2023 Faculty Courses School of Computing Undergraduate major in Mathematical and Computing Science
Data Analysis
- Academic unit or major
- Undergraduate major in Mathematical and Computing Science
- Instructor(s)
- Sumio Watanabe
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 3-4 Tue (W8E-308(W834)) / 3-4 Fri (W8E-308(W834))
- Class
- -
- Course Code
- MCS.T332
- Number of credits
- 200
- Course offered
- 2023
- Offered quarter
- 4Q
- Syllabus updated
- Jul 8, 2025
- Language
- Japanese
Syllabus
Course overview and goals
Both Fundamentals of Probability (MCS.T212) and Mathematical Statistics (MCS.T223) are necessary. Based on probability theory and mathematical statistics, mathematical structure of data analysis is and its applications are introduced.
Course description and aims
Using probability theory and mathematical statistics, let's study and understand basic points of data analysis.
Keywords
probability theory and mathematical statistics are necessary, mathematics is the most important.
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
- This field is still evolving, so we encourage you to continue learning even after graduation.
Class flow
In data analysis, their mathematical foundations and applications to practical problems are explained.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | True distribution is different from any statistical model. |
A statistical model only a tool, which is not the real world. |
| Class 2 | regression analysis, layered neural networks |
regression analysis, layered neural networks |
| Class 3 | regression analysis, layered neural networks |
regression analysis, layered neural networks |
| Class 4 | classification |
classification |
| Class 5 | Principal component analysis, autoencoder |
Principal component analysis, autoencoder |
| Class 6 | latent variable |
latent variable |
| Class 7 | Time sequence |
Basic time sequence |
| Class 8 | time series analysis, convolutional neural network |
Application of time series analysis, convolutional neural network |
| Class 9 | Bayesian Inference |
Understanding the meaning of statistical model and prior distribution |
| Class 10 | Bayes estimation, generalization and training losses |
Application of Bayesian estimation, generalization and training losses |
| Class 11 | information criteria and cross validation |
information criteria and cross validation |
| Class 12 | marginal likelihood |
marginal likelihood |
| Class 13 | Causal inference in statistics |
Causal inference in statistics |
| Class 14 | Causal inference in statistics (2) |
Causal inference in statistics |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class. They should do so by referring to textbooks and other course material.
Textbook(s)
None.
Reference books, course materials, etc.
You need basic probability theory (MCS.T212) and mathematical statistics (MCS.T223).
Evaluation methods and criteria
Reports.
Related courses
- MCS.T212 ： Fundamentals of Probability
- MCS.T223 ： Mathematical Statistics
Prerequisites
Two lectures, both 'Fundamentals of Probability (MCS.T212)' and 'Mathematical Statistics (MCS.T223)' are necessary for this lecture. 'Lebesgue Integration (MCS.T3-4)' is recommended.
Other
Both Fundamentals of Probability (MCS.T212) and Mathematical Statistics (MCS.T223) are necessary. This lecture is mainly suitable for students of the 3rd year undergraduate.