Big Data and Data Science Program

1. Introduction
Big data (Big Data, also known as massive data) refers to larger or more complex data sets that cannot be processed by traditional data processing techniques. At the same time, in the case of the same total amount of data, after merging each small data set (Data set) to analyze a lot of additional information and data correlation, it can be used to analyze business trends, judge the quality of research, prevent the spread of diseases, fight crime or Measurement of real-time traffic conditions, etc., so the application level of big data is extremely wide, and it is also one of the main tools of social science and applied science in the future. In order to pay equal attention to learning and application, so that students can enter the industry immediately after graduation, the big data course is the main course to guide the future industrial development.

2. Setting purpose and goals
The design of this course aims to provide students with a basic understanding of big data, train students to be familiar with the theoretical basis of big data and the application of technology in the industry, so that students can create their own advantages through professional big data-related courses, and respond to global industrial development and The technological environment changes, effectively cultivating outstanding new talents suitable for social industries.

3. Course setting and planning units
This course is a cross-faculty credit course, jointly planned by the School of Management and the Department of Information Management.

4. Participating teaching and research units
Department of Information Management, Department of Business Administration, Department of Finance, Department of Economics, Department of Accounting and Information Technology, Department of Labor Relations, Department of Mathematics, Department of Information Engineering, Department of Communication Engineering and Department of Electrical Engineering.

5. Teachers
Department of Information Management, Department of Business Administration, Department of Finance, Department of Economics, Department of Accounting and Information Technology, Department of Labor Relations, Department of Mathematics, Department of Information Engineering, Department of Communication Engineering and Department of Electrical Engineering High-quality teachers teach.

Teacher	Affiliated Unit	Full-time (Part-time) Job	Highest Education	Specialties
Qingbiao Liu	Department of Finance	Full-time	Ph.D. in Finance, Cleveland State University	Financial Markets, Financial Institutions, Corporate Finance
Ziling Lin	Department of Finance	Full-time	Ph.D. in Finance, National Taiwan University	Financial Risk Management, Actuarial Accounting
Fumin Zeng	Department of Economics	Full-time	PhD in Economics, University of Nottingham, UK	Applied Individual Economics, Applied Econometrics, Health Economics
Anxiang Wang	Department of Labor	Full-time	Doctor of Industrial Engineering, National Tsing Hua University	Human Factors Engineering, Labor Safety, Labor Hygiene
Fan Wu	Department of Information Management	Full-time	Ph.D., Institute of Information Engineering, National Taiwan University	Big data analysis and Internet of Things application, medical information system design, distributed database system, object-oriented analysis
Xuzhe Wu	Department of Accounting and Information Technology	Full-time	Doctor of Business Administration, University of Warwick, UK	Forecasting models, case studies, enterprise digitization, internal control
Yuxiu Lin	Department of Information Management	Full-time	PhD in Healthcare Policy and Management, University of South Carolina, USA	Evaluation of healthy life quality of the elderly, health spatial measurement analysis, health research of the elderly in rural areas, long-term care supply and demand and distribution, medical big data analysis
Xinyuan Hong	Department of Information Management	Full-time	Ph.D. in Information Management, National Sun Yat-sen University	Business applications of decision support systems, knowledge management, e-commerce/e-government, and data mining
Longquan Lu	Department of Business Administration	Full-time	PhD in Marketing, University of Mississippi	Marketing research. Marketing ethics. database marketing
Zhongwei Shen	Department of Mathematics	Full-time	Ph.D., Institute of Statistics, National Central University	Long-term follow-up data analysis, robust statistical inference
Hongbin Lai	Department of Economics	Full-time	PhD in Economics, Pennsylvania State University	econometrics, general economics
Jingyi Lai	Department of Finance	Full-time	PhD in Agricultural Economics, Michigan State University	Financial Risk Management, Financial Measurement
Bo Nong Huang	Department of Economics	Distinguished Professor	Ph.D., Department of Economics, University of Oklahoma, USA	Financial Economics, International Finance
Mushu Yun	Department of Finance	Full-time	PhD in Finance, Louisiana State University	Financial Asset Pricing, Mutual Fund Market Behavior
Zhiqi Huang	Department of Finance	Full-time	PhD in Finance, Queen's University, Canada	Corporate Finance, Investment, International Finance
Hancheng Zhang	Department of Information Engineering	Part-time	Ph.D. in Information Engineering, National Chung Cheng University	Database, web design
Renhong Huang	Department of Information Engineering	Distinguished Professor	PhD in Information Science, University of Massachusetts	Computer Networks, Digital Learning, Internet Applications
Baoda You	Department of Information Engineering	Full-time	PhD in Electrical Engineering, Purdue University	Intelligent system design, intelligent network, ICAL, nonlinear system, e-Learning, computer-aided instruction
Pei-Ju Lee	Department of Information Management	Full-time	PhD in Information Science, University of Pittsburgh	Big data information integration and analysis, data mining and text mining, database management, UI/UX
Tingzhao Hou	Department of Electrical Engineering、Department of Communication	Full-time	PhD in Electrical Engineering, University of Southern California	Asynchronous transfer mode system, communication network, performance analysis, communication protocol, wireless network
Wei-Yen Hsu	Department of Information Management	Full-time	PhD in Information Engineering, Cheng Kung University	Image processing, image recognition, neural network (machine learning), signal processing, big data data analysis, data mining
Zhenguo Jiang	Department of Information Engineering	Full-time	Ph.D. in Information Engineering, National Tsing Hua University	Computer Vision, Machine Learning, Multimedia Processing Analysis
Jingqun Huang	Department of Electrical Engineering	Full-time	Ph.D., Institute of Electronic Engineering, National Chiao Tung University	Image processing, computer vision, pattern recognition, machine learning, smart sensor network, artificial learning intelligence

