Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2025 347204 Data Analysis and Graphics 1 3.0 3 ★ You-Jin Park 4IEM4A
4IEM4B 17 0 工管四甲乙合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2025	347204	Data Analysis and Graphics	1	3.0	3	★	You-Jin Park	4IEM4A 4IEM4B	17	0	工管四甲乙合開

Course Plan：

Instructor You-Jin Park　Office Hours

Email eugenepark993@gmail.com

Last Updated 2025-05-28 10:27:16

Course Objective This course is an introduction to statistical computing and will be taught in Python. Python is widely used not only in the engineering but also in natural science, medicine, biology, physics, and business. This course will introduce you to a vibrant and growing community of open source data analysts that is pushing the state of the art in data analysis and presentation. (Another benefit to you as students is that open source software is free!) This course’s main objective is to: learn how to take raw data, explore it, and present the results in a useful way. Thus, in this course, you will learn all of the basic skills needed to do each of these steps with the statistical language Python.

Course Schedule Week 1-2 Introduction to 'Data analysis and Graphics’ and Python Week 3 Operations in Python Week 4-5 Vector and matrix, tabulation and graphics Week 6-7 Probability distributions Week 8-9 Basic statistics Week 10-11 Hypothesis tests Week 12 ANOVA Week 13-14 Simple linear regression Week 15-16 Multiple linear regressio Week 17 Presentation I Week 18 Presentation II

Evaluation and grading policy Exam or assignment or presentation: 90 % Attendance: 10 %

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Textbook: (1) Python Data Science Handbook by Jake VanderPlas, (2) Statistics and Machine Learning in Python by Edouard Duchesnay, (3) An Introduction to Statistics with Python: With Applications in the Life Sciences by Thomas Haslwanter Supplementary references: (1) Engineering Statistics (5th Ed., Wiley) by Montgomery, Runger, and Hubele (ISBN: 978-0-324-59433-1), (2) Data analysis and decision making (4th Ed.) by Albright, Winston, and Zappe

The access to curricular consultation Email or i-school system.
Office hour: Thursday 11:10 ~ 12:00

The course corresponds to the SDGs SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
SDG11：永續城市與社區（Sustainable Cities and Communities）

Does the course incorporate AI AI2：鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）

Note
The course link will be sent by the school E-mail.

Instructor	You-Jin Park　Office Hours
Email	eugenepark993@gmail.com
Last Updated	2025-05-28 10:27:16
Course Objective	This course is an introduction to statistical computing and will be taught in Python. Python is widely used not only in the engineering but also in natural science, medicine, biology, physics, and business. This course will introduce you to a vibrant and growing community of open source data analysts that is pushing the state of the art in data analysis and presentation. (Another benefit to you as students is that open source software is free!) This course’s main objective is to: learn how to take raw data, explore it, and present the results in a useful way. Thus, in this course, you will learn all of the basic skills needed to do each of these steps with the statistical language Python.
Course Schedule	Week 1-2 Introduction to 'Data analysis and Graphics’ and Python Week 3 Operations in Python Week 4-5 Vector and matrix, tabulation and graphics Week 6-7 Probability distributions Week 8-9 Basic statistics Week 10-11 Hypothesis tests Week 12 ANOVA Week 13-14 Simple linear regression Week 15-16 Multiple linear regressio Week 17 Presentation I Week 18 Presentation II
Evaluation and grading policy	Exam or assignment or presentation: 90 % Attendance: 10 %
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Textbook: (1) Python Data Science Handbook by Jake VanderPlas, (2) Statistics and Machine Learning in Python by Edouard Duchesnay, (3) An Introduction to Statistics with Python: With Applications in the Life Sciences by Thomas Haslwanter Supplementary references: (1) Engineering Statistics (5th Ed., Wiley) by Montgomery, Runger, and Hubele (ISBN: 978-0-324-59433-1), (2) Data analysis and decision making (4th Ed.) by Albright, Winston, and Zappe
The access to curricular consultation	Email or i-school system. Office hour: Thursday 11:10 ~ 12:00
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG11：永續城市與社區（Sustainable Cities and Communities）
Does the course incorporate AI	AI2：鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
Note	The course link will be sent by the school E-mail.