Hiroshima University Syllabus

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Japanese
Academic Year 2022Year School/Graduate School School of Informatics and Data Science
Lecture Code KA203001 Subject Classification Specialized Education
Subject Name 情報データ科学演習III
Subject Name
(Katakana)
ジョウホウデータカガクエンシュウ3
Subject Name in
English
Informatics and data science, Exercise III
Instructor HIGAKI TORU,NAKASHIMA KENICHIRO,KOIKE MAYU,FURUKAWA YOSHIYA
Instructor
(Katakana)
ヒガキ トオル,ナカシマ ケンイチロウ,コイケ マユ,フルカワ ヨシヤ
Campus Higashi-Hiroshima Semester/Term 3rd-Year,  Second Semester,  3Term
Days, Periods, and Classrooms (3T) Mon5-7:EDU K104,EDU K203,ENG 116
Lesson Style Seminar Lesson Style
(More Details)
 
Exercise 
Credits 1.0 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 College of Information Science, Information Science Department, 3rd grade
Keywords Image processing, generalized linear model, multivariate analysis, questionnaire survey 
Special Subject for Teacher Education   Special Subject  
Class Status
within Educational
Program
・ Prerequisite subjects for "Image processing":
Image processing
Programming I, II, III, IV
Algorithms and data structures
Seminar on Information Data Science I "Data Structure and Algorithm"

・ Prerequisite subjects for "Survey Data Analysis 2":
Seminar on Information Data Science II "Survey Data Analysis 1"
Behaviormetrics
Linear model
Generalized linear model (GLM)
Multivariate analysis 
Criterion referenced
Evaluation
Informatics and Data Science Program
(Knowledge and Understanding)
・D1. Knowledge and skills required for understanding the theoretical system of statistics and data analysis, and for precisely and efficiently analyzing qualitative/quantitative information in big data.

(Abilities and Skills)
・A. Skills related to the development of an information infrastructure,information processing techniques, and technology for producing new added value through data analysis.

・ B. Ability to identify and solve new problems on their own by quantitative and logical thinking based on data, diverse perspectives, and advanced skills for information processing and analysis.
 
Class Objectives
/Class Outline
In this class, students engage in exercises on specialized and practical content based on the knowledge that has been widely learned in previous lectures of the Faculty of Information Science. Students learn the ability to find solutions for given exercises and problems, to deal with them, and to summarize the results as reports in this class. Exercises will be conducted on the theme of "image processing" and "survey data analysis 2". All students engage in both themes through this class.  

In "Image Processing", students will be needed to understand the basic algorithms for processing a large number of images by programming with Python. “Survey data analysis 2” is based on psychology and behaviormetrics. Students learn analysis such as generalized linear model and multivariate analysis, using the data collected in the theme survey data analysis 1 of Information and Data Science Exercise II. 
Class Schedule 1st
guidance for the two themes, distribution for materials and notes.
2nd
Prepare for the themes.
3rd
Theme 1-1
4th
Theme 1-2
5th
Theme 1-3
6th
Theme 1-4
7th
Theme 1-5
8th
Theme 1-6
9th
Theme 2-1
10th
Theme 2-2
11th
Theme 2-3
12th
Theme 2-4
13th
Theme 2-5
14th
Theme 2-6
15th
Final exam


-

Assign report tasks for each theme.
Conduct the final examination.

Theme "Image processing"
1,2: Create a program for using Python, reading, displaying, and drawing images.
3,4: Create a program for typical conversion of luminance and threshold processing of luminance value.
5,6: Create a representative filter processing program and process a large amount of image big data.

Theme "Survey Data Analysis 2"
1,2: Preparation for analysis plan from perspective of psychology and behaviormetrics.
3,4: Data processing.
5,6: Data analysis and discussion using generalized linear model. 
Text/Reference
Books,etc.
Distribute materials 
PC or AV used in
Class,etc.
 
(More Details) Distribute materials
Use your personal computer. Bring it with you every time. Be fully charged. 
Learning techniques to be incorporated  
Suggestions on
Preparation and
Review
Review the knowledge that has been widely learned in previous lectures of the Faculty of Information Science. 
Requirements Submit report assignments for all the themes by the deadline. 
Grading Method Comprehensively evaluate reports and regular examinations 
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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