ANM01001 Foundation of Date Analysis

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School School of Integrated Arts and Sciences Department of Integrated Arts and Sciences

Lecture Code ANM01001 Subject Classification Specialized Education

Subject Name データ解析序説

Subject Name
（Katakana）データカイセキジヨセツ

Subject Name in
English Foundation of Date Analysis

Instructor HASHIMOTO SHINTARO

Instructor
(Katakana) ハシモト　シンタロウ

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

Days, Periods, and Classrooms (4T) Mon5-8：IAS K109

Lesson Style Lecture Lesson Style
(More Details) 　

Lecture

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

Course Level 2 : Undergraduate Low-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 01 : Mathematics/Statistics

Eligible Students Students who are interested in data analysis

Keywords Mathematical statistics, 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 designed to allow students to understand the basics of mathematical statistics and to learn the meanings of the typical data analysis methods
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Integrated Arts and Sciences
（Knowledge and Understanding）
・Knowledge and understanding of the importance and characteristics of each discipline and basic theoretical framework.
（Abilities and Skills）
・The ability and skills to collect and analyze necessary literature or data among various sources of information on individual academic disciplines.
・The ability and skills to specify necessary theories and methods for consideration of issues.

Class Objectives
/Class Outline Various factors are intertwined in the natural and social phenomena observed in our daily lives. To correctly understand these phenomena, it necessary to summarize the observed data in a simplified form or to examine the relationship between the factors that produce these phenomena. Data analysis is an important tool for this. In this series of lectures, I will introduce the basics of data analysis techniques.

Class Schedule Lesson 1: Introduction to R (1)
Lesson 2: Introduction to R (2)
Lesson 3: Data visualization (1)
Lesson 4: Data visualization (2)
Lesson 5: Principle component analysis
Lesson 6: Clustering
Lesson 7: Group work (1)
Lesson 8: Group work (1)
Lesson 9: Hypothesis testing (1)
Lesson 10: Hypothesis testing (2)
Lesson 11: Linear regression model（1）
Lesson 12: Linear regression model（2）
Lesson 13: Generalized linear model (1)
Lesson 14: Generalized linear model (2)
Lesson 15: Presentation

Several quiz will be given in the class and a final report will be required.

Text/Reference
Books,etc. Some reference books will be introduced in the first class.

PC or AV used in
Class,etc. 　

(More Details) Projector (PC)，your own PC

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review In order to adequately understand the data analysis techniques, it is important to apply them to real data.

Requirements Prior knowledge of the basics of mathematics (Linear algebra and differential and integral calculus) and statistics is assumed.

Grading Method Class quiz and final report.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message Please bring your own PC in the class. In the exercise using R programming language, the multivariate data analysis methods are applied to some data.

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 Integrated Arts and Sciences Department of Integrated Arts and Sciences
Lecture Code	ANM01001	Subject Classification	Specialized Education
Subject Name	データ解析序説
Subject Name （Katakana）	データカイセキジヨセツ
Subject Name in English	Foundation of Date Analysis
Instructor	HASHIMOTO SHINTARO
Instructor (Katakana)	ハシモト　シンタロウ
Campus	Higashi-Hiroshima	Semester/Term	2nd-Year, Second Semester, 4Term
Days, Periods, and Classrooms	(4T) Mon5-8：IAS K109
Lesson Style	Lecture	Lesson Style (More Details)
Lecture
Credits	2.0	Class Hours/Week		Language of Instruction	J : Japanese
Course Level	2 : Undergraduate Low-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	01 : Mathematics/Statistics
Eligible Students	Students who are interested in data analysis
Keywords	Mathematical statistics, 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 designed to allow students to understand the basics of mathematical statistics and to learn the meanings of the typical data analysis methods
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Integrated Arts and Sciences （Knowledge and Understanding）・Knowledge and understanding of the importance and characteristics of each discipline and basic theoretical framework. （Abilities and Skills）・The ability and skills to collect and analyze necessary literature or data among various sources of information on individual academic disciplines. ・The ability and skills to specify necessary theories and methods for consideration of issues.
Class Objectives /Class Outline	Various factors are intertwined in the natural and social phenomena observed in our daily lives. To correctly understand these phenomena, it necessary to summarize the observed data in a simplified form or to examine the relationship between the factors that produce these phenomena. Data analysis is an important tool for this. In this series of lectures, I will introduce the basics of data analysis techniques.
Class Schedule	Lesson 1: Introduction to R (1) Lesson 2: Introduction to R (2) Lesson 3: Data visualization (1) Lesson 4: Data visualization (2) Lesson 5: Principle component analysis Lesson 6: Clustering Lesson 7: Group work (1) Lesson 8: Group work (1) Lesson 9: Hypothesis testing (1) Lesson 10: Hypothesis testing (2) Lesson 11: Linear regression model（1） Lesson 12: Linear regression model（2） Lesson 13: Generalized linear model (1) Lesson 14: Generalized linear model (2) Lesson 15: Presentation Several quiz will be given in the class and a final report will be required.
Text/Reference Books,etc.	Some reference books will be introduced in the first class.
PC or AV used in Class,etc.
(More Details)	Projector (PC)，your own PC
Learning techniques to be incorporated
Suggestions on Preparation and Review	In order to adequately understand the data analysis techniques, it is important to apply them to real data.
Requirements	Prior knowledge of the basics of mathematics (Linear algebra and differential and integral calculus) and statistics is assumed.
Grading Method	Class quiz and final report.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	Please bring your own PC in the class. In the exercise using R programming language, the multivariate data analysis methods are applied to some data.
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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