Course Syllabus

Course Information：

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

Spring Semester
Year 2021 287174 Principles and Applications of Data Science 1 3.0 3 ★ CHUAN-MING LIU GCSIE
IEECSG 77 1 ◎

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2021	287174	Principles and Applications of Data Science	1	3.0	3	★	CHUAN-MING LIU	GCSIE IEECSG	77	1	◎

Course Plan：

Instructor CHUAN-MING LIU

Email cmliu@ntut.edu.tw

Last Updated 2020-12-14 20:55:06

Course Objective 資料科學探索如何從大量、多元、非結構化的資料中發掘有價值的資訊，並有效提供相關的應用策略，探討的層面包括：資料處理及計算、資料儲存、數理統計及資料分析技術與應用等。本課程討論此一領域的進展，並使學生了解相關知識與技術工具，進而具備資料科學思維及應用能力。課程內容以廣度為主，主題包含資料管理、資料分析、資料視覺化、機器學習、深度學習，巨量資料等。 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.

Course Schedule -An Overview -Mathematical Preliminaries -Data Munging -Scores and Rankings -Statistical Analysis -Visualizing Data -Mathematical Models -Linear Algebra -Linear and Logistic Regression -Distance and Network Methods -Machine Learning -Case Study: Practical Applications -Project Demo

Evaluation and grading policy Class Participation: 5% Homework/Report: 25％ Project: 70% (Proposal 20% ; Final Demo 30%; 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

Note
　

Instructor	CHUAN-MING LIU
Email	cmliu@ntut.edu.tw
Last Updated	2020-12-14 20:55:06
Course Objective	資料科學探索如何從大量、多元、非結構化的資料中發掘有價值的資訊，並有效提供相關的應用策略，探討的層面包括：資料處理及計算、資料儲存、數理統計及資料分析技術與應用等。本課程討論此一領域的進展，並使學生了解相關知識與技術工具，進而具備資料科學思維及應用能力。課程內容以廣度為主，主題包含資料管理、資料分析、資料視覺化、機器學習、深度學習，巨量資料等。 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.
Course Schedule	-An Overview -Mathematical Preliminaries -Data Munging -Scores and Rankings -Statistical Analysis -Visualizing Data -Mathematical Models -Linear Algebra -Linear and Logistic Regression -Distance and Network Methods -Machine Learning -Case Study: Practical Applications -Project Demo
Evaluation and grading policy	Class Participation: 5% Homework/Report: 25％ Project: 70% (Proposal 20% ; Final Demo 30%; 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
Note