30101001 Fundamental Data Science

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School Liberal Arts Education Program

Lecture Code 30101001 Subject Classification Information and Data Science Courses

Subject Name データサイエンス基礎

Subject Name
（Katakana）データサイエンスキソ

Subject Name in
English Fundamental Data Science

Instructor ODA RYOYA,YANAGIHARA HIROKAZU,YAMADA HIROSHI,IMORI SHINPEI,MONDEN REI,SUKENAGA MASAYUKI,CASTELLANOS LUIS PEDRO,WAKAKI HIROFUMI

Instructor
(Katakana) オダ　リョウヤ,ヤナギハラ　ヒロカズ,ヤマダ　ヒロシ,イモリ　シンペイ,モンデン　レイ,スケナガ　マサユキ,カステヤノス　モスコソ　ルイス　ペドロ,ワカキ　ヒロフミ

Campus Higashi-Hiroshima Semester/Term 1st-Year, Second Semester, 4Term

Days, Periods, and Classrooms (4T) Inte：Online

Lesson Style Lecture Lesson Style
(More Details) 　

Mainly video lectures (including practical assignments)

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

Course Level 1 : Undergraduate Introductory

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 01 : Mathematics/Statistics

Eligible Students B1

Keywords basic data science, statistical methods, practical exercises using R and Excel

Special Subject for Teacher Education 　 Special Subject 　

Class Status within
Liberal Arts Education Acquire basic knowledge and skills for data science
Expected Outcome 1. To acquire basic knowledge, skills, and attitudes regarding information science and data science, and thereby to be able to process data and handle information appropriately.
2. To understand and be able to explain information ethics and social challenges for data usage.

Class Objectives
/Class Outline Learn the basics of data science and data analysis

Class Schedule lesson 1：Guidance and Introduction
lesson 2：Data acquisition and open data, data science ethics
lesson 3：Types of data and descriptive statistics
lesson 4：Descriptive statistics
lesson 5：Visualizing data in R
lesson 6：Correlation and regression
lesson 7：Simple regression analysis using Excel
lesson 8：Principal component analysis and cluster analysis using R
lesson 9：Probability
lesson 10：Random variable and probability distributions
lesson 11：Basic probability distributions
lesson 12：Bivariate probability distributions
lesson 13：Methods for data collection
lesson 14：Point estimation and interval estimation
lesson 15：Interval estimation

Quizzes will be assigned after each lecture
No final exam in the end of the semester

Text/Reference
Books,etc. Not specified

PC or AV used in
Class,etc. 　

(More Details) handouts, lecture slides, requisite PC specified by Hiroshima University

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review Review each lesson using lecture materials.

Requirements Practical exercises will be given in the course. Therefore, a requisite PC specified by Hiroshima University (https://www.hiroshima-u.ac.jp/en/about/initiatives/jyoho_ka/hikkei_pc) is mandatory to have.
Please make sure that Excel is correctly installed and ready to use.
Note that the course contents are largely overlapped between the current course and the following courses, "Fundamental Data Science (30101001)" and "Fundamental Data Science (30830001)". Thus, you can only enroll in one of the courses ("Fundamental Data Science (30104001)" is given in English).

Grading Method check tests (Quizzes)

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message The course is in on-demand format using Moodle. We will notify you about the details of the course (e.g. how to watch lecture videos) via Momiji.

