|
課程名稱 |
數據分析之計算統計學
Computational Statistics for Data Analytics |
|
開課學期 |
112-1 |
|
授課對象 |
工學院 土木工程學系 |
|
授課教師 |
汪立本 |
|
課號 |
CIE5140 |
|
課程識別碼 |
521 U9270 |
|
班次 |
|
|
學分 |
3.0 |
|
全/半年 |
半年 |
|
必/選修 |
選修 |
|
上課時間 |
星期一6(13:20~14:10)星期四6,7(13:20~15:10) |
|
上課地點 |
普501普501 |
|
備註 |
本課程中文授課,使用英文教科書。須修過「工程統計學」及「計算機程式」。教材、作業及考試題目為英文。
限學士班三年級以上
總人數上限：30人 |
|
|
|
|
課程簡介影片 |
|
|
核心能力關聯 |
本課程尚未建立核心能力關連 |
|
課程大綱
|
|
為確保您我的權利,請尊重智慧財產權及不得非法影印
|
|
課程概述 |
This course is an extension of the Engineering Statistics and Computer Programming courses. Students will work extensively with real-world data (relevant to engineering, physics and environment) during classes. The knowledge learned from the aforementioned two courses will be briefly reviewed and further strengthened through a series of hands-on projects. This course will enable students to develop solid data analytical skills and problem-solving mindsets, which will be useful whether they decide to work in industry or academia in the future. |
|
課程目標 |
With the development of sensing and computational technologies, the amount of data that modern engineers have to handle on a daily basis has largely increased. The aim of this course is to provide civil engineering students proper training to ensure that they will be equipped with essential skills to explore unknown data, as well as to develop data scientists’ problem-solving and self-learning mindsets. |
|
課程要求 |
Computer programming
Engineering statistics |
|
預期每週課後學習時數 |
|
|
Office Hours |
|
|
指定閱讀 |
|
|
參考書目 |
Larry Wasserman, All of Statistics: A Concise Course in Statistical Inference, Springer, USA, 2004.
Allen B. Downey, Think Bayes: Bayesian Statistics Made Simple, O'Reilly, 2012.
Allen B. Downey, Think Stats: Probability and Statistics for Programmers, O'Reilly, 2014.
Allen B. Downey, Think Stats: Exploratory Data Analysis in Python, O'Reilly, 2014.
Annette J. Dobson & Adrian G. Barnett, An Introduction to Generalized Linear Models, 4th Edition, Chapman & Hall/CRC, 2018.
Christian Onof, Lecture Notes for Statistics, Imperial College London, 2017. |
|
評量方式
(僅供參考) |
|
|
針對學生困難提供學生調整方式 |
|
上課形式 |
以錄音輔助, 提供學生彈性出席課程方式 |
|
作業繳交方式 |
|
|
考試形式 |
|
|
其他 |
|
|
|
週次 |
日期 |
單元主題 |
|
Week 1 |
2023/09/04 (Mon) |
- Course Intro
- Python in a nutshell |
|
Week 1 |
2023/09/07 (Thu) |
- Descriptive Stats
- Probability and Random variables |
|
Week 2 |
2023/09/11 (Mon) |
Python for basic data processing |
|
Week 2 |
2023/09/14 (Thu) |
Probability distributions |
|
Week 3 |
2023/09/18 (Mon) |
Scipy.stats for probability distribution and random variable sampling |
|
Week 3 |
2023/09/21 (Thu) |
- Probability distribution fitting, MLE
- Mixture distribution fitting |
|
Week 4 |
2023/09/25 (Mon) |
MLE fitting: handmade vs. scipy.stats |
|
Week 4 |
2023/09/28 (Thu) |
No class |
|
Week 5 |
2023/10/02 (Mon) |
Midterm (I) |
|
Week 5 |
2023/10/05 (Thu) |
Multivariable (Part 1)
Multivariable (Part 2) |
|
Week 6 |
2023/10/09 (Mon) |
No Class (bridge holiday) -- made up on 2023/09/23
Multivariable coding |
|
Week 6 |
2023/10/12 (Thu) |
Confidence intervals |
|
Week 7 |
2023/10/16 (Mon) |
Bootstrapping |
|
Week 7 |
2023/10/19 (Thu) |
Statistical test (Part 1)
Statistical test (Part 2) |
|
Week 8 |
2023/10/26 (Thu) |
Midterm (II): take-home (2022/10/23 – 2022/10/30) |
|
Week 8 |
2023/10/23 (Mon) |
Statistical test coding |
|
Week 9 |
2023/10/30 (Mon) |
Midterm (II): take-home (2022/10/23 – 2022/10/30) |
|
Week 9 |
2023/11/02 (Thu) |
Linear Regression
|
|
Week 10 |
2023/11/06 (Mon) |
Trend analysis coding |
|
Week 10 |
2023/11/09 (Thu) |
Working with public datasets |
|
Week 11 |
2023/11/13 (Mon) |
Working with scientific data files
|
|
Week 11 |
2023/11/16 (Thu) |
Spatial statistics (I): variogram |
|
Week 12 |
2023/11/20 (Mon) |
Spatial structure analysis Coding |
|
Week 12 |
2023/11/23 (Thu) |
Spatial statistics (II): kriging |
|
Week 13 |
2023/11/27 (Mon) |
Kriging Coding
|
|
Week 13 |
2023/11/30 (Thu) |
Data visualisation |
|
Week 14 |
2023/12/04 (Mon) |
Data analysis with ChatGPT |
|
Week 14 |
2023/12/07 (Thu) |
Bayesian application: Kalman filter |
|
Week 15 |
2023/12/11 (Mon) |
Kalman filter coding with filterpy |
|
Week 15 |
2023/12/14 (Thu) |
Bayesian inference: basics
Bayesian inference: simulation |
|
Week 16 |
2023/12/18 (Mon) |
Boostrapping (small samples)
Final assignment (2022/12/18 – 2022/12/22) |
|
Week 16 |
2023/12/21 (Thu) |
Final assignment (2022/12/18 – 2022/12/22) |
|