The development of science and technology has led to phenomenal changes in global politics, economy, and society. Moreover, the variety of talents needed for contemporary science and technology has also reflected a state of highly dramatic change. Thus, the “Data Analytics (DA)” track features a rolling design methodology to train talents who can meet the needs of contemporary technological development and social applications, which further promote technological and social innovation.
In recent years, with the rapid accumulation of data, the appropriate analysis of such data has become a crucial driver in promoting technological, corporate, and social innovation. Moreover, data sciences connect the web of knowledge across a substantial number of disciplines that create a real-time collective portrait of the world containing a vast array of diversities and individual characteristics and ideologies. Thus, the track curriculum of DA serves as a gateway to advanced data analysis courses, complementing the foundational knowledge of other tracking modules for application and training. Our primary goal is to cultivate professionals in using Data Analytics & Artificial Intelligence with a focus on social analysis and organizational management for innovative developments in the industry, which also involve critical thinking in data sciences.
2. The Design of Courses
DA combines basic, advanced, interdisciplinary and practical courses. Freshmen and sophomores will be provided with basic data science programming courses such as Introduction for AI and Data Science. These courses are project-oriented designs. Juniors and seniors will be offered advanced courses which include the following: International Innovation Management; Machine Learning & Deep Learning; Business Data Analytics; Sustainable Development and Data Analytics; AI and Ethics; AI and Governance; Innovative Information and Data Project Design; and Database Design and Management.DA also provides internship opportunities and a capstone course to train students to further develop their collaboration and professional skills.
|Year 1-1||(CC) Economics I
(CC) Statistics I: R
|Year 1-2||(CC) Computational Programming I: Python
(CC) Statistics II: R
(CC) Economics II
|Year 2-1||(RE) Introduction to AI|
|Year 2-2||(RE) Data Science: R and Python
(IO) Data Visualization: Power BI and R
|Year 3||(IO) International Innovation Management
(RE) Machine Learning and AI: Python
(IO) Innovative Information and Data Project Design
(IO) AI and Governance
(IO) Database Design and Management: MySQL
|Year 4||(IO) Business Data Analytics: R and Python
(IO) AI and Ethic
(IO) Sustainable Development and Data Analytics: R
(IO) Deep Learning
Core Curriculum (CC), Required Electives (RE), and Issue Oriented (IO)