Hiroshima University Syllabus

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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
Subject Name in
Fundamental Data Science
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 Outcome1. 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 
Not specified 
PC or AV used in
(More Details) handouts, lecture slides, requisite PC specified by Hiroshima University 
Learning techniques to be incorporated  
Suggestions on
Preparation and
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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