2025Year

First Semester

Practical Data Analysis

Lecture

Practical Data Analysis

Z0400012 Practical Data Analysis

MIKI Yoshio

2.0Credits

Tue.2Period

Shinjuku Remote

.,A-0473教室（大学院工学研究科）

A) A high degree of specialized expertise　100%
B) The skills to use science and technology　0%
C) The ability to conduct research independently, knowledge pertaining to society and occupations, and sense of ethics required of engineers and researchers　0%
D) Creative skills in specific areas of specialization　0%

This class is a [hybrid] class with BYOD prerequisite. This is a measure to enable students to take the class both in Shinjuku and Hachioji, so students who belong to a laboratory in Shinjuku can take the class in the classroom. Students in Hachioji can take the class remotely. However, it is somewhat difficult to take the class on a small screen of a PC (smart phones are not recommended), so please take the class on a large screen or in an environment with a second display.
The course is designed to teach data analysis skills that are common and necessary for engineers who are engaged in the repetitive basic operations of “acquiring data”, “analyzing data”, “analyzing the causes of results”, and “considering improvement methods”. The content of the course is designed to be at the level of the Statistics Certification Examination Level2 or higher.

Students taking this course must have studied undergraduate statistics and multivariate analysis.

Discussion Debate／Presentation／Interactive classes using ICT

1. Guidance
2.Extension of definition of probability and mother function
3. Law of large numbers, Central limit theorem (proof)
4. Probability distributions and applications
5. Statistics and various estimation methods
6. Information content and interval estimation
7. Basic testing methods
8. Various probability distributions and testing methods
9. Basics of stochastic processes Time series models
11.Regression analysis and design of experiments
12.Multivariate analysis
13. Bayesian statistics and Bayesian inference
14. Artificial Intelligence and Mathematical Statistical Models
15. Review

Grades of A+, A, B, C, D, and F will be given based on the assignments (reports) and final reports (which may be classroom tests) given in each class.

Grades will be released on the university-wide grade release date.

Not specified

University of Tokyo Publication Society ISBN 978-4-13-042065-5

Email mikiyo@cc.kogakuin.ac.jp for questions

Although the course is structured around statistics, it aims to cultivate knowledge that can be used in experiments and research, rather than being theory-driven.

Applicable

データサイエンティストの経験がある教員が、具体的な問題の解決法を活かし、高度なデータ分析アルゴリズムについて講義する。

Informatics Program