本課程是在引導學生進入資料科學的領域，搭配Python程式語言進行實際操作，訓練學生具備程式設計、統計分析與專業領域的整合技能，以培養學生成為跨領域應用的資料科學家。課程設計將從最基礎的程式邏輯開始，用淺顯文字和簡短程式，手把手帶領學生學習Python的語法與各種應用，內容包括：一、網路資料擷取、資料清理與儲存；二、使用資料科學套件的進行探索資料分析；三、運用機器學習的演算法來預測資料；四、文字資料的探勘和視覺化的表達。
在完成此課程的學習，學生將能夠
(1) 對資料科學有基本的觀念，以及具備Python操作的能力；
(2) 從網際網路擷取不同型態的資料、瞭解資料分析的程序與方法、視覺化資料的表達與溝通；
(3) 運用所學到的知識與技能，融入在跨領域的應用。This course is to guide students into the field of data science, and cooperate with Python programming language for actual operations, train students to have integrated skills in programming, statistical analysis and professional fields to cultivate data scientists who generate cross-domain applications. The course design will start with the most basic program logic, use clean text and short programs to lead students to learn Python's syntax and various applications, including: 1. Online data acquisition, data cleaning and storage; 2. Use data science The exploration data analysis of the suite; 3. Use machine learning algorithms to predict data; 4. Exploration and visual expression of text data.
After completing the learning process, students will be able to
(1) Have basic concepts about data science and have the ability to operate Python;
(2) Obtain data of different types from the Internet, understand the procedures and methods of data analysis, and visualize the expression and communication of data;
(3) Use the knowledge and skills learned to integrate into applications across fields.
本課程是在引導學生進入資料科學的領域，搭配Python程式語言進行實際操作，訓練學生具備程式設計、統計分析與專業領域的整合技能，以培養學生成為跨領域應用的資料科學家。
課程設計將從最基礎的程式邏輯開始，用淺顯文字和簡短程式，手把手帶領學生學習Python的語法與各種應用，內容包括：一、網路資料擷取、資料清理與儲存；二、使用資料科學套件的進行探索資料分析；三、運用機器學習的演算法來預測資料；四、文字資料的探勘和視覺化的表達。
This course is to guide students into the field of data science, and cooperate with Python programming language for actual operations, train students to have integrated skills in programming, statistical analysis and professional fields to cultivate data scientists who generate cross-domain applications.
The course design will start with the most basic program logic, use clean text and short programs to lead students to learn Python's syntax and various applications, including: 1. Online data acquisition, data cleaning and storage; 2. Use data science The exploration data analysis of the suite; 3. Use machine learning algorithms to predict data; 4. Exploration and visual expression of text data.
教科書：自行編製講義。
參考書：
1. Grus, J. 2015. Data Science from Scratch. O’Reilly Media.
2. McKinney, W. 2017. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. 2nd Edition. O’Reilly Media.
3. VanderPlas, J. 2016. Python Data Science Handbook: Essential Tools for Working with Data. O’Reilly Media.
4. Hofmann, M. and A. Chisholm. 2016. Text Mining and Visualization: Case Studies Using Open-Source Tools. Taylor & Francis.
Textbook: Self-editing lectures.
Reference book:
1. Grus, J. 2015. Data Science from Scratch. O’Reilly Media.
2. McKinney, W. 2017. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. 2nd Edition. O’Reilly Media.
3. VanderPlas, J. 2016. Python Data Science Handbook: Essential Tools for Working with Data. O’Reilly Media.
4. Hofmann, M. and A. Chisholm. 2016. Text Mining and Visualization: Case Studies Using Open-Source Tools. Taylor & Francis.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 課堂參與課堂參與 Class Participation |
15 | 日後可能視情況調整比例分配 |
| 作業作業 Action |
25 | 日後可能視情況調整比例分配 |
| 期中考期中考 Midterm exam |
30 | 日後可能視情況調整比例分配 |
| 期末報告期末報告 Final report |
30 | 日後可能視情況調整比例分配 |
