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

114-1 349389 資料科學 1 3.0 3 ★ 朴維鎮工管所 30 0 　

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
114-1	349389	資料科學	1	3.0	3	★	朴維鎮	工管所	30	0

教學大綱與進度：

教師姓名朴維鎮　教師諮商時間(Office Hours)

Email eugenepark993@gmail.com

最後更新時間 2025-05-28 10:29:34

課程大綱 This course introduces students to the process of data science, covering problem formulation, data acquisition, modeling and analysis, and presentation and integration into action. Students will learn useful skills as well as basic statistical methods by completing several hands-on problems focused on real-world data. This course starts with a brief introduction to multivariate data analysis and data summarization including visualization. Then it extends to focus on using data to develop predictive capabilities using supervised and unsupervised machine learning models (regression, classification, clustering, PCA, etc.).

課程進度 Week 1 Data summarization and visualization Week 2-4 Multivariate normal distribution Week 5-6 Principal component analysis Week 7-8 Naïve Bayes, Neural networks Week 9 Exam or assignment Week 10-14 Supervised learning (Regression, Classification, Decision tree, kNN, SVM, Ensemble method – Bagging, RF, Boosting) Week 15-17 Unsupervised learning (Clustering, k-means) Week 18 Exam or assignment

評量方式與標準 1. Final Exam or Project: 40 % 2. Assignment: 50 % 3. Attendance: 10 %

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
Textbook: ‘Applied Multivariate Statistical Analysis’ written by Richard A. Johnson and Dean W. Wichern. Supplementary references: ‘Introduction to Machine Learning with Python: A Guide for Data Scientists’ written by Andreas C. Müller and Sarah Guido

課程諮詢管道 Email or i-school system.
Office hour: Thursday 11:10 ~ 12:00

課程對應SDGs指標 SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
SDG11：永續城市與社區（Sustainable Cities and Communities）

課程是否導入AI ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）

備註
The course link will be sent by the school E-mail.

教師姓名	朴維鎮　教師諮商時間(Office Hours)
Email	eugenepark993@gmail.com
最後更新時間	2025-05-28 10:29:34
課程大綱	This course introduces students to the process of data science, covering problem formulation, data acquisition, modeling and analysis, and presentation and integration into action. Students will learn useful skills as well as basic statistical methods by completing several hands-on problems focused on real-world data. This course starts with a brief introduction to multivariate data analysis and data summarization including visualization. Then it extends to focus on using data to develop predictive capabilities using supervised and unsupervised machine learning models (regression, classification, clustering, PCA, etc.).
課程進度	Week 1 Data summarization and visualization Week 2-4 Multivariate normal distribution Week 5-6 Principal component analysis Week 7-8 Naïve Bayes, Neural networks Week 9 Exam or assignment Week 10-14 Supervised learning (Regression, Classification, Decision tree, kNN, SVM, Ensemble method – Bagging, RF, Boosting) Week 15-17 Unsupervised learning (Clustering, k-means) Week 18 Exam or assignment
評量方式與標準	1. Final Exam or Project: 40 % 2. Assignment: 50 % 3. Attendance: 10 %
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 Textbook: ‘Applied Multivariate Statistical Analysis’ written by Richard A. Johnson and Dean W. Wichern. Supplementary references: ‘Introduction to Machine Learning with Python: A Guide for Data Scientists’ written by Andreas C. Müller and Sarah Guido
課程諮詢管道	Email or i-school system. Office hour: Thursday 11:10 ~ 12:00
課程對應SDGs指標	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG11：永續城市與社區（Sustainable Cities and Communities）
課程是否導入AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
備註	The course link will be sent by the school E-mail.