Hiroshima University Syllabus

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Japanese
Academic Year 2022Year School/Graduate School School of Informatics and Data Science
Lecture Code KA227001 Subject Classification Specialized Education
Subject Name 社会とデータ解析
Subject Name
(Katakana)
シャカイトデータカイセキ
Subject Name in
English
Society and Data Analysis
Instructor FUKUI KEISUKE
Instructor
(Katakana)
フクイ ケイスケ
Campus Higashi-Hiroshima Semester/Term 3rd-Year,  Second Semester,  Intensive
Days, Periods, and Classrooms (Int) Inte
Lesson Style Lecture Lesson Style
(More Details)
 
 
Credits 2.0 Class Hours/Week   Language of Instruction B : Japanese/English
Course Level 3 : Undergraduate High-Intermediate
Course Area(Area) 25 : Science and Technology
Course Area(Discipline) 02 : Information Science
Eligible Students
Keywords Effect verification, Causal Inference, Data analysis, R 
Special Subject for Teacher Education   Special Subject  
Class Status
within Educational
Program
 
Criterion referenced
Evaluation
Informatics and Data Science Program
(Comprehensive Abilities)
・D3. Ability to examine social needs and issues which are interlinked in a complex manner, using a top-down view to solve the problems through quantitative and logical thinking based on data, diverse perspectives, and advanced skills in information processing and analysis.
 
Class Objectives
/Class Outline
1. To understand the basics of causal inference for effect verification
2. To analysis using actual data. 
Class Schedule lesson1 Class guidance, Installation of R and Rstudio
lesson2 Fundamentals of R 1
lesson3 Fundamentals of R 2
lesson4 Data handling and descriptive statistics
lesson5 Data visualization and descriptive statistics
lesson6 Importance of comparison for the effect verification
lesson7 Basics of inferential statistics and RCTs for the effect verification
lesson8 Basics of regression analysis and application to effect verification 1
lesson9 Basics of regression analysis and application to effect verification 2
lesson10 Estimating the causal effect using propensity score
lesson11 Actual effect verification based on actual data
lesson12 DID and its application
lesson13 CausalImpact and its application
lesson14 RDD and its application
lesson15 Review

Report 
Text/Reference
Books,etc.
Materials will be distributed.  
PC or AV used in
Class,etc.
 
(More Details) R, Studio, PC 
Learning techniques to be incorporated  
Suggestions on
Preparation and
Review
The students are expected to take any statistics subjects. 
Requirements  
Grading Method Grading will be decided based on attendance, report and a fraction of in-class contribution. 
Practical Experience  
Summary of Practical Experience and Class Contents based on it  
Message  
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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