### 2023　Data Analysis

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Academic unit or major
Undergraduate major in Mathematical and Computing Science
Instructor(s)
Watanabe Sumio
Class Format
Lecture    (Face-to-face)
Media-enhanced courses
Day/Period(Room No.)
Tue3-4(W8E-308(W834))  Fri3-4(W8E-308(W834))
Group
-
Course number
MCS.T332
Credits
2
Academic year
2023
Offered quarter
4Q
Syllabus updated
2023/11/21
Lecture notes updated
-
Language used
Japanese
Access Index

### Course description and aims

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.

### Student learning outcomes

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 that will be developed

 ✔ 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/Required learning

Course schedule Required learning
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

### Out-of-Class Study Time (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.

None.

### Reference books, course materials, etc.

You need basic probability theory (MCS.T212) and mathematical statistics (MCS.T223).

Reports.

### Related courses

• MCS.T212 ： Fundamentals of Probability
• MCS.T223 ： Mathematical Statistics

### Prerequisites (i.e., required knowledge, skills, courses, etc.)

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.