| Academic Year |
2024Year |
School/Graduate School |
Common Graduate Courses (Doctoral Course) |
| Lecture Code |
8E550101 |
Subject Classification |
Common Graduate Courses |
| Subject Name |
データサイエンス |
Subject Name
（Katakana） |
データサイエンス |
Subject Name in
English |
Data Science |
| Instructor |
YAMAMURA MARIKO |
Instructor
(Katakana) |
ヤマムラ マリコ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, Second Semester, 3Term |
| Days, Periods, and Classrooms |
(3T) Tues9-10,Weds7-8：IMC-Main 2F PC Rm |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
|
| Lecture using power point and blackboard, practical training of data analysis |
| Credits |
2.0 |
Class Hours/Week |
|
Language of Instruction |
J
:
Japanese |
| Course Level |
5
:
Graduate Basic
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
01
:
Mathematics/Statistics |
| Eligible Students |
|
| Keywords |
Data reading & processing, Data visualization, Data analysis |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | This course is one of the elective subjects in the category of "Career Development and Data Literacy Courses" for Common Graduate Courses. This category of courses aims to provide opportunities for students to learn about the development of the current social systems, to gain knowledge needed for the future, to concretely tackle the challenges facing modern society, and to acquire the ability to utilize knowledge and skills. |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
We study data reading, data processing, data analysis, and perform the exercise using the R. |
| Class Schedule |
lesson1 Explanation of the lecture
lesson2 Basic operation of the R
lesson3 Data reading
lesson4 Studying the shape of data distribution
lesson5 Data filing and searching
lesson6 Scatter plot and correlation coefficient
lesson7 Single regression analysis
lesson8 Hypothesis testing for difference between means
lesson9 Graph drawing
lesson10 Estimation of multiple regression model
lesson11 Variable selection in multiple regression model
lesson12 Generalized linear model
lesson13 Visualization of multivariate data
lesson14 Cluster analysis
lesson15 Discriminant analysis |
Text/Reference
Books,etc. |
Not specified |
PC or AV used in
Class,etc. |
|
| (More Details) |
Blackboard, power-point, PC |
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Please do not hesitate to ask a question if you have dubious points. |
| Requirements |
This is a lecture for super beginners who have never used R before. I would not recommend taking this lecture to anyone who has used R before, as it will be a boring lecture. |
| Grading Method |
Report |
| 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. |