KA236001 Informatics and Data Science Exercise II

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School School of Informatics and Data Science

Lecture Code KA236001 Subject Classification Specialized Education

Subject Name 情報科学演習II

Subject Name
（Katakana）ジョウホウカガクエンシュウ２

Subject Name in
English Informatics and Data Science Exercise II

Instructor OKAMURA HIROYUKI,ZHENG JUNJUN,HIRAKAWA MAKOTO

Instructor
(Katakana) オカムラ　ヒロユキ,テイ　シュンシュン,ヒラカワ　マコト

Campus Higashi-Hiroshima Semester/Term 3rd-Year, First Semester, 2Term

Days, Periods, and Classrooms (2T) Thur1-3：IMC-Main 2F Seminar Rm,ENG 111

Lesson Style Seminar Lesson Style
(More Details) Face-to-face, Online (on-demand)

Hands-on practice

Credits 1.0 Class Hours/Week 3 Language of Instruction B : Japanese／English

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students Dept. Info.

Keywords Please see Japanese syrabus

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) Please see Japanese syrabus
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・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.
・D3. Knowledge of hardware and software and programming ability to process data efficiently.

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.
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・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.

Intelligence Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・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.
・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT.

Class Objectives
/Class Outline Based on the broad knowledge acquired through previous courses in the School of Informatics and Data Science, students will engage in exercises covering specialized and practical topics. Through the assigned exercises and problems, students will develop the ability to identify solutions independently, address the problems, and summarize and report the results in written reports. Specifically, exercises will be conducted on the themes of “Social Science Data Analysis Using R” and “Software Development.”

Software Development
Students will experience fundamental software development by conducting team-based development of software packages using Git and GitHub.

Social Science Data Analysis Using R
Students will practice data handling, visualization, and analysis using R libraries, and gain experience in data analysis within the field of psychology.

Class Schedule Week 1: Guidance
In Informatics Exercise II, students will conduct exercises on two themes: “Social Science Data Analysis Using R” and “Software Development.” Each theme consists of three weeks of exercises.
During the guidance session, materials for both themes will be distributed and important instructions will be explained.
Weeks 2, 3, and 4
Social Science Data Analysis Using R (Instructor: Hirakawa)
- Week 2: Data Handling Using tidyverse
- Week 3: Data Visualization Using ggplot2
- Week 4: Analysis Exercises with Psychological Data
Weeks 5, 6, and 7
Software Development (Instructors: Okamura, Zheng)
- Week 5: Git/GitHub Exercises
- Week 6: Team Development Exercises
- Week 7: Package Development Exercises
Week 8: Preparation and Writing of the Final Report

A final report will be assigned for each theme.

Assign final report tasks for each theme.

Text/Reference
Books,etc. Data analysis using R packages: Materials will be distributed.
Software development: Materials will be distributed.

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

(More Details) handouts, PC

Learning techniques to be incorporated Post-class Report

Suggestions on
Preparation and
Review Data analysis using R packages: Please do each assignments by the deadline.
Software development: Please do each assignments by the deadline.

Requirements

Grading Method Comprehensively evaluate final reports and regular examinations.
Each of the two final reports must be at least 60% in order to receive credit.

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	2026Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA236001	Subject Classification	Specialized Education
Subject Name	情報科学演習II
Subject Name （Katakana）	ジョウホウカガクエンシュウ２
Subject Name in English	Informatics and Data Science Exercise II
Instructor	OKAMURA HIROYUKI,ZHENG JUNJUN,HIRAKAWA MAKOTO
Instructor (Katakana)	オカムラ　ヒロユキ,テイ　シュンシュン,ヒラカワ　マコト
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, First Semester, 2Term
Days, Periods, and Classrooms	(2T) Thur1-3：IMC-Main 2F Seminar Rm,ENG 111
Lesson Style	Seminar	Lesson Style (More Details)	Face-to-face, Online (on-demand)
Hands-on practice
Credits	1.0	Class Hours/Week	3	Language of Instruction	B : Japanese／English
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students	Dept. Info.
Keywords	Please see Japanese syrabus
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)	Please see Japanese syrabus
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Computer Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・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. ・D3. Knowledge of hardware and software and programming ability to process data efficiently. 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. （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・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. Intelligence Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・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. ・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT.
Class Objectives /Class Outline	Based on the broad knowledge acquired through previous courses in the School of Informatics and Data Science, students will engage in exercises covering specialized and practical topics. Through the assigned exercises and problems, students will develop the ability to identify solutions independently, address the problems, and summarize and report the results in written reports. Specifically, exercises will be conducted on the themes of “Social Science Data Analysis Using R” and “Software Development.” Software Development Students will experience fundamental software development by conducting team-based development of software packages using Git and GitHub. Social Science Data Analysis Using R Students will practice data handling, visualization, and analysis using R libraries, and gain experience in data analysis within the field of psychology.
Class Schedule	Week 1: Guidance In Informatics Exercise II, students will conduct exercises on two themes: “Social Science Data Analysis Using R” and “Software Development.” Each theme consists of three weeks of exercises. During the guidance session, materials for both themes will be distributed and important instructions will be explained. Weeks 2, 3, and 4 Social Science Data Analysis Using R (Instructor: Hirakawa) - Week 2: Data Handling Using tidyverse - Week 3: Data Visualization Using ggplot2 - Week 4: Analysis Exercises with Psychological Data Weeks 5, 6, and 7 Software Development (Instructors: Okamura, Zheng) - Week 5: Git/GitHub Exercises - Week 6: Team Development Exercises - Week 7: Package Development Exercises Week 8: Preparation and Writing of the Final Report A final report will be assigned for each theme. Assign final report tasks for each theme.
Text/Reference Books,etc.	Data analysis using R packages: Materials will be distributed. Software development: Materials will be distributed.
PC or AV used in Class,etc.	Handouts, Microsoft Teams, moodle
(More Details)	handouts, PC
Learning techniques to be incorporated	Post-class Report
Suggestions on Preparation and Review	Data analysis using R packages: Please do each assignments by the deadline. Software development: Please do each assignments by the deadline.
Requirements
Grading Method	Comprehensively evaluate final reports and regular examinations. Each of the two final reports must be at least 60% in order to receive credit.
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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