PROBABILITY[DS1](GIGA/GG/GI)

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Lecturer(s)	SAKAI, SHOTARO
Credit(s)	2
Academic Year/Semester	2025 Fall
Day/Period	Mon.1
Campus	SFC
Class Format	Face-to-face classes (conducted mainly in-person)
Registration Number	45516
Faculty/Graduate School	POLICY MANAGEMENT / ENVIRONMENT AND INFORMATION STUDIES
Year Level	1, 2, 3, 4
Field	FUNDAMENTAL SUBJECTS SUBJECTS OF DATA SCIENCE DATA SCIENCE 1
Grade Type	This item will appear when you log in (Keio ID required).
English Support	With English Support
Prerequisites(Recommended)	B3101 統計基礎/INTRODUCTION TO STATISTICS B3103 微分・積分/CALCULUS
Related Classes	B3104 線形代数/LINEAR ALGEBRA
Location	SFC
Course Requirements	This item will appear when you log in (Keio ID required).
Student Screening *For conditions regarding "additional permission", please refer to the "Student Screening Details" section. Approval for additional permission is at the lecturer's discretion, and is not guaranteed.	This item will appear when you log in (Keio ID required).
Screening Method *If selection is by lottery: Complete the course registration process and check your permission status on the course registration screen. If selection is by assignment: Carefully review the "Student Screening Details" section, register for the course via the "Assignment Submission URL," and submit the required assignment.	This item will appear when you log in (Keio ID required).
Expected Number of Acceptances	This item will appear when you log in (Keio ID required).
Contact(Mail)	This item will appear when you log in (Keio ID required).
Course Description	Students learn statistical science, modelling and simulation to develop mathematical reasoning skills applicable to data analysis and decision making in a variety of fields.
K-Number	FPE-CO-03013-212-12

Course Administrator	Faculty/Graduate School	FPE	POLICY MANAGEMENT / ENVIRONMENT AND INFORMATION STUDIES
Course Administrator	Department/Major	CO
Main Course Number	Level	0	Faculty-wide
	Major Classification	3	Fundamental Subjects (Other Than Introductory Subjects, Subjects of Language Communication)
	Minor Classification	01	Subjects of Data Science - Data Science 1
	Subject Type	3	Elective subject
Supplemental Course Information	Class Classification	2	Lecture
	Class Format	1	Face-to-face classes (conducted mainly in-person)
	Language of Instruction	2	English
Academic Discipline		12	Analysis, applied mathematics, and related fields

Course Summary

This course offers an introduction to the mathematical theory of probability. The fundamentals of set theory and combinatorics are presented as the foundation for developing probability theory. Following the introduction of the concept of probability, key topics are explored, including conditional probability, independence, Bayes' theorem, random variables, probability distributions, expectation, variance, the law of large numbers, and the central limit theorem.

Course Description/Objectives/Teaching Method/Intended Learning Outcome

The theme of this lecture is the mathematical theory of probability with a focus on data science. Probability is, in fact, a fundamental tool in statistics, information theory, and computer science. Building on high school mathematics, we will explore several important topics in probability. The goal is to develop familiarity with this increasingly important subject so that students can freely apply basic probability theory in their respective fields of study in the future.

Lab / Skill-development / On-site training	An educational approach in which students deepen their understanding of educational content through first-hand experience and trial-and-error by initiating their own activities at facilities and accommodations both on and off campus.
Fieldwork	An educational approach in which students conduct surveys and observations on their own to collect information both on and off campus.
Presentations	An educational approach in which students deepen their understanding of educational content by preparing materials and delivering presentations in class.
Discussions, Debates	An educational approach in which students deepen their understanding of educational content through sharing and discussing their opinions on designated topics.
Group work	An educational approach in which students are divided into small groups and assigned tasks on issues related to educational content.
Role-playing	An educational approach in which students deepen their understanding of educational content by being assigned specific roles to play, thus allowing them to experience different viewpoints.
Flipped classroom	An educational approach in which students study the course lecture in advance, and questions and discussions relating to that content are then held in class.
Problem-based learning	An educational approach in which students acquire knowledge and skills related to educational content by attempting to tackle specific projects or problems posed to them.

Keio University Syllabus and Timetable