目標：
- 學習計算思維
- 學會如何使用 Python 做科學計算以及資料處理
- 學會簡單網路爬蟲
- 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，並能以清楚明瞭的圖表方式來呈現，對於資料所呈現的訊息能快速地掌握。

Target:
- Learning calculation thinking
- How to use Python for scientific calculation and data processing
- Learning to simply crawl the Internet
- Visualization of academic data
- Lay the foundation for the application of large data in the future
Connotation:
This course provides students from non-information-related subjects and can learn digital skills and computing thinking in various subjects and fields and future jobs, allowing you to have information programming skills, and in the future, you can combine these information tools to solve professional problems in specific fields. This course uses learning the programming language Python, which teaches students to write programs, and also connects to the basics of online crawling and large data analysis. Python is a simple and easy to learn and easy to read. This course will lead everyone to understand this language and learn computer computing thinking. The course leads students from daily data processing to the field of computer science, and can use their own professional theoretical thinking to real applications, solve practical problems and special topics in programming language, and use natural domain applications as the direction, including teaching students how to analyze network data and government data to open the platform (open) data), large data analysis and data visualization methods can be presented in clear and clear charts, and the information presented in data can be quickly grasped.

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-edited textbooks
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/