Course Syllabus

Course Information：

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

Spring Semester
Year 2025 343109 Analysis and Decision Making Using Data 1 3.0 3 ★ You-Jin Park IMBA 5 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2025	343109	Analysis and Decision Making Using Data	1	3.0	3	★	You-Jin Park	IMBA	5	0

Course Plan：

Instructor You-Jin Park　Office Hours

Email eugenepark993@gmail.com

Last Updated 2024-12-18 13:40:29

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 1-3 Operations in Python, Vector and matrix, Tabulation and graphics Week 4-6 Introduction to data analysis for decision-making: Random variables, mean, median, standard deviation, correlation, covariance Week 7-8 Hypothesis tests Week 9: Midterm exam (or small presentation) Week 10-12 ANOVA Week 13-15 Simple linear regression Week 16-17 Multiple linear regression Week 18 Final exam (or small presentation)

Evaluation and grading policy 1. Attendance: 10% 2. Assignment: 30 % 3. Exam or presentation: 60%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
(1) Engineering Statistics (5th Ed., Wiley) by Montgomery, Runger, and Hubele (ISBN: 978-0-324-59433-1) (2) Introduction to Python for Econometrics, Statistics and Data Analysis (4th Ed.) by Kevin Sheppard

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

The course corresponds to the SDGs SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

Does the course incorporate AI

Note
E-mail and MS Teams

Instructor	You-Jin Park　Office Hours
Email	eugenepark993@gmail.com
Last Updated	2024-12-18 13:40:29
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 1-3 Operations in Python, Vector and matrix, Tabulation and graphics Week 4-6 Introduction to data analysis for decision-making: Random variables, mean, median, standard deviation, correlation, covariance Week 7-8 Hypothesis tests Week 9: Midterm exam (or small presentation) Week 10-12 ANOVA Week 13-15 Simple linear regression Week 16-17 Multiple linear regression Week 18 Final exam (or small presentation)
Evaluation and grading policy	1. Attendance: 10% 2. Assignment: 30 % 3. Exam or presentation: 60%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes (1) Engineering Statistics (5th Ed., Wiley) by Montgomery, Runger, and Hubele (ISBN: 978-0-324-59433-1) (2) Introduction to Python for Econometrics, Statistics and Data Analysis (4th Ed.) by Kevin Sheppard
The access to curricular consultation	Email or i-school system. Office hour: Thursday 11:00 ~ 12:00
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI
Note	E-mail and MS Teams