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- 工学院，物質理工学院，環境・社会理工学院共通科目
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School of Environment and Society
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- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Undergraduate major in Mathematical and Computing Science
- Instructor(s)
- Watanabe Sumio Suzuki Taiji
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue3-4(W834) Fri3-4(W834)
- Group
- -
- Course number
- MCS.T332
- Credits
- 2
- Academic year
- 2016
- Offered quarter
- 4Q
- Syllabus updated
- 2017/1/11
- Lecture notes updated
- 2017/2/2
- Language used
- Japanese
- Access Index
-
Course description and aims
The goal of this course is that students understand basic data analysis with applications to practical problems. In this lecture, practical tools are introduced, however, students should not choose academic lecture according to practicality. The true study based on mathematics gives you the wide viewpoints and deep insights.
Student learning outcomes
Let's study and understand basic points of data analysis with applications to practical problems. Then you understand the limit of data analysis.
Keywords
data analysis, real world, tool, and the limit of data analysis
Competencies that will be developed
| Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
For data analysis methods, their mathematical foundations and applications to practical problems are explained. Data analysis is a set of tools, which should be employed in a correct manner.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | True distribution is different from any statistical model. | Data analysis is a set of tools, which is not the real world. |
| Class 2 | regression analysis | Application of regression analysis |
| Class 3 | discriminant analysis | Application of discriminant analysis |
| Class 4 | factor analysis | Application of factor analysis |
| Class 5 | Principal component analysis | Application of principal component analysis |
| Class 6 | cluster analysis | Application of cluster analysis |
| Class 7 | time series analysis | Application of time series analysis |
| Class 8 | Summary | Summary of data analysis |
| Class 9 | Bayes estimation | Application of Bayesian estimation |
| Class 10 | Hierarchical Bayes | Application of Hierarchical Bayes |
| Class 11 | Hypothesis test | Application of hypothesis test |
| Class 12 | Problems of Hypothesis test | Problem of hypothesis test |
| Class 13 | Model Evaluation | Application of model evaluation |
| Class 14 | Real world | Data analysis is a set of tools. |
| Class 15 | Summary | Real world and data analysis. |
Textbook(s)
None.
Reference books, course materials, etc.
None.
Assessment criteria and methods
Reports.
Related courses
- MCS.T223 ： Mathematical Statistics
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Probability Theory and Statistics are necessary. Probability theory based on measure theory is not used in this lecture, however, it is very important, hence I recommend that students had better learn it.
