| 課程與核心能力關聯配比(%) |
課程目標之教學方法與評量方法 |
| 課程目標 |
核心能力 |
配比(%) |
教學方法 |
評量方法 |
| The goal of this course is to understand the basic concepts of statistics and become familiar with common data analysis techniques. Through hands-on experience with real-world datasets, students will learn how to appropriately analyze data and extract useful insights. |
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| 授課內容(單元名稱與內容、習作/每週授課、考試進度-共16週加自主學習) |
| 週次 |
授課內容 |
| 第1週 |
Overview and Software Installation |
| 第2週 |
Data Input and Output |
| 第3週 |
Flow Control |
| 第4週 |
Data Structures and Functions |
| 第5週 |
Comparing Differences |
| 第6週 |
Operations on Matrices |
| 第7週 |
Student Presentation 1 |
| 第8週 |
Estimation and Optimization |
| 第9週 |
Merging and Filtering Data |
| 第10週 |
Association and Prediction |
| 第11週 |
Association and Prediction |
| 第12週 |
Association and Prediction |
| 第13週 |
Unsupervised Learning |
| 第14週 |
Unsupervised Learning |
| 第15週 |
Unsupervised Learning |
| 第16週 |
Student Presentation 2 |
自主學習
內容 |
02.閱覽產業及學術相關多媒體資料
03.製作專題報告
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| 學習評量方式 |
Quizzes: 28%
Presentation 1: 36%
Presentation 2: 36% |
| 教科書＆參考書目(書名、作者、書局、代理商、說明) |
Hansjörg Neth (2025). Introduction to data science (i2ds). Available at https://bookdown.org/hneth/i2ds/
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2022). An introduction to statistical learning: with applications in R (2nd ed). Springer. |
| 課程教材（教師個人網址請列在本校內之網址） |
| The iLearning site will host the uploaded materials |
| 課程輔導時間 |
| Tuesday 13:00~15:00 |
| 聯合國全球永續發展目標(連結網址) |
| 08.就業與經濟成長 09.工業、創新基礎建設 | 提供體驗課程：N |
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