K5116010 Data Processing and Numerical Analysis

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School School of Engineering

Lecture Code K5116010 Subject Classification Specialized Education

Subject Name データ処理および数値解析

Subject Name
（Katakana）データショリオヨビスウチカイセキ

Subject Name in
English Data Processing and Numerical Analysis

Instructor SUGIO KENJIRO

Instructor
(Katakana) スギオ　ケンジロウ

Campus Higashi-Hiroshima Semester/Term 2nd-Year, Second Semester, 4Term

Days, Periods, and Classrooms (4T) Mon5-6,Weds5-6：ENG 220

Lesson Style Lecture Lesson Style
(More Details) 　

Lecture and Exercise
Exercise using your own laptop PC is planned for each lecture.

Credits 2.0 Class Hours/Week 　 Language of Instruction B : Japanese／English

Course Level 2 : Undergraduate Low-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 10 : Integrated Engineering

Eligible Students 2nd grade students and others

Keywords Programming language, Data structure, Data analysis, Visualization, Image Processing

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) Acquiring the basic knowledge and techniques of computers, data processing, and programming language required for experiments or numerical simulation in science and technology.
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Program of Mechanical Systems Engineering
（Abilities and Skills）
・Acquring basis of mechanical system engineering steadily and developing the applied skill.

Program of Material Processing
（Abilities and Skills）
・Acquiring basis of mechanical system, material creation and processing engineering steadily, and being able to apply

Program of Energy Transform Engineering
（Abilities and Skills）
・Acquring basis of mechanical system engineering steadily and developing the applied skill.

Class Objectives
/Class Outline (1) Understanding the importance of the data structure by learning Python programming
(2) Making the foundation of computer use for science and technology by learning the data analysis using Python
(3) Learning the way of modern programming by making use of various libraries for Python.

Class Schedule lesson1 Introduction of the class, Environmental construction, Python basics (1)
lesson2 Python basics (2)
lesson3 Python basics (3)
lesson4 Python basics (4)
lesson5 NumPy basics (1)
lesson6 NumPy basics (2)
lesson7 Data operation using pandas (1)
lesson8 Data operation using pandas (2)
lesson9 Data operation using pandas (3)
lesson10 Visualization using Matplotlib (1)
lesson11 Visualization using Matplotlib (2)
lesson12 Image processing using OpneCV (1)
lesson13 Image processing using OpneCV (2)
lesson14 Machine learning usng scikit-learn (1)
lesson15 Machine learning using scikit-learn (2)

The end-term examination is given.

Text/Reference
Books,etc. Using your own laptop PC from the first lecture is planned.
Lecture materials will be distributed at Bb9.

references：
Bill Lubanovic "Introducing Python" O'REILLY [Both Japanese version and English version available]
Jake VanderPlas "Python Data Science Handbook" O'REILLY [Both Japanese version and English version available]

PC or AV used in
Class,etc. 　

(More Details) your own laptop PC

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review Programming tasks will be imposed every lecture.

Before lesson 1: Connecting your PC to the Wifi in the classroom
lesson 1-4 : Programming with Python
lesson 5-6 : Programming with NumPy
lesson 7-9 : Programming with pandas
lesson 10-11 : Programming with Matplotlib
lesson 12-13 : Programming with OpenCV
lesson 14-15 : Programming with scikit-learn

Requirements

Grading Method The end-term examination(~50%) and learning attitude(~50%)
60% or more in total is required.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message In principle, we won't accept your question for your own PC such as network troubles during the lecture.

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 Engineering
Lecture Code	K5116010	Subject Classification	Specialized Education
Subject Name	データ処理および数値解析
Subject Name （Katakana）	データショリオヨビスウチカイセキ
Subject Name in English	Data Processing and Numerical Analysis
Instructor	SUGIO KENJIRO
Instructor (Katakana)	スギオ　ケンジロウ
Campus	Higashi-Hiroshima	Semester/Term	2nd-Year, Second Semester, 4Term
Days, Periods, and Classrooms	(4T) Mon5-6,Weds5-6：ENG 220
Lesson Style	Lecture	Lesson Style (More Details)
Lecture and Exercise Exercise using your own laptop PC is planned for each lecture.
Credits	2.0	Class Hours/Week		Language of Instruction	B : Japanese／English
Course Level	2 : Undergraduate Low-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	10 : Integrated Engineering
Eligible Students	2nd grade students and others
Keywords	Programming language, Data structure, Data analysis, Visualization, Image Processing
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)	Acquiring the basic knowledge and techniques of computers, data processing, and programming language required for experiments or numerical simulation in science and technology.
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Program of Mechanical Systems Engineering （Abilities and Skills）・Acquring basis of mechanical system engineering steadily and developing the applied skill. Program of Material Processing （Abilities and Skills）・Acquiring basis of mechanical system, material creation and processing engineering steadily, and being able to apply Program of Energy Transform Engineering （Abilities and Skills）・Acquring basis of mechanical system engineering steadily and developing the applied skill.
Class Objectives /Class Outline	(1) Understanding the importance of the data structure by learning Python programming (2) Making the foundation of computer use for science and technology by learning the data analysis using Python (3) Learning the way of modern programming by making use of various libraries for Python.
Class Schedule	lesson1 Introduction of the class, Environmental construction, Python basics (1) lesson2 Python basics (2) lesson3 Python basics (3) lesson4 Python basics (4) lesson5 NumPy basics (1) lesson6 NumPy basics (2) lesson7 Data operation using pandas (1) lesson8 Data operation using pandas (2) lesson9 Data operation using pandas (3) lesson10 Visualization using Matplotlib (1) lesson11 Visualization using Matplotlib (2) lesson12 Image processing using OpneCV (1) lesson13 Image processing using OpneCV (2) lesson14 Machine learning usng scikit-learn (1) lesson15 Machine learning using scikit-learn (2) The end-term examination is given.
Text/Reference Books,etc.	Using your own laptop PC from the first lecture is planned. Lecture materials will be distributed at Bb9. references： Bill Lubanovic "Introducing Python" O'REILLY [Both Japanese version and English version available] Jake VanderPlas "Python Data Science Handbook" O'REILLY [Both Japanese version and English version available]
PC or AV used in Class,etc.
(More Details)	your own laptop PC
Learning techniques to be incorporated
Suggestions on Preparation and Review	Programming tasks will be imposed every lecture. Before lesson 1: Connecting your PC to the Wifi in the classroom lesson 1-4 : Programming with Python lesson 5-6 : Programming with NumPy lesson 7-9 : Programming with pandas lesson 10-11 : Programming with Matplotlib lesson 12-13 : Programming with OpenCV lesson 14-15 : Programming with scikit-learn
Requirements
Grading Method	The end-term examination(~50%) and learning attitude(~50%) 60% or more in total is required.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	In principle, we won't accept your question for your own PC such as network troubles during the lecture.
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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