Course Syllabus

Course Information：

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

Spring Semester
Year 2024 331657 Statistical Data Analytics 1 3.0 3 ★ You-Jin Park 4IEM3C 1 0 工管三甲乙丙合開丙10人

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2024	331657	Statistical Data Analytics	1	3.0	3	★	You-Jin Park	4IEM3C	1	0	工管三甲乙丙合開丙10人

Course Plan：

Instructor You-Jin Park　Office Hours

Email eugenepark993@gmail.com

Last Updated 2023-12-26 20:30:38

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
(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

Note
Email and MS Teams

Instructor	You-Jin Park　Office Hours
Email	eugenepark993@gmail.com
Last Updated	2023-12-26 20:30:38
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 (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
Note	Email and MS Teams