HB410000 Data Science

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School School of Science

Lecture Code HB410000 Subject Classification Specialized Education

Subject Name データ科学

Subject Name
（Katakana）データカガク

Subject Name in
English Data Science

Instructor ODA RYOYA

Instructor
(Katakana) オダ　リョウヤ

Campus Higashi-Hiroshima Semester/Term 2nd-Year, Second Semester, 4Term

Days, Periods, and Classrooms (4T) Thur5-6,Fri3-4：IMC-Main 2F PC Rm

Lesson Style Lecture Lesson Style
(More Details) Face-to-face

Lecture using power point and blackboard

Credits 2.0 Class Hours/Week 4 Language of Instruction J : Japanese

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 01 : Mathematics/Statistics

Eligible Students

Keywords Data base, Search, Hypothesis testing, Estimation, Data analysis

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) Mathematics
（Knowledge and Understanding）
・Acquiring knowledge and vision on advanced theories as an extension of core theory of modern mathematics.
（Abilities and Skills）
・To learn basic knowledge, skills, and attitudes related to information. Based on them, to be able to process, output and input information, as well as to utilize information appropriately.

Class Objectives
/Class Outline Data science is the science for the data itself. We study data filing (data base), data searching, data analysis, and perform the exercise using suitable software if needed.

Class Schedule lesson1 Introduction
lesson2 Getting used to the R 1
lesson3 Getting used to the R 2
lesson4 Getting used to the R 3
lesson5 Getting used to the R 4
lesson6 Two-sample test
lesson7 single regression
lesson8 Multiple regression analysis 1
lesson9 Multiple regression analysis 2
lesson10 Generalized linear model
lesson11 Visualization of multidimensional data
lesson12 Cluster analysis 1
lesson13 Cluster analysis 2
lesson14 Discriminant analysis 1
lesson15 Discriminant analysis 2

Two report assignments are scheduled.

Text/Reference
Books,etc. Not specified

PC or AV used in
Class,etc. Handouts, moodle

(More Details) Blackboard, power-point

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 class using the statistical analysis software R. We will cover everything from the basics of how to use R to data analysis. In the first class, we will introduce R (Posit cloud or RStudio). If you are using your own PC, you will need to install Posit cloud or RStudio.

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	2025Year	School/Graduate School	School of Science
Lecture Code	HB410000	Subject Classification	Specialized Education
Subject Name	データ科学
Subject Name （Katakana）	データカガク
Subject Name in English	Data Science
Instructor	ODA RYOYA
Instructor (Katakana)	オダ　リョウヤ
Campus	Higashi-Hiroshima	Semester/Term	2nd-Year, Second Semester, 4Term
Days, Periods, and Classrooms	(4T) Thur5-6,Fri3-4：IMC-Main 2F PC Rm
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face
Lecture using power point and blackboard
Credits	2.0	Class Hours/Week	4	Language of Instruction	J : Japanese
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	01 : Mathematics/Statistics
Eligible Students
Keywords	Data base, Search, Hypothesis testing, Estimation, Data analysis
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)	Mathematics （Knowledge and Understanding）・Acquiring knowledge and vision on advanced theories as an extension of core theory of modern mathematics. （Abilities and Skills）・To learn basic knowledge, skills, and attitudes related to information. Based on them, to be able to process, output and input information, as well as to utilize information appropriately.
Class Objectives /Class Outline	Data science is the science for the data itself. We study data filing (data base), data searching, data analysis, and perform the exercise using suitable software if needed.
Class Schedule	lesson1 Introduction lesson2 Getting used to the R 1 lesson3 Getting used to the R 2 lesson4 Getting used to the R 3 lesson5 Getting used to the R 4 lesson6 Two-sample test lesson7 single regression lesson8 Multiple regression analysis 1 lesson9 Multiple regression analysis 2 lesson10 Generalized linear model lesson11 Visualization of multidimensional data lesson12 Cluster analysis 1 lesson13 Cluster analysis 2 lesson14 Discriminant analysis 1 lesson15 Discriminant analysis 2 Two report assignments are scheduled.
Text/Reference Books,etc.	Not specified
PC or AV used in Class,etc.	Handouts, moodle
(More Details)	Blackboard, power-point
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 class using the statistical analysis software R. We will cover everything from the basics of how to use R to data analysis. In the first class, we will introduce R (Posit cloud or RStudio). If you are using your own PC, you will need to install Posit cloud or RStudio.
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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