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 328788 Data Science and Visualization 1 3.0 3 ★ Li, Gordon Chun Yue GBM 15 1 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2024	328788	Data Science and Visualization	1	3.0	3	★	Li, Gordon Chun Yue	GBM	15	1

Course Plan：

Instructor Li, Gordon Chun Yue　Office Hours

Email G.C.Li@ntut.edu.tw

Last Updated 2024-05-29 12:21:38

Course Objective This course introduces data science and data visualization through the R language. The course assumes no prior experience in R or data programming. Focusing on applied data science skills, students will be introduced to the methods to tidy, explore, transform, and visualize data. Students will practice them with real data in exercises and projects.

Course Schedule Week. Dates: Content I.Data Science 1. Introduction to the course, installation 2. Basic R Commands, packages 3. Data selection, Data formats 4. Workflows, comparisons 5. Data transformations 6. Functions, Iteration II. Visualization 7. Data Preparation (Ch. 1) 8. Introduction to ggplot (Ch. 2) 9. Exam Week 10. Univariate Graphs (Ch. 3), Bivariate Graphs (Ch. 4) 11. Multivariate Graphs (Ch. 5), Maps (Ch. 6) 13. Time Series Graphs (Ch. 7), Statistical Models and Graphs (Ch. 8) III. Practice 13. Project Time 14. Introduction to R libraries and modeling 15. Exam Week Presentation 16. Project Presentation (for those who haven't presented) 17. Project Presentation (for those who haven't presented)

Evaluation and grading policy 32.5% Homework and Quizzes 32.5% Midterm 35.0% Final Project 100% Total

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Textbook: Data Visualization with R, Rob Kabacoff (free) Reference: Hands-On Programming with R, Garrett Grolemund (free) Reference: R in Action: Data Analysis and Graphics with R, Robert Kabacoff, Manning Publications, 2015

The access to curricular consultation Send LINE message or email the instructor.

The course corresponds to the SDGs

Note
https://meet.google.com/xgw-otio-bvi

Instructor	Li, Gordon Chun Yue　Office Hours
Email	G.C.Li@ntut.edu.tw
Last Updated	2024-05-29 12:21:38
Course Objective	This course introduces data science and data visualization through the R language. The course assumes no prior experience in R or data programming. Focusing on applied data science skills, students will be introduced to the methods to tidy, explore, transform, and visualize data. Students will practice them with real data in exercises and projects.
Course Schedule	Week. Dates: Content I.Data Science 1. Introduction to the course, installation 2. Basic R Commands, packages 3. Data selection, Data formats 4. Workflows, comparisons 5. Data transformations 6. Functions, Iteration II. Visualization 7. Data Preparation (Ch. 1) 8. Introduction to ggplot (Ch. 2) 9. Exam Week 10. Univariate Graphs (Ch. 3), Bivariate Graphs (Ch. 4) 11. Multivariate Graphs (Ch. 5), Maps (Ch. 6) 13. Time Series Graphs (Ch. 7), Statistical Models and Graphs (Ch. 8) III. Practice 13. Project Time 14. Introduction to R libraries and modeling 15. Exam Week Presentation 16. Project Presentation (for those who haven't presented) 17. Project Presentation (for those who haven't presented)
Evaluation and grading policy	32.5% Homework and Quizzes 32.5% Midterm 35.0% Final Project 100% Total
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Textbook: Data Visualization with R, Rob Kabacoff (free) Reference: Hands-On Programming with R, Garrett Grolemund (free) Reference: R in Action: Data Analysis and Graphics with R, Robert Kabacoff, Manning Publications, 2015
The access to curricular consultation	Send LINE message or email the instructor.
The course corresponds to the SDGs
Note	https://meet.google.com/xgw-otio-bvi