8E550101 Data Science

Hiroshima University Syllabus

Japanese

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 　

Instructor
(Katakana) 　

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.

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
Instructor (Katakana)
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.

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