Course title
L09867003
Data Analysis Method

 kimura masaomi
Course description
Understand necessary knowledge of mathematics and statistics and basic concept of statistical analysis for data analysis.
Acquire basic ideas and procedures to get correct information from data
Purpose of class
Understand necessary knowledge of mathematics and statistics and acquire basic ideas and procedures for data analysis
Goals and objectives
  1. The students can understand knowledge of mathematics and statistics necessary for data analysis
  2. The students can understand basic ideas of statistical analysis necessary for data analysis
  3. The students can acquire basic techniques of statistical analysis necessary for data analysis
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. Purpose of statistical analysis
Basic knowledge of statistical analysis 		Read Chap.1 and Chap. 2 of textbook 150minutes
2. Cross tabulation and independence test
Difference test and analysis of variance 		Read Chap. 3 150minutes
3. Single regression analysis
Multiple regression analysis 		Read Chap. 3.1 150minutes
4. Variable synthesis and principal component analysis Review Chap. 3.3 150minutes
5. Quantification method 1 & 2 Review the distributed material. 150minutes
6. Quantification method 3 Review the distributed material. 150minutes
7. Clustering (1)
- k-means
- agglomerative clustering 		Review Chap. 4 150minutes
8. Clustering (2)
- Density-based method
- Other methods

Model selection
- AIM 		Review Chap. 4 420minutes
9. Decision tree Review Chap. 4.6 150minutes
10. SVM Review Chap.4.7 150minutes
11. Neural Network and Deep learning Review the distributed material. 150minutes
12. Exercise of Data analysis (1) Review the distributed material. 150minutes
13. Exercise of Data analysis (2) Review the distributed material. 150minutes
14. Review and final examination Review and reporting 430minutes
Total. - - 2650minutes
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 final exam Total.
1. 34% 34%
2. 33% 33%
3. 33% 33%
Total. 100% -
Evaluation method and criteria
Quiz 30%、Final examination or report 70%
Students will get 60 points, if they understand fundamental algorithms and can apply it to small/middle sized data.
Textbooks and reference materials
Textbook: Nagatugu Yamanouchi.,Introduction of data analysis with Python, Ohm sha
Prerequisites
Carculus, lenear algebra and statistics are necessary.
Learning of Applied mathematics is desirable.
Office hours and How to contact professors for questions
  • 13:00 to 15:00 on Friday
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
  • Course that cultivates a basic problem-solving skills
Active-learning course
Most classes are interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicatable
N/A N/A
Education related SDGs:the Sustainable Development Goals
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
Last modified : Tue Sep 01 04:20:39 JST 2020