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 |
|---|---|---|---|---|
| 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 |
| Mushu Yun | Department of Finance | Full-time | PhD in Finance, Louisiana State University | Financial Asset Pricing, Mutual Fund Market Behavior |
| Hancheng Zhang | Department of Information Engineering | Part-time | Ph.D. in Information Engineering, National Chung Cheng University | Database, web design |
| 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 |
| 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 |
| Jing-Guo Hsu | Department of Information Management | Full-time | Ph.D., in Information Engineering, National Chiao Tung University | 5G Wireless Communications, 5G Internet of Things, Artificial Intelligence Applications |
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.
| 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 | |||
Step 1 - Submit Application
Please download the "National Chung Cheng University Interdisciplinary Credit Program and Micro-Program Application Form" from the Teaching Division website of our school, and submit the application directly to the Department of Information Management office.
Step 2 - Complete Coursework
Please follow the curriculum plan listed in this program to complete your courses.
Step 3 - Apply for Certification
Please download the "National Chung Cheng University Interdisciplinary Credit Program and Micro-Program Certification Application Form" from the Teaching Division website of our school, and submit it along with your academic transcript and "Graduation Qualification Review Form" to the Department of Information Management office for review.
Step 4 - Academic Affairs Office Review
The Department of Information Management will forward the certification application materials to the Academic Affairs Office for review.
Step 5 - Certificate Issuance
Those who meet the program requirements can obtain the program certificate issued by the Academic Affairs Office.
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.