KA238002 Informatics and Data Science Exercise IV(Data Science Program)

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School School of Informatics and Data Science

Lecture Code KA238002 Subject Classification Specialized Education

Subject Name 情報科学演習IV（データ科学プログラム）

Subject Name
（Katakana）ジョウホウカガクエンシュウ４（データカガクプログラム）

Subject Name in
English Informatics and Data Science Exercise IV(Data Science Program)

Instructor HIRAKAWA MAKOTO,YANAGIHARA HIROKAZU,TAKAFUJI DAISUKE,YAMADA HIROSHI,SHIMA TADASHI,MONDEN REI,TING HIAN ANN,NUNES TENDEIRO JORGE,MUKAIDANI HIROAKI,SUMIYA TAKAHIRO,MORIMOTO YASUHIKO

Instructor
(Katakana) ヒラカワ　マコト,ヤナギハラ　ヒロカズ,タカフジ　ダイスケ,ヤマダ　ヒロシ,シマ　タダシ,モンデン　レイ,ティン　ヒェン　アン,ナヌッシュ　テンデイル　ジョージ,ムカイダニ　ヒロアキ,スミヤ　タカヒロ,モリモト　ヤスヒコ

Campus Higashi-Hiroshima Semester/Term 3rd-Year, Second Semester, 4Term

Days, Periods, and Classrooms (4T) Fri5-7：ENG 111

Lesson Style Seminar Lesson Style
(More Details) 　

Hands-on practice

Credits 1.0 Class Hours/Week 　 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 literature reading, programing, 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.
（Comprehensive Abilities）
・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science.

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.

Class Schedule week 1. Guidance and specialized exercises on data science (instructor 1)
week 2. specialized exercises on data science (instructor 2)
week 3. specialized exercises on data science (instructor 3)
week 4. specialized exercises on data science (instructor 4)
week 5. specialized exercises on data science (instructor 5)
week 6. specialized exercises on data science (instructor 6)
week 7. specialized exercises on data science (instructor 7)
week 8. specialized exercises on data science (instructor 8)

An assignment will be given at each topic.

Text/Reference
Books,etc. Follow the instructions of the instructor of each session.

PC or AV used in
Class,etc. 　

(More Details) handouts, PC

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review Since a series of topics will be introduced in the first guidance session, students are encouraged to gather relevant information in advance for a better understanding of the exercises.
Students will be required to write reports on assignments given by the instructor.

Requirements

Grading Method An assignment will be given at each topic. These will be evaluated as a whole.

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	School of Informatics and Data Science
Lecture Code	KA238002	Subject Classification	Specialized Education
Subject Name	情報科学演習IV（データ科学プログラム）
Subject Name （Katakana）	ジョウホウカガクエンシュウ４（データカガクプログラム）
Subject Name in English	Informatics and Data Science Exercise IV(Data Science Program)
Instructor	HIRAKAWA MAKOTO,YANAGIHARA HIROKAZU,TAKAFUJI DAISUKE,YAMADA HIROSHI,SHIMA TADASHI,MONDEN REI,TING HIAN ANN,NUNES TENDEIRO JORGE,MUKAIDANI HIROAKI,SUMIYA TAKAHIRO,MORIMOTO YASUHIKO
Instructor (Katakana)	ヒラカワ　マコト,ヤナギハラ　ヒロカズ,タカフジ　ダイスケ,ヤマダ　ヒロシ,シマ　タダシ,モンデン　レイ,ティン　ヒェン　アン,ナヌッシュ　テンデイル　ジョージ,ムカイダニ　ヒロアキ,スミヤ　タカヒロ,モリモト　ヤスヒコ
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, Second Semester, 4Term
Days, Periods, and Classrooms	(4T) Fri5-7：ENG 111
Lesson Style	Seminar	Lesson Style (More Details)
Hands-on practice
Credits	1.0	Class Hours/Week		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	literature reading, programing, 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. （Comprehensive Abilities）・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science.
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.
Class Schedule	week 1. Guidance and specialized exercises on data science (instructor 1) week 2. specialized exercises on data science (instructor 2) week 3. specialized exercises on data science (instructor 3) week 4. specialized exercises on data science (instructor 4) week 5. specialized exercises on data science (instructor 5) week 6. specialized exercises on data science (instructor 6) week 7. specialized exercises on data science (instructor 7) week 8. specialized exercises on data science (instructor 8) An assignment will be given at each topic.
Text/Reference Books,etc.	Follow the instructions of the instructor of each session.
PC or AV used in Class,etc.
(More Details)	handouts, PC
Learning techniques to be incorporated
Suggestions on Preparation and Review	Since a series of topics will be introduced in the first guidance session, students are encouraged to gather relevant information in advance for a better understanding of the exercises. Students will be required to write reports on assignments given by the instructor.
Requirements
Grading Method	An assignment will be given at each topic. These will be evaluated as a whole.
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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