目標：
- 學習計算思維
- 學會如何使用 Python 做科學計算以及網路爬蟲、資料處理
- 學會資料視覺化
- 奠定日後大數據相關應用之基石
內涵：
本課程針對非資訊相關科系所學生，能學習各科系領域及未來職場之數位技能與運算思維，使得您具備資訊程式技能，未來也在於可以結合這些資訊工具，解決特定領域的專業問題。本課程藉由學習程式語言Python，帶同學撰寫程式、也接觸網路爬蟲、大數據分析基礎。Python是一種簡單易學、容易閱讀，這門課程將深入淺出的帶領大家認識這個語言，學習電腦運算思維。課程帶領同學從日常的資料處理進入電腦科學的領域，能夠將自身專業所學理論實現於真實應用，以程式語言解決實際生活問題與專題以自然領域應用為導向， 包含教導同學如何分析網路資料、政府的資料開放平臺(open data)、大數據分析以及資料視覺化方法，讓圖表不再單調。

Target:
- Learn computational thinking
- Learn how to use Python for scientific computing, web crawling, and data processing
- Learn to visualize data
- Lay the foundation for future big data related applications
Connotation:
This course is aimed at students from non-information related majors. It can learn digital skills and computational thinking in various fields and future workplaces, so that you can have information programming skills. In the future, you can also combine these information tools to solve professional problems in specific fields. By learning the programming language Python, this course will teach students to write programs and gain exposure to web crawlers and the basics of big data analysis. Python is a simple language that is easy to learn and read. This course will guide you to understand this language and learn computer computing thinking in simple and easy-to-understand terms. The course leads students from daily data processing to the field of computer science, enabling students to implement the theories they have learned in their majors into real applications, using programming language to solve real-life problems and special topics oriented to natural field applications. It includes teaching students how to analyze online data, the government's open data platform (open data), big data analysis and data visualization methods, so that charts are no longer monotonous.

1. 自編教材
2. https://blockly-games.appspot.com
3. https://www.codecademy.com/learn/python
4. https://openpyxl.readthedocs.io/
5. https://docs.scipy.org/doc/
6. http://pandas.pydata.org
7. http://bokeh.pydata.org/

1. Self-compiled teaching materials
2. https://blockly-games.appspot.com
3. https://www.codecademy.com/learn/python
4. https://openpyxl.readthedocs.io/
5. https://docs.scipy.org/doc/
6. http://pandas.pydata.org
7. http://bokeh.pydata.org/