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 |
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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 |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
課程大綱
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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 |
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Office Hours |
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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) |
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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 |
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