National Taiwan Normal University Course Outline
Spring , 2026

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I.Course information
Serial No. 2887 Course Level Undergraduate
Course Code CSU0051 Chinese Course Name 智慧城市中的資料科學與通訊
Course Name Data Science and Communication in Smart Cities
Department Department of Computer Science and Information Engineering
Two/one semester 1 Req. / Sel. Sel.
Credits 1.0 Lecturing hours Lecture hours: 1
Teach in English Y Teach in National Languages
Prerequisite Course
Comment ◎Distance learning course All distance learning courses display synchronous learning and face-to-face learning time only.
Course Description
Day & Class Period/Location ◎Face-to-Face/Synchronous Learning
Curriculum Goals Corresponding to the Departmental Core Goal
1. Learn the concept of smart cities and some complex social issues therein. College:
 4-2 Awareness of the impact of information technology to the environment
2. Acquire problem-solving education in data science and data communication. College:
 1-2 Understanding relationship between software and hardware, and operating principle of computer systems
3. Gain hands-on experiences in data transportation and analytics. College:
 2-3 Capability to enhance professional thinking from experience
4. Be able to discuss and propose solutions to complex issues in smart cities. College:
 4-1 Understanding social responsibility of properly using information technology skills

II. General Syllabus
Instructor(s) WANG, Chao/ 王超
Schedule

Final update on 2026/01/19, for the 2026 spring semester. The updated parts are highlighted below:

The vision of smart cities emphasizes how intelligent public services may be orchestrated to the benefit of the overall community. To this end, data acquisition, communication, and interpretation lie at the heart of the design and implementation. Through this course, students will acquire problem-solving education in data science and data communication basics, gain hands-on experiences in the subject matter, and learn to discuss complex social issues in smart cities and to propose solutions to them.

This is an online course co-taught by NTNU and Kyushu University professors, and students from each university can enroll. Each student group will include both NTNU students and Kyushu University students. It is a 5-day, 16-hour intensive lectures/presentations during the weekends in the later part of the spring semester. See the course meeting dates shown on the course enrollment system.

Day 1: April 25 (2 hours, 2-4PM Taiwan time): Course overview
Day 2: May 9 (4 hours, 1-5PM Taiwan time): Data Science, Data communication
Day 3: May 16 (3 hours, 1-4PM Taiwan time): Student project workshop
Day 4: May 23 (3 hours, 1-4PM Taiwan time): Student project presentation and discussion
Day 5: June 6 (4 hours, 1-5PM Taiwan time): Selected topic in data science and communication; optional for students from Kyushu University, required for students enrolled via NTNU system.

** Notice that unlike the usual NTNU course schedule, each lecture will start on the x o'clock as stated above. For example, on Day 1 the lecture will start on 2PM, not 2:20PM.

 

This is an entry-level course, but students who would like to take this course must already know how to write Python programs. For some optional advanced topics, we will use C.

Instructional Approach
Methods Notes
Formal lecture PPT lectures in data science and communication
Group discussion Taiwanese and Japanese students will group together to work on short projects
Problem-based learning Throughout the lectures we will identify complex social issues in smart cities
Cooperative learning Inter-group presentation and intra-group discussion
Lab/Studio Hands-on data communication and analytics experiments
Grading assessment
Methods Percentage Notes
Assignments 20 % Hands-on assignments and article readings
Class discussion involvement 20 % Synchronous/asynchronous online discussion
Attendances 20 % Attending both the lectures and group activities
Presentation 40 % Final presentation on findings and solutions to complex social issues in smart cities
Adjustment methods for students
Required and Recommended Texts/Readings with References

Online learning materials and selected articles from related literature.

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