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

113-1 337594 資料科學 1 3.0 3 ★ 王正豪
邱弘緯 AI雙聯專班 10 0 碩一修，9/15.29；10/6.20.27；11/10

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
113-1	337594	資料科學	1	3.0	3	★	王正豪邱弘緯	AI雙聯專班	10	0	碩一修，9/15.29；10/6.20.27；11/10

教學大綱與進度：

教師姓名王正豪　教師諮商時間(Office Hours)

Email jhwang@ntut.edu.tw

最後更新時間 2024-09-27 11:24:25

課程大綱資料科學為最熱門的職業之一，各行各業對於數據分析的人才需求正在蓬勃發展，本課程在於培育從事資料科學或機器學習的人員來發展相關知識和技能。課程內容以線性代數、機率、統計學等工程數學為理論學習基礎，操作Python作為資料科學的實作工具，包括其相關Python基礎語法、各種資料型態、數據可視化、開源工具等。接著學習利用Python取得數據資料，進而操作各種迴歸分析來手動分析數據資料，並實作梯度遞減，決策樹等最佳化演算方法。最後則是介紹機器學習、深度學習等最新的AI演算方法來分析大數據並輔助決策。 Data Science has been ranked as one of the hottest professions and the demand for data practitioners is booming. This course is designed to develop people engaged in data science or machine learning to develop relevant knowledge and skills. You will understand how data science works based on mathematics, such as linear algebra, probability and statistics. Python is the best tool to learn how to conduct data science analyze data. Learn the basic language skills, common data types, visualization tools and extended modules for data grabbing and regression analysis. Gradient decent and decision tree for optimization will be covered. Finally, AI algorithm, machine learning, and deep learning, are introduced how data help in decision making.

課程進度
(1-16週) 1. Characteristics of data 2. Introduction to Python 3. Basics: probability, statistics, linear algebra 4. Data Preprocessing 5. Frequent Pattern Mining 6. Regression and Prediction 7. Classification 8. Clustering 9. Applications

評量方式與標準 Labs and exercises: 50% Term project presentation and report: 50%

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
1. Python Data Science Handbook: Essential Tools for Working with Data 1st Edition, by Jake VanderPlas, 2017. 2. Data Science from Scratch: First Principles with Python, 2nd edition, by Joel Grus, 2019.

課程諮詢管道 The students can discuss with the teacher and the TA by appointment via e-mail. Other communication channels such as Teams or LINE might be established.

延伸教學與資源

課程對應SDGs指標無（None）

課程是否導入AI

備註 The course sessions will be recorded when possible. Online sessions via Microsoft Teams will be scheduled if possible.

教師姓名	王正豪　教師諮商時間(Office Hours)
Email	jhwang@ntut.edu.tw
最後更新時間	2024-09-27 11:24:25
課程大綱	資料科學為最熱門的職業之一，各行各業對於數據分析的人才需求正在蓬勃發展，本課程在於培育從事資料科學或機器學習的人員來發展相關知識和技能。課程內容以線性代數、機率、統計學等工程數學為理論學習基礎，操作Python作為資料科學的實作工具，包括其相關Python基礎語法、各種資料型態、數據可視化、開源工具等。接著學習利用Python取得數據資料，進而操作各種迴歸分析來手動分析數據資料，並實作梯度遞減，決策樹等最佳化演算方法。最後則是介紹機器學習、深度學習等最新的AI演算方法來分析大數據並輔助決策。 Data Science has been ranked as one of the hottest professions and the demand for data practitioners is booming. This course is designed to develop people engaged in data science or machine learning to develop relevant knowledge and skills. You will understand how data science works based on mathematics, such as linear algebra, probability and statistics. Python is the best tool to learn how to conduct data science analyze data. Learn the basic language skills, common data types, visualization tools and extended modules for data grabbing and regression analysis. Gradient decent and decision tree for optimization will be covered. Finally, AI algorithm, machine learning, and deep learning, are introduced how data help in decision making.
課程進度 (1-16週)	1. Characteristics of data 2. Introduction to Python 3. Basics: probability, statistics, linear algebra 4. Data Preprocessing 5. Frequent Pattern Mining 6. Regression and Prediction 7. Classification 8. Clustering 9. Applications
評量方式與標準	Labs and exercises: 50% Term project presentation and report: 50%
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 1. Python Data Science Handbook: Essential Tools for Working with Data 1st Edition, by Jake VanderPlas, 2017. 2. Data Science from Scratch: First Principles with Python, 2nd edition, by Joel Grus, 2019.
課程諮詢管道	The students can discuss with the teacher and the TA by appointment via e-mail. Other communication channels such as Teams or LINE might be established.
延伸教學與資源
課程對應SDGs指標	無（None）
課程是否導入AI
備註	The course sessions will be recorded when possible. Online sessions via Microsoft Teams will be scheduled if possible.