校際選修

115-1 選課時程

進行中

  • 初選第一階段 6/15/2026
  • 初選第二階段 6/22/2026
  • 校際選修 8/24/2026
  • 初選第三階段 8/31/2026
  • 開學後加退選 9/7/2026
  • 逾期加退選 9/21/2026
選課資源

資料科學

Data Science

學期
106-2
學分
3 學分
當期課號
5092
永久課號
GEE9024
開課單位
電機工程學系
授課教師
帥宏翰
校區
光復
類別
選修
上課時間表
週二
2
09:00–09:50
資料科學
EDB07
3 節連堂
3
10:10–11:00
4
11:10–12:00

* 根據陽明交大上課時間表所列

概述

This course will attempt to apply data mining, statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight from data. Only minimal probability and statistics background is expected. We will survey the foundational and important topics in data science listed as follows. 1) Data Manipulation 2) Data Analysis with Statistics and Machine Learning 3) Data Communication with Information Visualization 4) Data at massive scale (big data analytics)

先修科目

Prerequisites: Programming (C++, JAVA, or Python), Database, and Probability and Statistics *Knowledge of Python will be useful for the assignments

評分方式

Midterm: 30% Final Project (in groups): 27% Homework : 48% (6 @ 8% each) Class participation (up to 6 points)

週次計畫
週次主題
第 2 週Introduction to Data Science
第 3 週Intro Python / Data Crawling [HW1: Crawling Release]
第 4 週Data Mining (Frequent Pattern –Apriori + FP-Tree)
第 5 週Data Mining (Classification) [HW2: Data Mining]
第 6 週Data Mining (Classification)
第 7 週Data Mining (Clustering) + Evaluation
第 8 週Statistical Measurements/Feature Selection/Dimension Reduction [HW3: dimension reduction/feature selection]
第 9 週Machine Learning/Deep Learning
第 10 週Midterm
第 11 週Deep Learning [HW4]
第 12 週Multimedia Processing
第 13 週NLP [HW5]
第 14 週Invited Talk
第 15 週Graph Theory/SN analysis [HW6]
第 16 週Graph Theory/SN analysis
第 17 週Final presentation
第 18 週Final presentation
教科書

There is no single textbook. However, a couple of books that are useful and helpful are listed below: (1) Introduction to Data Mining, by P.-N. Tan, M. Steinbach, V. Kumar, 2005 (ISBN:0321321367) (2) Doing Data Science, by C. O'Neil and R. Schutt, 2013 (ISBN: 978-1-4493-5865-5) (3) Python for Data Analysis, by W. McKinney, 2012 (ISBN: 978-1-4493-1979-3)

Office Hours
地點
ED-807
時間
2 pm - 4 pm every Tuesday
聯絡方式
TEL:(03)571-2121#54530 EMAIL: hhshuai@nctu.edu.tw