University of North Texas 
Data Science Degree
Bachelor of Science (B.S.)
Program type:
Major
Format:
Hybrid
Est. time to complete:
4 years
Credit Hours:
120
Harness the tools that help businesses and institutions do more with data.
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.
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Request More InfoWhy Earn a Data Science Degree?
The field of data science continues to develop into a steady and demanding career path. Through relevant coursework, students will gain knowledge in key areas, including data management, data modeling, big data, data analytics, data and information visualization, information organization, internet applications development, and game design and technology.
The program will educate a new generation of information professionals, particularly those students with an interest and background in science who are intent upon pursuing an analytics-related field. Students will acquire the types of skills, critical thinking, and competencies needed in data science and digital data management. Students can further guide their learning by choosing to focus on the information science or business analytics professional field.
In recent years, data scientists’ roles have increased in importance as businesses realize the importance of collecting and analyzing data to make strategic decisions. Data scientists are curious individuals, with the technical skills to collect and analyze data from various sources, extract and organize the information, and communicate findings to answer questions and help in the decision-making processes.
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.
What Can You Do With a Data Science Degree?
A bachelor's degree in Data Science is the entry point for many data-related careers in the public and private sector, such as:
- 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
Data Science Degree Courses You Could Take
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. Other topics
include CRTSP-DM; SEMMA; data assurance; policy; ethics; privacy and security; and
principles and practice of technical, statistical and human behavior, as well as social
and professional issues related to the handling of data.
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. Covers principles and theories
of data mining and text mining techniques as well as analytical applications of data
mining and knowledge discovery 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.
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