30104001 Fundamental Data Science

Hiroshima University Syllabus

Japanese

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

Lecture Code 30104001 Subject Classification Information and Data Science Courses

Subject Name Fundamental Data Science

Subject Name
（Katakana）

Subject Name in
English Fundamental Data Science

Instructor NUNES TENDEIRO JORGE

Instructor
(Katakana) ナヌッシュ　テンデイル　ジョージ

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 E : English

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 lesson1: Guidance and Introduction
lesson2: Data acquisition and open data, data science ethics
lesson3: Types of data and descriptive statistics
lesson4: Descriptive statistics
lesson5: Visualizing data in R
lesson6: Correlation and regression
lesson7: Simple regression analysis using Excel
lesson8: Principal component analysis and cluster analysis using R
lesson9: Probability
lesson10: Random variable and probability distributions
lesson11: Basic probability distributions
lesson12: Bivariate probability distributions
lesson13: Methods for data collection
lesson14: Point estimation and interval estimation
lesson15: 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. Be aware that the course contents are largely overlapped between the current course and the following courses: "Fundamental Data Science (30101001)" and "Fundamental Data Science (30830001)". Therefore, once you enroll in this course, you cannot enroll to the other previously mentioned two courses (note that the other two courses are given in Japanese).

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, Microsoft Teams, and Microsoft Stream. 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,
Lesson 7: Dec 20th,
Lesson 8: Dec 20th,
Lesson 9: Jan 8th,
Lesson 10: Jan 10th,
Lesson 11: Jan 15th,
Lesson 12: Jan 15th,
Lesson 13: Jan 22nd,
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	30104001	Subject Classification	Information and Data Science Courses
Subject Name	Fundamental Data Science
Subject Name （Katakana）
Subject Name in English	Fundamental Data Science
Instructor	NUNES TENDEIRO JORGE
Instructor (Katakana)	ナヌッシュ　テンデイル　ジョージ
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	E : English
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	lesson1: Guidance and Introduction lesson2: Data acquisition and open data, data science ethics lesson3: Types of data and descriptive statistics lesson4: Descriptive statistics lesson5: Visualizing data in R lesson6: Correlation and regression lesson7: Simple regression analysis using Excel lesson8: Principal component analysis and cluster analysis using R lesson9: Probability lesson10: Random variable and probability distributions lesson11: Basic probability distributions lesson12: Bivariate probability distributions lesson13: Methods for data collection lesson14: Point estimation and interval estimation lesson15: 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. Be aware that the course contents are largely overlapped between the current course and the following courses: "Fundamental Data Science (30101001)" and "Fundamental Data Science (30830001)". Therefore, once you enroll in this course, you cannot enroll to the other previously mentioned two courses (note that the other two courses are given in Japanese).
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, Microsoft Teams, and Microsoft Stream. 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, Lesson 7: Dec 20th, Lesson 8: Dec 20th, Lesson 9: Jan 8th, Lesson 10: Jan 10th, Lesson 11: Jan 15th, Lesson 12: Jan 15th, Lesson 13: Jan 22nd, 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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