| Academic Year |
2024Year |
School/Graduate School |
School of Informatics and Data Science |
| Lecture Code |
KA122001 |
Subject Classification |
Specialized Education |
| Subject Name |
多変量解析 |
Subject Name
（Katakana） |
タヘンリョウカイセキ |
Subject Name in
English |
Multivariate Analysis |
| Instructor |
SUMIYA TAKAHIRO |
Instructor
(Katakana) |
スミヤ タカヒロ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
2nd-Year, Second Semester, 3Term |
| Days, Periods, and Classrooms |
(3T) Thur7-10：ENG 220 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
|
| Lecture, Hands-on |
| Credits |
2.0 |
Class Hours/Week |
|
Language of Instruction |
J
:
Japanese |
| Course Level |
2
:
Undergraduate Low-Intermediate
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
01
:
Mathematics/Statistics |
| Eligible Students |
2nd degree |
| Keywords |
|
| 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) | Computer Science Program
（Abilities and Skills）
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.
Data Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently.
Intelligence Science Program
（Knowledge and Understanding）
・D1. A deep systematic understanding of the advanced intelligence of human beings and its realization by computers. |
|---|
Class Objectives
/Class Outline |
The target of this class is to learn elementary methods on multivariate analysis, and obtain ability to apply them on real data. |
| Class Schedule |
lesson 1: Introduction to statistical analysis software "R" (Data type and statistical functions)
lesson 2: Introduction to statistical analysis software "R" (function definition and control structure)
lesson 3: Cluster analysis 1 (description of method)
lesson 4: Cluster analysis 2 (data analysis exercise with R)
lesson 5: Principal components analysis 1 (description of method)
lesson 6: Principal components analysis 2 (data analysis exercise with R)
lesson 7: Factor analysis 1 (description of method)
lesson 8: Factor analysis 2 (data analysis exercise with R)
lesson 9: Visualization of multivariate data
lesson 10: Special lecture by cloud engineer (BI tool)
lesson 11: Special lecture by Local venture business owner (starting up, practical data analysis, web site construction)
lesson 12: Special lecture by Local venture business owner (starting up, practical data analysis, web site construction)
lesson 13: Generative AI and data analysis
lesson 14: Exercise 1 (Data analysis practice with free theme)
lesson 15: Exercise 2 (Data analysis practice with free theme)
final examination on week 15 or 16
Due to the schedules of external lecturer, sessions may be subject to change in order. |
Text/Reference
Books,etc. |
Slide files on moodle |
PC or AV used in
Class,etc. |
|
| (More Details) |
Hikkei PC (laptop) |
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Try to apply the data analysis methods on your own data, or any data you can find. |
| Requirements |
|
| Grading Method |
assignment, final exam |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
We will have hands-on using "R". Bring your laptop on every week. |
| 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. |