Required courses, 9 hours

DTSC 5501 - Fundamentals of Data Analytics
DTSC 5502 - Principles and Techniques for Data Science
DTSC 5505 - Applied Machine Learning for Data Scientists

Guided electives, 15 hours

The guided electives are courses with advanced topics in both data science and data analytics. The student can choose from the following courses which concentrate on specific methodologies and tools in data science and data analytics.

Students must take 15 hours from the following list of courses.

ADTA 5230 - Data Analytics II
CSCE 5213 - Modeling and Simulation
CSCE 5218 - Deep Learning
CSCE 5300 - Introduction to Big Data and Data Science
DSCI 5240 - Data Mining and Machine Learning for Business
or
CSCE 5380 - Data Mining
DSCI 5330 - Business Intelligence Foundations
DSCI 5340 - Predictive Analytics and Business Forecasting
INFO 5040 - Information Behavior
INFO 5206 - Information Retrieval Design
INFO 5307 - Knowledge Management Tools and Technologies
INFO 5503 - Knowledge Management Processes and Practices
INFO 5810 - Data Analysis and Knowledge Discovery
DSCI 5360 - Data Visualization for Analytics
or
INFO 5709 - Data Visualization and Communication
LING 5410 - Foundations of Computational Linguistics
LING 5412 - Advanced Models of Language
LING 5415 - Special Topics in Computational Linguistics

General electives, 9 hours

Students must take 9 hours from the following list of courses. They are allowed to pursue courses from outside this list and in their areas of interest with the approval of the advisor.

BMEN 5210 - Biomedical Engineering Laboratory
BMEN 5315 - Computational Methods in Biomedical Engineering
BMEN 5940 - Biomedical Engineering Seminar
CSCE 5200 - Information Retrieval and Web Search
CSCE 5214 - Software Development for Artificial Intelligence
CSCE 5216 - Pattern Recognition
INFO 5091 - Data Science Internship
INFO 5200 - Information Organization
INFO 5205 - Information Indexing, Abstracting and Retrieval
INFO 5223 - Metadata for Information Organization and Retrieval I
INFO 5224 - Metadata for Information Organization and Retrieval II
INFO 5305 - Systems Analysis and Design
INFO 5365 - Health Librarianship and Informatics
INFO 5637 - Medical Informatics
INFO 5707 - Data Modeling for Information Professionals
INFO 5731 - Computational Methods for Information Systems
INFO 5735 - Usability and User Experience Metrics
INFO 5737 - Information and Cyber-Security
INFO 5745 - Information Architecture
INFO 5770 - Introduction to Health Data Analytics
INFO 6050 - Health Research Methodology
LING 5405 - Programming for Linguistics
LTEC 5300 - Learning and Cognition
LTEC 5320 - Contemporary Issues in Workforce Learning and Performance
LTEC 5702 - Applications of Artificial Intelligence in Learning Analytics
LTEC 5703 - Artificial Intelligence Technologies for Learning and Performance

Practicum/research project/thesis, 3-6 hours

DTSC 5082 - Seminar in Research and Research Methodology (DTSC 5082 Course Request) 
INFO 5090 - Practicum and Internship in the Field Study (DS Practicum Process) - the practicum requirement cannot be waived for Data Science MS students.

In addition to the courses listed above from the UNT Catalog, the following are also approved courses for the M.S. Data Science degree:

• ADTA 5240 - Guided Elective - Cannot also take DSCI 5240 (or CSCE 5380 for students beginning Fall 2022 or later)
• ADTA 5250 - Guided Elective - Cannot also take INFO 5709 or DSCI 5360
• ADTA 5340 - Guided Elective - Cannot also take DSCI 5340
• ADTA 5550 - Guided Elective - Requires prerequisite of either ADTA 5240 or ADTA 5340
• DTSC 5565 - Guided Elective - Requires prerequisite of DTSC 5501

If you have questions about any other courses beyond those listed above and on the UNT Catalog page for your degree, please reach out to the Academic Advising office via ci-advising@unt.edu.