Course Syllabus

Course Information：

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

Fall Semester
Year 2020 276516 Introduction to Data Science 1 3.0 3 ★ CHUAN-MING LIU IEECSU2 15 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2020	276516	Introduction to Data Science	1	3.0	3	★	CHUAN-MING LIU	IEECSU2	15	0

Course Plan：

Instructor CHUAN-MING LIU

Email cmliu@ntut.edu.tw

Last Updated 2020-06-08 14:31:48

Course Objective Data Science is an emerging multi-discipline field which is the intersection of computer science, statistics, and mathematics. This course is designed to provide a hands-on introduction to Data Science by challenging students to build predictive models. In this course, we will discuss some fundamental components for efficiently and effectively handling the data as well as presenting data.

Course Schedule
(Week 1-16) -An Overview (3 hrs) -Mathematical Preliminaries (6 hrs) -Data Munging (3 hrs) -Scores and Rankings (3 hrs) -Introduction to Statistical Analysis (6 hrs) -Visualizing Data (3 hrs) -Mathematical Models (3 hrs) Midterm Exam (3 hrs) -Fundamental Linear Algebra (6 hrs) -Linear and Logistic Regression (6 hrs) -Distance and Network Methods (3 hrs) -Case Study: Practical Applications (3 hrs) -Project Demo (6 hrs)

Evaluation and grading policy Class Participation: 5% Homework/Report: 15％ Midterm Exam: 20% Project: 60% (Proposal 20% ; Final Demo 20%; Report 20%)

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Steven S. Skiena, The Data Science Design Manual, Springer-Verlag, 2017, ISBN-13: 978-3319554433 References: - Cathy O'Neil and Rachel Schutt. Doing Data Science, Straight Talk From The Frontline. O'Reilly. 2014. - Jure Leskovek, Anand Rajaraman and Jerey Ullman. Mining of Massive Datasets. v2.1, Cambridge University Press. 2014. (free online) - Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. ISBN 0262018020. 2013. - Foster Provost and Tom Fawcett. Data Science for Business: What You Need to Know about Data Mining and Data-analytic Thinking. ISBN 1449361323. 2013. - Avrim Blum, John Hopcroft and Ravindran Kannan. Foundations of Data Science. (Note: this is a book currently being written by the three authors. The authors have made the first draft of their notes for the book available online. The material is intended for a modern theoretical course in computer science.)

The access to curricular consultation

Expanding teaching and resources

The course corresponds to the SDGs

Does the course incorporate AI

Note
　

Instructor	CHUAN-MING LIU
Email	cmliu@ntut.edu.tw
Last Updated	2020-06-08 14:31:48
Course Objective	Data Science is an emerging multi-discipline field which is the intersection of computer science, statistics, and mathematics. This course is designed to provide a hands-on introduction to Data Science by challenging students to build predictive models. In this course, we will discuss some fundamental components for efficiently and effectively handling the data as well as presenting data.
Course Schedule (Week 1-16)	-An Overview (3 hrs) -Mathematical Preliminaries (6 hrs) -Data Munging (3 hrs) -Scores and Rankings (3 hrs) -Introduction to Statistical Analysis (6 hrs) -Visualizing Data (3 hrs) -Mathematical Models (3 hrs) Midterm Exam (3 hrs) -Fundamental Linear Algebra (6 hrs) -Linear and Logistic Regression (6 hrs) -Distance and Network Methods (3 hrs) -Case Study: Practical Applications (3 hrs) -Project Demo (6 hrs)
Evaluation and grading policy	Class Participation: 5% Homework/Report: 15％ Midterm Exam: 20% Project: 60% (Proposal 20% ; Final Demo 20%; Report 20%)
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Steven S. Skiena, The Data Science Design Manual, Springer-Verlag, 2017, ISBN-13: 978-3319554433 References: - Cathy O'Neil and Rachel Schutt. Doing Data Science, Straight Talk From The Frontline. O'Reilly. 2014. - Jure Leskovek, Anand Rajaraman and Jerey Ullman. Mining of Massive Datasets. v2.1, Cambridge University Press. 2014. (free online) - Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. ISBN 0262018020. 2013. - Foster Provost and Tom Fawcett. Data Science for Business: What You Need to Know about Data Mining and Data-analytic Thinking. ISBN 1449361323. 2013. - Avrim Blum, John Hopcroft and Ravindran Kannan. Foundations of Data Science. (Note: this is a book currently being written by the three authors. The authors have made the first draft of their notes for the book available online. The material is intended for a modern theoretical course in computer science.)
The access to curricular consultation
Expanding teaching and resources
The course corresponds to the SDGs
Does the course incorporate AI
Note