KA237002 Informatics and Data Science Exercise III(Data Science Program)

Hiroshima University Syllabus

Japanese

Academic Year 2024Year 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（データカガクプログラム）

Subject Name in
English Informatics and Data Science Exercise III(Data Science Program)

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) 　

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 　 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, creating a graph from data
lesson4
Normal distribution, regression analysis
lesson5
Assignments and reports for Data Analysis
lesson6
About machine learning
lesson7
Preparation for machine learning
lesson8
Linear regression
lesson9
Logistic regression
lesson10
SVM
lesson11
Decision tree
lesson12
Random forest
lesson13
k-NN
lesson14
k-means
lesson15
Assignments and reports for Machine Learning

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. 　

(More Details)

Learning techniques to be incorporated 　

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	2024Year	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（データカガクプログラム）
Subject Name in English	Informatics and Data Science Exercise III(Data Science Program)
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)
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		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, creating a graph from data lesson4 Normal distribution, regression analysis lesson5 Assignments and reports for Data Analysis lesson6 About machine learning lesson7 Preparation for machine learning lesson8 Linear regression lesson9 Logistic regression lesson10 SVM lesson11 Decision tree lesson12 Random forest lesson13 k-NN lesson14 k-means lesson15 Assignments and reports for Machine Learning 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.
(More Details)
Learning techniques to be incorporated
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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