| Academic Year |
2024Year |
School/Graduate School |
Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Transdisciplinary Science and Engineering Program |
| Lecture Code |
WSQN2701 |
Subject Classification |
Specialized Education |
| Subject Name |
Data Analytics for Sustainable Development |
Subject Name
（Katakana） |
|
Subject Name in
English |
Data Analytics for Sustainable Development |
| Instructor |
CHIKARAISHI MAKOTO,KASHIMA SAORI,HOSAKA TETSURO,LEE HANSOO |
Instructor
(Katakana) |
チカライシ マコト,カシマ サオリ,ホサカ テツロウ,リー ハンスウ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, Second Semester, 4Term |
| Days, Periods, and Classrooms |
(4T) Tues1-4：East Library 3F Seminar Rm A,East Library 3F Seminar Rm B,East Library 3F Seminar Rm C |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
|
| Lecture, exercise |
| Credits |
2.0 |
Class Hours/Week |
|
Language of Instruction |
E
:
English |
| Course Level |
5
:
Graduate Basic
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
10
:
Integrated Engineering |
| Eligible Students |
Master Students |
| Keywords |
Data Analysis, sustainable development, interdisciplinary studies (rooted in urban and transportation planning, epidemiology, ecology, climatology) |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
This course is designed for understanding basic statistics and introducing data analytic skills with R for sustainable development studies, including urban and transportation planning, epidemiology, ecology, and climatology. After basic introductory classes on statistics with exercises, four case studies on different disciplines will follow, including advanced methods used in each research field. |
| Class Schedule |
1-2. Introduction and descriptive statistics (+exercise)
3. Statistical tests (+exercise)
4. Statistical tests (+exercise)
5. Linear regression (+exercise)
6. Linear regression (+exercise)
7. Logistic regression (+exercise)
8. Logistic regression (+exercise)
9. Case 1: Estimation of treatment effects (+exercise)
10. Case 1: Estimation of treatment effects (+exercise)
11. Case 2: Count data model: Poisson model and its extensions (+exercise)
12. Case 2: Count data model: Poisson model and its extensions (+exercise)
13. Case 3: Discrete choice model: multinomial logit model and its extensions (+exercise)
14. Case 3: Discrete choice model: multinomial logit model and its extensions (+exercise)
15. Case 4: Time series analysis: ARMA model and its extensions (+exercise)
16. Case 4: Time series analysis: ARMA model and its extensions (+exercise)
The maximum number of students for this course is 30. |
Text/Reference
Books,etc. |
Crawley, M.J. (2014) Statistics: An Introduction Using R, Second Edition, Wiley. (翻訳版：野間口健太郎・菊池泰樹 (2016) 統計学：Rを用いた入門書, 第2版, 共立出版)
Dalgaard, P. (2008) Introductory Statistics with R, Springer. |
PC or AV used in
Class,etc. |
|
| (More Details) |
R (https://www.r-project.org/) |
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
All lectures will be made with exercises in R. Students are required to prepare a PC capable of running R. |
| Requirements |
The maximum number of students for this course is 30. A higher priority will be given to the students who belong to the Transdisciplinary Science and Engineering (TSE) Program. |
| Grading Method |
Four exercises (5% for each)
Four reports (one report for each case. 20% for each) |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
|
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |