Master of Science in Data Science

The Master of Science in Data Science program equips students with the interdisciplinary skills required for modern, data-driven problem solving. It provides a strong foundation in statistical reasoning, computational techniques, and the integration of knowledge across diverse domains.

Degree Requirements 

To earn the Master of Science (M.S.) degree in Data Science, students must complete a minimum of 24 credits, including at least 18 credits from the department. Within this framework, coursework must cover the following areas:

• Statistics Field (3 credits): Builds a strong foundation in statistical theory and methodology, enabling students to apply rigorous analytical reasoning to data.
• Information Field (9 credits): Emphasizes programming, data management, and computational methods essential for working with large-scale and complex datasets.
• Interdisciplinary Field (12 credits): Bridges statistics, computer science, and domain-specific applications, preparing students to apply data science methods to real-world challenges across disciplines.

Graduate students are required to take at least three courses per semester (up to a maximum of 15 credit hours). In addition, they must present the findings of a written report in an oral examination and submit a thesis to the Department.

By combining technical expertise with applied problem-solving, the program prepares graduates for careers in data science, analytics, and technology, as well as for advanced academic and research opportunities.

Course Information

Required Courses

E0644 Database (3): The database is an organized collection of data stored and accessed electronically from a computer system. This course provides an overview of the current database management systems(DBMS) and SQL. The goals are 1) to get students familiar with how to use a relational database system to solve real problems; 2) how to use noSQL databases.

M0800 Business Ethics (1): What other issues dose a business should attend to in addition to its “bottom line”? When a business operates globally, should it modify its ethical standard based on local laws and regulations? This course covers such questions and hopes to provide students with some generally accepted guidelines. Students will not only read about relevant theories in business ethics but also discuss various business ethics issues.

T0095 Seminar I (1/1): The aim of this course is to help graduate students to understand the recent developments and results of statistical research in different areas. This course provides opportunities for students to practice the skills of oral presentation. A few invited talks are also given by some scholars in this semester. With the process of reporting and questioning, it is possible for the students to improve their skills in briefing. The invited talks can also increase the statistical knowledge of students. Furthermore, it intends to improve the research ability and quality of students.

T0096 Seminar II (1/1):  The aim of this course is to help graduate students to understand the recent developments and results of statistical and data science research in different areas. This course provides opportunities for students to practice the skills of oral presentation. A few invited talks are also given by some scholars in this semester. With the process of reporting and questioning, it is possible for the students to improve their skills in briefing. The invited talks can also increase the statistical knowledge of students. Furthermore, it intends to improve the research ability and quality of students.

Elective Courses 

• Statistics Field : Statistical Analysis Methods (3), Applied Multivariate Analysis (3), Time Series Analysis (3), Data Modeling and Applications (3).
• Information Field: Parallel Computing (3), Data Structures (3), R Programming (3), Python Programming (3), Java Programming (3), Cloud Computing (3), Data Mining (3).
• Interdisciplinary Field: Text Mining and Social Media Analysis (3), High-dimensional Graphical Techniques (3), Data Visualization (3), Machine Learning (3), Special Topics on Big Data Analytics from Social Media (3), Deep Learning (3), Recommender Systems (3), Special Topics in Data Science Applications (3), Quantitative Finance (3), Machine Learning and Biostatistics Applica in Health Data Science (3), Marketing Data Science (3), E-Commerce and Internet Marketing Practice (3), Data Analysis and Predictive Model (3), Financial Software Applications (3), Artificial Intelligence Business Applications (3), Sustainable Finance (3), Machine Learning Applications and Practice (3), Industry Database Applications and Practices (3), AI and Quantitative Trading Simulation (3), Impact Investing (3).