Numbering Code	U-ECON00 20020 SJ43	Year/Term	2022 ・ First semester
Number of Credits	2	Course Type	Seminar
Target Year	2nd year students	Target Student
Language	Japanese	Day/Period	Wed.4
Instructor name	AKITA YUYA (Graduate School of Economics Professor)
Outline and Purpose of the Course	With the development of information technology, as epitomized by the Internet, we live in an age characterized by the production of massive amounts of data about all kinds of matters. Our challenge is how to make best use of this data. In this seminar, we shall be learning about information processing techniques such as pattern recognition and data mining, with the aim of becoming capable of applying this knowledge to actual data.
Course Goals	The aim of this seminar is for students to secure foundational knowledge regarding the field known as data science, as well as the skills required to deploy this knowledge in independently analyzing and interpreting data using a computer.
Schedule and Contents	We shall be studying the major systems used for conducting statistical analysis and pattern recognition through exercises using the statistical software R, and classroom learning that focuses on explaining the mathematical background behind these systems. Although the difficulty of the class contents could be adjusted depending on the proficiency level of the students; generally speaking, the items below will be studied throughout weeks 1-3. In the final 2-3 weeks of the first and second semester, respectively, students will be utilizing the methods they have learned to conduct analyses of concrete problems, and they will subsequently share their results in a presentation. 1. The foundations of R 2. Principle component analysis 3. Factor analysis 4. Correspondence analysis 5. Multidimensional scaling 6. Cluster analysis 7. Regression analysis 8. Discriminant analysis 9. Time series model 10. Neural networks, machine learning
Evaluation Methods and Policy	Evaluation will be based on attendance at the seminars (50%) and the contents of presentations made during the seminar (50%). Furthermore, credit for the unit will not be granted to students who miss more than 1 in 3 classes.
Course Requirements	It would be best if students had already completed Introduction to Information Processing, and be adequately proficient in using computers. However, these are not compulsory requirements. It is advisable for students to have taken introductory classes related to mathematics and statistics. Please note that students are required to enroll in both the first and second semesters in order to take this class.
Study outside of Class (preparation and review)	Revision work and homework will be assigned, as necessary.
Textbooks	Textbooks/References	Jin Mingzhe,『Rによるデータサイエンス(第2版) データ解析の基礎から最新手法まで』 (Morikita Shuppan) ISBN:978-4-627-09602-8 2017
References, etc.	No other texts specifically required.