課程名稱 |
資料分析方法 Data Analytics |
開課學期 |
112-2 |
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 |
新402 |
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 increasingly recognized as a pivotal element across various sectors. This course aims to demystify prevalent jargon, including data mining, big data, artificial intelligence, machine learning, and deep learning, prevalent across diverse media platforms. We intend to dissect the foundational concepts associated with these buzzwords, in addition to exploring a spectrum of methodologies such as multivariate statistical inference, alongside unsupervised and supervised learning algorithms. Throughout the course, R or Python will serve as the instrumental tools, facilitating the analysis, synthesis, and application of these methodologies in real-world scenarios.
This course is meticulously structured in a blended learning format, encompassing a variety of components: asynchronous video content for independent learning, interactive face-to-face discussions, practical homework exercises, and a culminating group project.
Prospective participants are encouraged to attend the inaugural lecture to gauge the course's alignment with their academic and professional aspirations. Access codes for course registration will be issued post the initial session enrollment. |
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 |
備註: TBD |
指定閱讀 |
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參考書目 |
‧ Strang, G. (2006). Linear Algebra and Its Applications
‧ Montgomery, D. C., & Runger, G. C. (2014). Applied Statistics and Probability for Engineers
‧ Rencher, A. C., & Christensen, W. F. (2012). Methods of Multivariate Analysis
‧ Johnson, R., & Wichern D. (2014). Applied Multivariate Statistical Analysis
‧ Izenman A. J., 1st edition, Modern Multivariate Statistical Techniques
‧ James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An Introduction to Statistical Learning
‧ Goodfellow, I., Bengio, Y., & 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 |
Week 1 |
Feb. 19 |
Review & Preview |
Week 2 |
Feb. 26 |
Regression Analysis |
Week 3 |
Mar. 04 |
Regression Analysis |
Week 4 |
Mar. 11 |
Multivariate Statistical Inference |
Week 5 |
Mar. 18 |
Dimension Reduction Techniques |
Week 6 |
Mar. 25 |
Partial Least Squares Regression |
Week 7 |
Apr. 01 |
Big Data Infrastructure × Team Building* |
Week 8 |
Apr. 08 |
Mid-term Exam |
Week 9 |
Apr. 15 |
Supervised Learning Algorithms |
Week 10 |
Apr. 22 |
Supervised Learning Algorithms |
Week 11 |
Apr. 29 |
Unsupervised Learning Algorithms |
Week 12 |
May 06 |
Unsupervised Learning Algorithms |
Week 13 |
May 13 |
Machine Learning Techniques |
Week 14 |
May 20 |
Deep Neural Nets |
Week 15 |
May 27 |
Deep Neural Nets |
Week 16 |
Jun. 03 |
Project Presentation Day (Peer Review*) |
Week 17 |
Jun. 07 |
Report Due |
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