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 guides students into the field of data science, using the Python programming language for practical operations. It trains students to have the integration skills of programming, statistical analysis and professional fields, so as to train students to become data scientists with cross-domain applications. The course design will start with the most basic programming logic, using simple words and short programs to guide students step by step in learning Python's syntax and various applications. The content includes: 1. Internet data acquisition, data cleaning and storage; 2. Use of data science suites to explore data analysis; 3. Use of machine learning algorithms to predict data; 4. Exploration and visual expression of text data.
Upon completion of this course, students will be able to
(1) Have a basic concept of data science and the ability to operate in Python;
(2) Retrieve different types of data from the Internet, understand the procedures and methods of data analysis, and express and communicate visual data;
(3) Apply the learned knowledge and skills into cross-field applications.

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 guides students into the field of data science, using the Python programming language for practical operations. It trains students to have the integration skills of programming, statistical analysis and professional fields, so as to train students to become data scientists with cross-domain applications.
The course design will start with the most basic programming logic, using simple words and short programs to guide students step by step in learning Python's syntax and various applications. The content includes: 1. Internet data acquisition, data cleaning and storage; 2. Use of data science suites to explore data analysis; 3. Use of 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: Prepare your own handouts.
Reference books:
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.