Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	365718	Data Science	1	3.0	3	★	Jenq-Haur Wang Tun-Wen Pai	AI雙聯專班	14	0	碩一修

Course Plan：

Instructor	Jenq-Haur Wang　Office Hours
Email	jhwang@ntut.edu.tw
Last Updated	2026-08-17 17:54:56
Course Objective	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.
Course Schedule (Week 1-16)	1. Data Preprocessing 2. Frequent Pattern Mining 3. Classification 4. Clustering 5. Introduction to Distributed Framework
Flexible Learning (Week 17-18)	Category ● 學生分組實作及討論(Student group practice and discussion) ● 遠距教學(Distance learning) Content Week 17-18: Group-based project implementation and discussions for students, and group-based online presentation of project results. Hours 6 Learning Outcomes In addition to online presentation of project results, each team has to submit project files, including: presentation files, source codes, documentation. Assessment Percentage Project Results Presentation: 30%~35%
Evaluation and grading policy	Attendance and In-class Exercises: 65%~70% Project Results Presentation: 30%~35%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Jiawei Han, Jian Pei, and Hanghang Tong, Data Mining: Concepts and Techniques, 4th ed., Morgan Kaufmann Publishers, Oct. 2022. [DM4] (selected chapters)
The access to curricular consultation	In addition to Teams for the course, you may contact with the TA and Instructor by e-mail.
Expanding teaching and resources	● 混成學習 (Blended Learning) 課程結合實體授課與線上學習（同步視訊／非同步活動）。 Combines face-to-face instruction with online learning (including synchronous video sessions or asynchronous activities). ● 數位科技融入 (Digital Technology Integration) 運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。 Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment.
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
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