### 2017　Data Analysis

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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
2017
Offered quarter
4Q
Syllabus updated
2017/4/18
Lecture notes updated
2018/1/31
Language used
Japanese
Access Index

### Course description and aims

The goal of this lecture is that students understand basic data analysis with applications to practical problems. Both practical tools and basic concepts 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, the limit of data analysis, and true study

### 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 Prediction Accuracy and model evaluation Application of model evaluation based on prediction accuracy
Class 14 Real world and model identification Model identification in the real world
Class 15 Summary Real world and data analysis.

None.

None.

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.