predictive modeling, descriptive modeling, basic data analytics, data visualization, and evaluation. Emphasis on integration and synthesis of principles and techniques to solving real-world information science problems using effective data science tools. 
• INFO 5709 Data Visualization and Communication. 3 credit hours. 
  Introduces principles and techniques for data visualization for creating meaningful displays of quantitative and qualitative data to facilitate decision-making. Emphasis is placed on the identification of patterns, trends and differences among data sets.
• 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.

Ready to Apply?

Students will be admitted to the Data Science GAC beginning Fall 2022. 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 for the Data Science GAC 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.

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.

 

Contact Information: