Course title
Y02112001
Introduction to Data and Science

 YAMAZAWA Hiroshi

HIROSE Sampei

NAKAGAWA Takahiro
Course description
Recent developments in information communication and measurement technologies have made it possible to acquire a wide variety of data. Furthermore, methods for extracting information from the obtained data are rapidly developing. As a result, it is becoming more and more important to find and solve problems using data, and people who can use data effectively are in demand in a variety of fields.
In this class, students learn the basics of data, programming, algorithms, machine learning, and other related topics.
Purpose of class
The purpose of this class is to understand the fundamentals of data so that it can be handled correctly.
Goals and objectives
  1. To be able to understand how data science is used.
  2. To be able to understand the basic algorithmic and programming issues necessary to work with data.
  3. To be able to read, handle, and explain data.
  4. To be able to perform data handling.
  5. To be able to understand the basics of machine learning.
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. Introduction to data science Read through the handouts before class. Review after class. 45minutes
2. Programming Basics 1 Read through the handouts before class. Review after class. 45minutes
3. Programming Basics 2 Read through the handouts before class. Review after class. 45minutes
4. Algorithm Basics Read through the handouts before class. Review after class. 45minutes
5. Data Basics Read through the handouts before class. Review after class. 45minutes
6. Reading, handling, and explaining data (including ethics) Read through the handouts before class. Review after class. 45minutes
7. Text analysis, time series data analysis, image analysis 1 Read through the handouts before class. Review after class. 45minutes
8. Text analysis, time series data analysis, image analysis 2 Read through the handouts before class. Review after class. 45minutes
9. Data handling 1 Read through the handouts before class. Review after class. 45minutes
10. Data handling 2 Read through the handouts before class. Review after class. 45minutes
11. Machine Learning 1: Regression Analysis Read through the handouts before class. Review after class. 45minutes
12. Machine Learning 2: Decision Trees Read through the handouts before class. Review after class. 45minutes
13. Machine Learning 3: Principal Component Analysis Read through the handouts before class. Review after class. 45minutes
14. Machine Learning 4: Cluster Analysis Read through the handouts before class. Review after class. 45minutes
Total. - - 630minutes
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 Reports Total.
1. 8% 8%
2. 31% 31%
3. 15% 15%
4. 15% 15%
5. 31% 31%
Total. 100% -
Evaluation method and criteria
Reports(100%)
60% if students can understand and solve exercises in the handouts
Textbooks and reference materials
Handouts are provided
Prerequisites
Find out how the data is used
Office hours and How to contact professors for questions
  • 30 minutes after class
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
More than one class is interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A 該当しない
Education related SDGs:the Sustainable Development Goals
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
Last modified : Wed Mar 23 04:30:33 JST 2022