課程資訊
課程名稱
資料分析方法
Data Analytics 
開課學期
110-2 
授課對象
共同教育中心  統計碩士學位學程  
授課教師
藍俊宏 
課號
IE5054 
課程識別碼
546EU4040 
班次
 
學分
3.0 
全/半年
半年 
必/選修
選修 
上課時間
星期一2,3,4(9:10~12:10) 
上課地點
綜202 
備註
本課程以英語授課。工程與環境統計領域選修課程之一。
總人數上限:42人 
 
課程簡介影片
 
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課程概述

Data analytics is now becoming the fashion in all domains. Related buzzwords, such as data mining, big data, artificial intelligence, machine learning, deep learning, are floating around in all kinds of media. In this course, we kick-off to understand the fundamental definitions behind all buzzwords as well as to learn the common techniques, such as multivariate statistical inference, and supervised/unsupervised learning algorithms. R or Python will be used through this course in order to comprehend, compare, and link the different techniques to the practical world.

DA course is now designed in a blended learning format, which includes: asynchronous video learning; face-to-face discussion; homework exercise; team project collaboration. 

課程目標
Students from this course shall learn to:
1. understand the data characteristics and the fitness of different algorithms;
2. pretreat and clean the data;
3. extract and select significant features;
4. explain the analytical results;
5. use R/Python for quick data analytics. 
課程要求
probability, statistics, linear algebra, and programming skills 
預期每週課後學習時數
 
Office Hours
 
指定閱讀
All the materials and videos will be available on COOL for you when enrolling in the course. 
參考書目
• Strang, G. (2006). Linear Algebra and Its Applications
• Montgomery, D. C., and Runger, G. C. (2014). Applied Statistics and Probability for Engineers
• Rencher, A. C., and Christensen, W. F. (2012). Methods of Multivariate Analysis
• Johnson, R., and Wichern D. (2014). Applied Multivariate Statistical Analysis
• Izenman A. J., 1st edition, Modern Multivariate Statistical Techniques
• James, G., Witten, D., Hastie, T., and Tibshirani, R. (2017). An Introduction to Statistical Learning
• Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning 
評量方式
(僅供參考)
   
課程進度
週次
日期
單元主題
第1週
2/14  Review & Preview 
第2週
2/21  Regression Analysis 
第3週
2/28  Bank Holiday (228 Peace Memorial Day) 
第4週
3/07  Multivariate Statistical Inference 
第5週
3/14  Dimension Reduction Techniques 
第6週
3/21  Partial Least Squares Regression 
第7週
3/28  Big Data Infrastructure 
第8週
4/04  Bank Holiday 
第9週
4/11  Mid-term Exam 
第10週
4/18  Supervised Learning Algorithms 
第11週
4/25  Supervised Learning Algorithms 
第12週
5/02  Unsupervised Learning Algorithms × Project Pitch 
第13週
5/09  Unsupervised Learning Algorithms 
第14週
5/16  Machine Learning Techniques 
第15週
5/23  Deep Neural Nets 
第16週
5/30  Project Presentation Day (Peer Review) 
第17週
6/06  Report Due