Japanese

Academic Year	2026Year	School/Graduate School	Liberal Arts Education Program
Lecture Code	30850001	Subject Classification	Information and Data Science Courses
Subject Name	情報・データ科学入門[1法夜,1経夜]
Subject Name （Katakana）	ジョウホウ・データカガクニュウモン
Subject Name in English	Introduction to Information and Data Sciences
Instructor	WAKUDA YUKI
Instructor (Katakana)	ワクダ　ユウキ
Campus	Higashi-Senda	Semester/Term	1st-Year,  Second Semester,  Second Semester
Days, Periods, and Classrooms	(2nd) Weds13-14：Online
Lesson Style	Seminar	Lesson Style (More Details)	Online (simultaneous interactive)
Credits	2.0	Class Hours/Week	2	Language of Instruction	J : Japanese
Course Level	1 : Undergraduate Introductory
Course Area（Area）	21 : Fundamental Competencies for Working Persons
Course Area（Discipline）	08 : Information Education
Eligible Students
Keywords	Information science, data science, information ethics, survey, programming, AI
Special Subject for Teacher Education		Special Subject
Class Status within Liberal Arts Education	Students learn information and data sciences as fundamental knowledge and skills of all the class.
Expected Outcome	1. Students will be able to properly communicate based on the knowledge and skills of information and data sciences. 2. Students will be able to explain required information ethics and social problems to use the data.
Class Objectives /Class Outline	The purpose of this class is to introduce students to fundamental knowledge and skills to use data and computer in an appropriate manner. Students will be able to solve problems in an information-oriented society in consideration for an information ethic.
Class Schedule	Session 1: Introduction to Data Science (Course Guidance)   - Course guidance   - Changes taking place in society   - Definition of terms (Data Science, AI, BI, DX)   - Introduction to the TA agent   - Pre-course survey Session 2: The Data Science Process and Required Skills   - Current state of data and AI utilization   - Skills required for data science   - Talent in demand   - [Exercise] Create a data science case study in a field of interest following CRISP-DM (AI-assisted) Session 3: Data Used in Society   - Data used in society   - [Exercise] Finding data: using open data (AI-assisted) Session 4: Application Areas of Data and AI   - Application areas of data and AI   - [Exercise] Investigate data and AI applications in a field of interest (AI-assisted) Session 5: Technologies for Data and AI Utilization   - Technologies for data and AI utilization   - [Exercise] Select one data analysis method and investigate its applications in a field of interest (AI-assisted)   - [Assignment 1] Written report on the data science process Session 6: Latest Trends in Data and AI Utilization   - Latest trends in data and AI utilization   - [Exercise] Investigate business models based on data and AI utilization (AI-assisted) Session 7: Reading Data (Fundamentals of Statistics)   - Reading data   - [Exercise] Reading data with spreadsheet software Session 8: Handling Data (Processing with Spreadsheets)   - Handling data   - [Exercise] Aggregating and organizing data with spreadsheet software (sorting, removing duplicates, filtering, extracting unique lists) Session 9: Describing Data (Visualization)   - Describing data   - Environment setup (in preparation for the Python exercises from the next session)   - [Exercise] Visualizing data with spreadsheet software Session 10: Fundamentals of Programming and Data Structures (Introduction to Python / Colab)   - Data structures and programming fundamentals   - Data handling (environment setup and basic operations)   - [Exercise] Loading data with AI assistance   - [Assignment 2] Simple data processing Session 11: Points to Note When Handling Data and AI (ELSI / Ethics)   - Points to note when handling data and AI   - Data handling (using the environment, part 1)   - [Exercise] Techniques for using generative AI appropriately and verifying its results Session 12: Data Handling with Python (pandas)   - Data handling (using the environment, part 2)   - [Exercise] Visualizing data with AI assistance Session 13: Protecting Data (Information Security)   - Points to note for protecting data   - Data handling (functions)   - [Exercise] Investigate methods of data anonymization Session 14: Protecting Data (Case Studies) and Visualization (matplotlib)   - Points to note for protecting data   - Data handling (matplotlib)   - [Exercise] Visualizing data with AI assistance Session 15: Summary and Further Development (Application to Your Own Research)   - Overall review   - Connection to applied and advanced undergraduate/graduate-level content   - Post-course survey   - [Assignment 3] Written report reflecting on the application of data science to your own research
Text/Reference Books,etc.	Teaching materials will be provided online. Related books are instructed during class.
PC or AV used in Class,etc.	Microsoft Teams, moodle
	(More Details)	Text, handout, Web, PC
Learning techniques to be incorporated	Discussions, Post-class Report
Suggestions on Preparation and Review	Follow instructions at the guidance and each class. Finish the online course within the specified period.
Requirements	The class room is specified for each fresh student. You will be able to find it on the bulletin bord.
Grading Method	Grades will be based on students' participation in class and on assignment reports.   - Per-session reports / class participation / active engagement in lectures and exercises: 60%   - Assignment reports, etc.: 40%
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	In this class PC will be used. It is not a class to learn PC and application software.
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.