6. Total credits of compulsory courses and elective credits
The core courses of this program include two types of compulsory and elective (such as the curriculum planning table), with a total of 18 credits, including 9 credits of compulsory, 6 credits of compulsory elective and 3 credits of elective. According to the school’s curriculum regulations, at least 9 credits of the 18-credit core subjects should be cross-collegiate or departmental courses, and can be counted as graduation credits for the student’s department, double major, auxiliary department or other courses.

7. Curriculum planning

Category		Course Title	Credit	Department	Remark
Common Compulsory (9 credits)		Statistics (1) (or Statistics)	3 credits	Department of Finance, Department of Economics, Department of Labor, Department of Business Administration
		Database Management	3 credits	Department of Accounting Information, Department of Information Management
		Data Mining and Application	3 credits	Department of Information Management
Required electives (6 credits)	Statistics advanced courses (choose one more)	Multivariate Data Analysis	3 credits	Department of Business Administration、Department of Information Management
		Regression Analysis	3 credits	Department of Mathematics	Prerequisite "Statistics"
		Statistics (2)	3 credits	Department of Finance、Department of Economics、Department of Business Administration
		Econometrics (1)	3 credits	Department of Economics、Department of Finance	Prerequisite courses "Principles of Economics (1) and Principles of Economics (2)"
		Econometrics (2)	3 credits	Department of Economics	Prerequisite subject "Econometrics (1)"
	Practical courses (choose one more)	Financial big data analysis	3 credits	Department of Finance	Prerequisite subject "Statistics (2)"
		Financial data analysis	3 credits	Department of Finance
		R language application and analysis method	3 credits	Department of Economics
		Big data analysis and application	3 credits	Department of Information Management
		Python language programming	3 credits	Department of Information Management
		R programming	3 credits	Department of Information Management
Elective (3 credits)		Internet of Things Core Technology	3 credits	Department of Information Engineering
		Neural Network	3 credits	Department of Information Engineering
		Network community analysis and management	3 credits	Department of Information Management
		Medical big data analysis and application	3 credits	Department of Information Management
		Introduction to Algorithms	3 credits	Department of Electrical Engineering、Department of Communication
		Machine learning	3 credits	Department of Information Management、Department of Information Engineering、Department of Electrical Engineering

8. Student Application and Approval Procedures

9. Awarding of course certificate
According to the "National Chung Cheng University Cross-Faculty (Institution) Course Setting Key Points", whoever completes the courses specified in this course will be issued a credit course certificate by the school after passing the review.

10. Other special regulations
This key point was approved by the curriculum committee meeting of the Department of Information Management, and implemented after being approved by the curriculum committee of the school, and the same is true when it is revised.

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