Hiroshima University Syllabus

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Academic Year 2022Year School/Graduate School School of Informatics and Data Science
Lecture Code KA204001 Subject Classification Specialized Education
Subject Name 情報データ科学演習IV
Subject Name
Subject Name in
Informatics and data science, Exercise IV
コンドウ トオル,ムラサワ マサタカ
Campus Higashi-Hiroshima Semester/Term 3rd-Year,  Second Semester,  4Term
Days, Periods, and Classrooms (4T) Fri5-7:ENG 111
Lesson Style Seminar Lesson Style
(More Details)
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
Keywords Programing, IoT, Data analysis  
Special Subject for Teacher Education   Special Subject  
Class Status
within Educational
Criterion referenced
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
This exercise will includes two themes based on the knowledge learned through Informatics and data science, Exercises I-III.  The goal is to acquire the ability to find a solution to a given exercise or problem, address it, and report the results. 
Class Schedule lesson1   Guidance

lesson2 to lesson7
 Hands on exercises on two different topics, three weeks each.

  (Topic1) IoT exercise by the Raspberry Pi
    1. Setup Raspberry Pi and learning the basic usage of Raspberry Pi.
  (Topic1) IoT exercise by the Raspberry Pi
    2. Visualize sensor data through a web server.
  (Topic1) IoT exercise by the Raspberry Pi
    3. Control embedded system using GPIO interface

  (Topic2) Data analysis in social science
    1. Learn about the current status and issues of available data for social science
  (Topic2) Data analysis in social science
    2. Understanding variables of interest in social science
  (Topic2) Data analysis in social science
    3. Data analysis using "R"

lesson 8  Examination 
PC or AV used in
(More Details) handouts, PC (including BYOD) 
Learning techniques to be incorporated  
Suggestions on
Preparation and
  (Topic1) IoT exercise by the Raspberry Pi
    You can use Raspberry Pi only during exercises, so it is difficult to prepare and learn by yourself. Handouts will be provided in advance, so read carefully before class and prepare for the day of the exercise.

  (Topic2) Data analysis in social science
    You will experience actual data analysis using open source software "R" and related libraries, so you have to install these in advance. Details will be given in the 1st lesson. 
Grading Method Evaluated by reports and examinations.  
Practical Experience  
Summary of Practical Experience and Class Contents based on it  
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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