Program Overview

Data Science Degree

Overview

The Bachelor of Science in Data Science at the University of North Texas is designed to meet the rising demand for professionals in data management, big data, and data analytics fields. The program will prepare students for careers in data science with a broad knowledge of the tools, techniques, and methods needed to analyze and work with data and information to help drive effective decisions and strategy in organizations.

Program Details

• Program Type: Bachelor of Science (B.S.)
• Format: Hybrid
• Estimated Time to Complete: 4 years
• Credit Hours: 120

Marketable Skills

• Collect, clean, process, and analyze data
• Data Science tools and techniques proficiency
• Critical thinking skills
• Interpret data for decision making
• Visualize data and communicate results

Data Science Degree Highlights

• Special lectures hosted by the College of Information and the department feature renowned scholars who provide different perspectives and insights into the data science field.
• Our students and faculty are active members of different professional associations and learned societies, such as the iSchools consortium, the American Library Association, the Association for Information Science and Technology, and the Knowledge and Information Professional Association.
• The job outlook looks promising for those in the data science field, with Data Scientist being named the best job in America for three years running, according to Glassdoor, one of the world’s largest job and recruiting sites.
• The Department of Information Science offers financial support and scholarships to its students to recognize exceptional academic and creative accomplishments.
• The Career Center is one of the many valuable resources available to you at UNT. The Career Center can provide advice about internships, future employment opportunities, and getting hands-on experience in your major.
• UNT’s Data Science program offers the convenience and flexibility of online, blended, and face-to-face classes, taught by experienced and knowledgeable professors in the field.

Career Opportunities

• Information analyst
• Data mining specialist
• Data architect
• Business intelligence developer
• Applications architect
• Infrastructure architect
• Enterprise architect
• Data scientist
• Data analyst
• Machine learning specialist
• Business analyst
• Data and analytics manager

Course Highlights

Principles of Data Science and Analytics (3 hrs)

Introduction to the fundamentals of data science and data analytics. Provides the required foundational knowledge and practice to students to successfully integrate automatic methods and tools for qualitative and quantitative analysis.

Data Visualization (3 hrs)

Enables students to combine statistical methods and graphic-centered computer-based treatment of structured and unstructured data. Includes theoretical considerations to visual design as well as practical computer scripting that will enable students to use visualization techniques and the necessary tools to visualize large sets of data and facilitate visual analysis.

Data Modeling and Data Warehousing (3 hrs)

Introduction to traditional linear and relational database theory and practice. Main focus is on modern approaches that include SQL and NoSQL, graph-based databases for structured and unstructured datasets, and standards for data representation and exchange (RDA, XML, JSON, etc.).

Statistical Methods for Data Science and Analysis (3 hrs)

Introduces students to both theories and applications of statistical methods. Students learn the core concepts of statistical computing and advanced techniques for data analysis, while working hands-on with real data using statistical tools.

Data Analysis and Knowledge Discovery (3 hrs)

Introduces the student to data analysis, data mining, text mining, and knowledge discovery principles, concepts, and practices to approach data and data mining tasks and techniques using suitable software and other data analysis tools.

Fundamentals of Information Technology Security (3 hrs)

Introduction to the security systems development life cycle and its effects on application development, software engineering, traditional systems analysis, and networking. Examines the various components of information privacy and security.