WMEC1400 Applied International Public Policy

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School Graduate School of Humanities and Social Sciences (Master's Course) Division of Humanities and Social Sciences Economics Program

Lecture Code WMEC1400 Subject Classification Specialized Education

Subject Name 応用国際公共政策

Subject Name
（Katakana）オウヨウコクサイコウキョウセイサク

Subject Name in
English Applied International Public Policy

Instructor YASUTAKE KOUICHI

Instructor
(Katakana) ヤスタケ　コウイチ

Campus Higashi-Hiroshima Semester/Term 1st-Year,  First Semester,  1Term

Days, Periods, and Classrooms (1T) Weds5-8：ECON B254

Lesson Style Lecture Lesson Style
(More Details) 　

The class will be a mix of lectures, discussions and student presentations.

Credits 2.0 Class Hours/Week 　 Language of Instruction B : Japanese／English

Course Level 5 : Graduate Basic

Course Area（Area） 24 : Social Sciences

Course Area（Discipline） 03 : Economics

Eligible Students

Keywords Data Science, Data Analysis, Point Processes, Machine Learning, Time Series Analysis, Foreign Exchange Rates, Financial Data

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline Students will learn data science-oriented time series analysis using time series data analysis such as foreign exchange rates. Specifically, we will challenge the analysis of large-scale data and predictive analysis using deep learning. No basic knowledge of programming languages is required. You can become a data scientist if you do a good job of reviewing and preparing for each class. We will guide you responsibly.

Class Schedule lesson1 Guidance (e.g., confirmation of each participant's computer environment) and preparation of data analysis environment (Anaconda or Google Colab will be used)
lesson2 What is time series analysis?
lesson3 Basic knowledge and basic practice on deep learning for time series forecasting
lesson4 Baseline and hands-on training for deep learning
lesson5 Implementation and practice of linear models and DNN (Deep Neural Networks)
lesson6  Implementation and hands-on practice of RNN and LSTM architectures
lesson7 CNN (Convolutional Neural Network) implementation and hands-on practice
lesson8 ARLSTM Architecture Implementation and Hands-on Practice
lesson9 Exercise (Forecasting Electricity Consumption in Different Countries)
lesson10 Implementation and exercise of time series forecasting using Prophet
lesson11 Exercises
lesson12 Time series data analysis and forecasting of foreign exchange rates
lesson13 Let's try to test the efficient market hypothesis (EMH)!  Part 1
lesson14 Let's try to test the efficient market hypothesis (EMH)!  Part 2
lesson15 Closing Session of this Course

Text/Reference
Books,etc. Marco Peixeiro, Time-Series Data Forecasting by Python, 2023 (in Japanese)

PC or AV used in
Class,etc. 　

(More Details) Google Colab and/or Anaconda

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review No prior knowledge of programming languages is required, although Python will be used. Even if you are a genuine beginner, if you are motivated and have a dream (such as a dream to become a data scientist), we will guide you responsibly! Please give it your best shot.

Requirements No prior knowledge of programming languages is required, but basic knowledge of computer file management is recommended. If you do not have such knowledge, please prepare and review the material thoroughly.

Grading Method Practical exercises and a final report will be used for grading.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message Again, beginners in programming are welcome. We will take care of you to the end as long as you do your preparation and review, actively ask questions if you don't understand something, and value your own motivation and dreams. We are looking forward to your challenge!

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2024Year	School/Graduate School	Graduate School of Humanities and Social Sciences (Master's Course) Division of Humanities and Social Sciences Economics Program
Lecture Code	WMEC1400	Subject Classification	Specialized Education
Subject Name	応用国際公共政策
Subject Name （Katakana）	オウヨウコクサイコウキョウセイサク
Subject Name in English	Applied International Public Policy
Instructor	YASUTAKE KOUICHI
Instructor (Katakana)	ヤスタケ　コウイチ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, First Semester, 1Term
Days, Periods, and Classrooms	(1T) Weds5-8：ECON B254
Lesson Style	Lecture	Lesson Style (More Details)
The class will be a mix of lectures, discussions and student presentations.
Credits	2.0	Class Hours/Week		Language of Instruction	B : Japanese／English
Course Level	5 : Graduate Basic
Course Area（Area）	24 : Social Sciences
Course Area（Discipline）	03 : Economics
Eligible Students
Keywords	Data Science, Data Analysis, Point Processes, Machine Learning, Time Series Analysis, Foreign Exchange Rates, Financial Data
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	Students will learn data science-oriented time series analysis using time series data analysis such as foreign exchange rates. Specifically, we will challenge the analysis of large-scale data and predictive analysis using deep learning. No basic knowledge of programming languages is required. You can become a data scientist if you do a good job of reviewing and preparing for each class. We will guide you responsibly.
Class Schedule	lesson1 Guidance (e.g., confirmation of each participant's computer environment) and preparation of data analysis environment (Anaconda or Google Colab will be used) lesson2 What is time series analysis? lesson3 Basic knowledge and basic practice on deep learning for time series forecasting lesson4 Baseline and hands-on training for deep learning lesson5 Implementation and practice of linear models and DNN (Deep Neural Networks) lesson6 Implementation and hands-on practice of RNN and LSTM architectures lesson7 CNN (Convolutional Neural Network) implementation and hands-on practice lesson8 ARLSTM Architecture Implementation and Hands-on Practice lesson9 Exercise (Forecasting Electricity Consumption in Different Countries) lesson10 Implementation and exercise of time series forecasting using Prophet lesson11 Exercises lesson12 Time series data analysis and forecasting of foreign exchange rates lesson13 Let's try to test the efficient market hypothesis (EMH)! Part 1 lesson14 Let's try to test the efficient market hypothesis (EMH)! Part 2 lesson15 Closing Session of this Course
Text/Reference Books,etc.	Marco Peixeiro, Time-Series Data Forecasting by Python, 2023 (in Japanese)
PC or AV used in Class,etc.
(More Details)	Google Colab and/or Anaconda
Learning techniques to be incorporated
Suggestions on Preparation and Review	No prior knowledge of programming languages is required, although Python will be used. Even if you are a genuine beginner, if you are motivated and have a dream (such as a dream to become a data scientist), we will guide you responsibly! Please give it your best shot.
Requirements	No prior knowledge of programming languages is required, but basic knowledge of computer file management is recommended. If you do not have such knowledge, please prepare and review the material thoroughly.
Grading Method	Practical exercises and a final report will be used for grading.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	Again, beginners in programming are welcome. We will take care of you to the end as long as you do your preparation and review, actively ask questions if you don't understand something, and value your own motivation and dreams. We are looking forward to your challenge!
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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