KA236001 Informatics and Data Science Exercise II

Hiroshima University Syllabus

Japanese

Academic Year 2025Year 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 ITOU YASUAKI,HIRAKAWA MAKOTO

Instructor
(Katakana) イトウ　ヤスアキ,ヒラカワ　マコト

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

Days, Periods, and Classrooms (2T) Thur1-3：East Library 3F Seminar Rm A,East Library 3F Seminar Rm B,East Library 3F Seminar Rm C,East Library 3F Seminar Rm D,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 Dijkstra's Algorithm, 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) 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 In this class, students engage in exercises on specialized and practical content based on the knowledge that has been widely learned in previous lectures of the Faculty of Information Science. Students learn the ability to find solutions for given exercises and problems, to deal with them, and to summarize the results as reports in this class. Exercises will be conducted on the theme of "parallel processing" and "data analysis using R packages". All students engage in both themes through this class.

Class Schedule week 1. guidance for the two themes, distribution for materials and notes.

2-4. Parallel processing (Ito)
Week 2: Parallel processing and parallel programming using MPI
Week 3: Parallel processing using MPI
Week 4: Parallel numerical integration using MPI

5-7. data analysis using R packages (Hirakawa)
week 5 Data handling using tidyverse package
week 6 Data visualization using ggplot2 package
week 7 Exercises in psychological data analysis

week 8. Preparation and writing of final reports

Assign final report tasks for each theme.

Text/Reference
Books,etc. Parallel processing: Materials will be distributed.
Data analysis using R packages: 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 Parallel processing: Please do each assignments by the deadline.
Data analysis using R packages: 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	2025Year	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	ITOU YASUAKI,HIRAKAWA MAKOTO
Instructor (Katakana)	イトウ　ヤスアキ,ヒラカワ　マコト
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, First Semester, 2Term
Days, Periods, and Classrooms	(2T) Thur1-3：East Library 3F Seminar Rm A,East Library 3F Seminar Rm B,East Library 3F Seminar Rm C,East Library 3F Seminar Rm D,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	Dijkstra's Algorithm, 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)	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	In this class, students engage in exercises on specialized and practical content based on the knowledge that has been widely learned in previous lectures of the Faculty of Information Science. Students learn the ability to find solutions for given exercises and problems, to deal with them, and to summarize the results as reports in this class. Exercises will be conducted on the theme of "parallel processing" and "data analysis using R packages". All students engage in both themes through this class.
Class Schedule	week 1. guidance for the two themes, distribution for materials and notes. 2-4. Parallel processing (Ito) Week 2: Parallel processing and parallel programming using MPI Week 3: Parallel processing using MPI Week 4: Parallel numerical integration using MPI 5-7. data analysis using R packages (Hirakawa) week 5 Data handling using tidyverse package week 6 Data visualization using ggplot2 package week 7 Exercises in psychological data analysis week 8. Preparation and writing of final reports Assign final report tasks for each theme.
Text/Reference Books,etc.	Parallel processing: Materials will be distributed. Data analysis using R packages: 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	Parallel processing: Please do each assignments by the deadline. Data analysis using R packages: 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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