Data Analysis Method

Course title

L0039000

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

The students can understand knowledge of mathematics and statistics necessary for data analysis
The students can understand basic ideas of statistical analysis necessary for data analysis
The students can acquire basic techniques of statistical analysis necessary for data analysis

Relationship between 'Goals and Objectives' and 'Course Outcomes'

	Final report	Quiz	Final exam	Total.
1.	10%	10%	13%	33%
2.	10%	10%	13%	33%
3.	10%	10%	14%	34%
Total.	30%	30%	40%	-

Evaluation method and criteria

Quiz 30%、Final examination 40% report 30%
Students will get 60 points, if they understand fundamental algorithms and can apply it to small/middle sized data.

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 Difference test and analysis of variance Cross tabulation and independence test	Read Chap.1 and Chap. 2 of textbook	150minutes
2.	Data analysis practice (1) Statistical test	Read Chap. 3	150minutes
3.	Single regression analysis Multiple regression analysis Quantification method 1 & 2	Read Chap. 3.1	150minutes
4.	Data analysis practice (2) regression analysis	Review Chap. 3.3	150minutes
5.	Variable synthesis and principal component analysis Quantification method 3	Review the distributed material.	150minutes
6.	Data analysis practice (3) principal component analysis	Review the distributed material.	150minutes
7.	Clustering - k-means - agglomerative clustering - Density-based method - Other methods Model selection - AIC	Review Chap. 4	150minutes
8.	Data analysis practice (4) Clustering	Review Chap. 4	420minutes
9.	Decision tree SVM	Review Chap. 4.6	150minutes
10.	Data analysis practice (5) Decision tree SVM	Review Chap.4.7	150minutes
11.	Neural Network and Deep learning Data analysis practice (6) Neural Network and Deep learning	Review the distributed material.	150minutes
12.	Exercise of Data analysis (1) Classification analysis for realistic data	Review the distributed material.	150minutes
13.	Exercise of Data analysis (2) Regression analysis for realistic data	Review the distributed material.	150minutes
14.	Review and final examination	Review and reporting	430minutes
Total.	-	-	2650minutes

Feedback on exams, assignments, etc.

ways of feedback	specific contents about "Other"
Feedback in the class

Textbooks and reference materials

Textbook: Nagatugu Yamanouchi.,Introduction of data analysis with Python, Ohm sha

Prerequisites

Carculus, linear 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 applicable
N/A	N/A

Education related SDGs:the Sustainable Development Goals

9.INDUSTRY, INNOVATION AND INFRASTRUCTURE

Last modified : Wed Mar 12 04:09:53 JST 2025