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

EnglishFundamental 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 EducationAcquire 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 OutlineLearn 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 semesterText/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

ReviewReview 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.