Other Days and time of posting lecture materials:
Lesson 1: Nov 29th, Lesson 2: Dec 4th, Lesson 3: Dec 6th, Lesson 4: Dec 11th , Lesson 5: Dec 13th, Lesson 6: Dec 18th, Lessons 7,8: Dec 20th, Lesson 9: Jan 8th, Lesson 10: Jan 10th, Lessons 11,12: Jan 15th, Lesson 13: Jan 22th, Lesson 14: Jan 24th , Lesson 15: Jan 29th (Each lesson will be posted at 8:30 AM)

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	Liberal Arts Education Program
Lecture Code	30101001	Subject Classification	Information and Data Science Courses
Subject Name	データサイエンス基礎
Subject Name （Katakana）	データサイエンスキソ
Subject Name in English	Fundamental Data Science
Instructor	ODA RYOYA,YANAGIHARA HIROKAZU,YAMADA HIROSHI,IMORI SHINPEI,MONDEN REI,SUKENAGA MASAYUKI,CASTELLANOS LUIS PEDRO,WAKAKI HIROFUMI
Instructor (Katakana)	オダ　リョウヤ,ヤナギハラ　ヒロカズ,ヤマダ　ヒロシ,イモリ　シンペイ,モンデン　レイ,スケナガ　マサユキ,カステヤノス　モスコソ　ルイス　ペドロ,ワカキ　ヒロフミ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, Second Semester, 4Term
Days, Periods, and Classrooms	(4T) Inte：Online
Lesson Style	Lecture	Lesson Style (More Details)
Mainly video lectures (including practical assignments)
Credits	2.0	Class Hours/Week		Language of Instruction	J : Japanese
Course Level	1 : Undergraduate Introductory
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	01 : Mathematics/Statistics
Eligible Students	B1
Keywords	basic data science, statistical methods, practical exercises using R and Excel
Special Subject for Teacher Education		Special Subject
Class Status within Liberal Arts Education	Acquire basic knowledge and skills for data science
Expected Outcome	1. To acquire basic knowledge, skills, and attitudes regarding information science and data science, and thereby to be able to process data and handle information appropriately. 2. To understand and be able to explain information ethics and social challenges for data usage.
Class Objectives /Class Outline	Learn the basics of data science and data analysis
Class Schedule	lesson 1：Guidance and Introduction lesson 2：Data acquisition and open data, data science ethics lesson 3：Types of data and descriptive statistics lesson 4：Descriptive statistics lesson 5：Visualizing data in R lesson 6：Correlation and regression lesson 7：Simple regression analysis using Excel lesson 8：Principal component analysis and cluster analysis using R lesson 9：Probability lesson 10：Random variable and probability distributions lesson 11：Basic probability distributions lesson 12：Bivariate probability distributions lesson 13：Methods for data collection lesson 14：Point estimation and interval estimation lesson 15：Interval estimation Quizzes will be assigned after each lecture No final exam in the end of the semester
Text/Reference Books,etc.	Not specified
PC or AV used in Class,etc.
(More Details)	handouts, lecture slides, requisite PC specified by Hiroshima University
Learning techniques to be incorporated
Suggestions on Preparation and Review	Review each lesson using lecture materials.
Requirements	Practical exercises will be given in the course. Therefore, a requisite PC specified by Hiroshima University (https://www.hiroshima-u.ac.jp/en/about/initiatives/jyoho_ka/hikkei_pc) is mandatory to have. Please make sure that Excel is correctly installed and ready to use. Note that the course contents are largely overlapped between the current course and the following courses, "Fundamental Data Science (30101001)" and "Fundamental Data Science (30830001)". Thus, you can only enroll in one of the courses ("Fundamental Data Science (30104001)" is given in English).
Grading Method	check tests (Quizzes)
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	The course is in on-demand format using Moodle. We will notify you about the details of the course (e.g. how to watch lecture videos) via Momiji.
Other	Days and time of posting lecture materials: Lesson 1: Nov 29th, Lesson 2: Dec 4th, Lesson 3: Dec 6th, Lesson 4: Dec 11th , Lesson 5: Dec 13th, Lesson 6: Dec 18th, Lessons 7,8: Dec 20th, Lesson 9: Jan 8th, Lesson 10: Jan 10th, Lessons 11,12: Jan 15th, Lesson 13: Jan 22th, Lesson 14: Jan 24th , Lesson 15: Jan 29th (Each lesson will be posted at 8:30 AM)
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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