查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

110-2 295660 資料科學原理與應用 1 3.0 3 ★ 劉傳銘資工四 7 0 資工四、資工所和電資國際班合開

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
110-2	295660	資料科學原理與應用	1	3.0	3	★	劉傳銘	資工四	7	0	資工四、資工所和電資國際班合開

教學大綱與進度：

教師姓名劉傳銘

Email cmliu@ntut.edu.tw

最後更新時間 2022-02-14 17:31:19

課程大綱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.

課程進度 -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

評量方式與標準 Class Participation: 5% Homework/Report: 25％ Project: 70% (Proposal 20% ; Final Demo 30%; Report 20%) For any inquiry, please use the following contact information: Emails: cmliu@ntut.edu.tw( school); 11107@cc.ntut.edu.tw (Microsoft Teams) Line ID: mobile.prof FB: facebook.com/howard.liu.5 Phone: 4251 (in school)

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：是
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.)

課程諮詢管道

備註
In case of the need for distance learning, the on-line teaching will be given during the official class hours using Microsoft Teams (URL: https://shorturl.at/kDEIT).

教師姓名	劉傳銘
Email	cmliu@ntut.edu.tw
最後更新時間	2022-02-14 17:31:19
課程大綱	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.
課程進度	-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
評量方式與標準	Class Participation: 5% Homework/Report: 25％ Project: 70% (Proposal 20% ; Final Demo 30%; Report 20%) For any inquiry, please use the following contact information: Emails: cmliu@ntut.edu.tw( school); 11107@cc.ntut.edu.tw (Microsoft Teams) Line ID: mobile.prof FB: facebook.com/howard.liu.5 Phone: 4251 (in school)
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：是 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.)
課程諮詢管道
備註	In case of the need for distance learning, the on-line teaching will be given during the official class hours using Microsoft Teams (URL: https://shorturl.at/kDEIT).