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 Read Chap. 2 of textbook 150minutes
2. Basic knowledge of statistical analysis Read Chap.1 150minutes
3. Cross tabulation and independence test
Difference test and analysis of variance 		Read Chap. 3 150minutes
4. Single regression analysis Read Chap. 3.1 150minutes
5. Multiple regression analysis Review the distributed material. 150minutes
6. Variable synthesis and principal component analysis Review Chap. 3.3 150minutes
7. Quantification method 1 & 2 Review the distributed material. 150minutes
8. Quantification method 3 Review the distributed material. 420minutes
9. Clustering (1)
- k-means
- agglomerative clustering 		Review Chap. 4 150minutes
10. Clustering (2)
- Density-based method
- Other methods 		Review Chap. 4 150minutes
11. Decision tree Review Chap. 4.6 150minutes
12. SVM Review Chap.4.7 150minutes
13. Model selection
- AIM 		Read distributed materials 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
Intermediate examination and report 50%、Final examination and report 50%
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
Relation to the environment
Non-environment-related course
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
Last modified : Thu Mar 21 15:08:40 JST 2019