KA237002 Informatics and Data Science Exercise III

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School School of Informatics and Data Science

Lecture Code KA237002 Subject Classification Specialized Education

Subject Name 情報科学演習III（データ科学プログラム）

Subject Name
（Katakana）ジョウホウカガクエンシュウ３

Subject Name in
English Informatics and Data Science Exercise III

Instructor ADILIN ANUARDI

Instructor
(Katakana) アディリン　アヌアルディ

Campus Higashi-Hiroshima Semester/Term 3rd-Year, Second Semester, 3Term

Days, Periods, and Classrooms (3T) Mon5-7：IMC-Main 2F Seminar Rm

Lesson Style Seminar Lesson Style
(More Details) Face-to-face, Online (on-demand)

Exercise-centered, face-to-face/non-face-to-face format

In principle, classes will be held in person, but online and on-demand classes using Teams will be offered as needed. Class time will be used mainly for questions and answers in exercises and for supplementing class materials. If online classes (via Teams) are to be held, we will notify you in advance via Teams.

Credits 1.0 Class Hours/Week 3 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 Data analysis, machine learning

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) The objective is to acquire the basic techniques of statistical analysis necessary to analyze various types of data and to acquire and apply basic knowledge of machine learning.
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.
・D3. Knowledge of hardware and software and programming ability to process data efficiently.

Data Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently.
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Intelligence Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.
（Comprehensive Abilities）
・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science.

Class Objectives
/Class Outline Based on the knowledge acquired in the past lectures, students will practice specialized and practical content. Students will learn the ability to find the solutions to given exercises and problems and to report the results. Specifically, students will learn "Data Analysis" and "Machine Learning" contents by programming in Python.

In "Data Analysis," students will pre-process a large amount of collected data and understand basic algorithms for data trends and statistical analysis. In "Machine Learning," students understand the basic algorithms of various machine learning models and let computers learn data and make predictions.

Class Schedule lesson1
Guidance
lesson2
Using Python
lesson3
Data pre-processing
lesson4
Creating a graph from data
lesson5
Normal distribution
lesson6
Regression analysis
lesson7
Assignments and reports for Data Analysis
lesson8
About machine learning
lesson9
Preparation for machine learning
lesson10
Linear regression, logistic regression, SVM
lesson11
Decision tree, random forest
lesson12
k-NN, k-means
lesson13
Predicting numbers from images
lesson14
Assignments and reports for Machine Learning
lesson15
Final assignments

Any changes to the class content will be informed through Teams in advance.

Text/Reference
Books,etc. Class materials will be distributed.

PC or AV used in
Class,etc. Handouts, Microsoft Teams, Microsoft Forms, moodle

(More Details)

Learning techniques to be incorporated Post-class Report

Suggestions on
Preparation and
Review Before each lecture, review the content of the previous class.

Requirements

Grading Method An assignment will be given at each lesson and a report will be assigned for each content.
These will be evaluated as a whole.

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.

Academic Year	2025Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA237002	Subject Classification	Specialized Education
Subject Name	情報科学演習III（データ科学プログラム）
Subject Name （Katakana）	ジョウホウカガクエンシュウ３
Subject Name in English	Informatics and Data Science Exercise III
Instructor	ADILIN ANUARDI
Instructor (Katakana)	アディリン　アヌアルディ
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, Second Semester, 3Term
Days, Periods, and Classrooms	(3T) Mon5-7：IMC-Main 2F Seminar Rm
Lesson Style	Seminar	Lesson Style (More Details)	Face-to-face, Online (on-demand)
Exercise-centered, face-to-face/non-face-to-face format In principle, classes will be held in person, but online and on-demand classes using Teams will be offered as needed. Class time will be used mainly for questions and answers in exercises and for supplementing class materials. If online classes (via Teams) are to be held, we will notify you in advance via Teams.
Credits	1.0	Class Hours/Week	3	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	Data analysis, machine learning
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)	The objective is to acquire the basic techniques of statistical analysis necessary to analyze various types of data and to acquire and apply basic knowledge of machine learning.
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Computer Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. ・D3. Knowledge of hardware and software and programming ability to process data efficiently. Data Science Program （Knowledge and Understanding）・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently. （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. Intelligence Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. （Comprehensive Abilities）・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science.
Class Objectives /Class Outline	Based on the knowledge acquired in the past lectures, students will practice specialized and practical content. Students will learn the ability to find the solutions to given exercises and problems and to report the results. Specifically, students will learn "Data Analysis" and "Machine Learning" contents by programming in Python. In "Data Analysis," students will pre-process a large amount of collected data and understand basic algorithms for data trends and statistical analysis. In "Machine Learning," students understand the basic algorithms of various machine learning models and let computers learn data and make predictions.
Class Schedule	lesson1 Guidance lesson2 Using Python lesson3 Data pre-processing lesson4 Creating a graph from data lesson5 Normal distribution lesson6 Regression analysis lesson7 Assignments and reports for Data Analysis lesson8 About machine learning lesson9 Preparation for machine learning lesson10 Linear regression, logistic regression, SVM lesson11 Decision tree, random forest lesson12 k-NN, k-means lesson13 Predicting numbers from images lesson14 Assignments and reports for Machine Learning lesson15 Final assignments Any changes to the class content will be informed through Teams in advance.
Text/Reference Books,etc.	Class materials will be distributed.
PC or AV used in Class,etc.	Handouts, Microsoft Teams, Microsoft Forms, moodle
(More Details)
Learning techniques to be incorporated	Post-class Report
Suggestions on Preparation and Review	Before each lecture, review the content of the previous class.
Requirements
Grading Method	An assignment will be given at each lesson and a report will be assigned for each content. These will be evaluated as a whole.
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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