課程資訊
課程名稱
Python程式設計基礎課程
Python Programming Foundation 
開課學期
111-1 
授課對象
 
授課教師
何承遠 
課號
IM1011 
課程識別碼
705 14000 
班次
01 
學分
3.0 
全/半年
半年 
必/選修
選修 
上課時間
星期一2,3,4(9:10~12:10) 
上課地點
管一B01 
備註
需自備筆電兼通識A6*。。A6*:量化分析與數學素養領域。可充抵通識
總人數上限:60人 
 
課程簡介影片
 
核心能力關聯
核心能力與課程規劃關聯圖
課程大綱
為確保您我的權利,請尊重智慧財產權及不得非法影印
課程概述

9/2 updated:
1. About online, e-learning, and live streaming
Right now, there are no online classes, e-learning materials, and live streaming, but I'm planning to record some videos, which have two versions, Chinese and English, for every week after the first week (Sept. 5~9).
When the videos are ready, I'll announce the news to all of you.
2. About extending number of student / adding course
As described on 8/18, basically, for the teaching quality, the number of students in this course would not be extended.
If you really want to know/learn Python, you are welcome to audit the course.
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8/18 updated:
There are a large number of registered students, therefore, for the teaching quality, the number of students in this course would not be extended.
This course will be offered every year, so the student who didn't be selected this time, you can register the course next time.
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In this course, you will learn and know 1) how to install Anaconda, which is a local platform to write Python programs, and use cloud Python platform, called Colab by Google, if you don't want to install Anaconda; and 2) basic commands and rules in Python, and Python-related packages and libraries, such as NumPy, Matplotlib, Pandas, and Scikit-Learn. After that, you will learn how to clean data, analyze data, and predict data with Python and its related libraries. Moreover, in most ends of parts, you will do some practices and write your code with Python to check your learning status. 

課程目標
1. Understand Python programming
2. Understand how to solve problem with Python
3. Understand how to analyze data and predict results with Python
 
課程要求
No 
預期每週課後學習時數
 
Office Hours
 
指定閱讀
No 
參考書目
1. Programming Python: Powerful Object-Oriented Programming
2. Learning Python
3. Python Cookbook: Recipes for Mastering Python 3
4. Introduction to Machine Learning with Python: A Guide for Data Scientists
 
評量方式
(僅供參考)
 
No.
項目
百分比
說明
1. 
Midterm 
30% 
 
2. 
Final Project 
40% 
 
3. 
Practice/Homework 
30% 
 
 
針對學生困難提供學生調整方式
 
上課形式
以錄影輔助, 提供學生彈性出席課程方式
作業繳交方式
口頭報告取代書面報告, 團體報告取代個人報告
考試形式
其他
由師生雙方議定
課程進度
週次
日期
單元主題
第1週
9/05  Course Overview, Python Setup, Python Object and Data Structure Basics 
第2週
9/12  Python Comparison Operators and Statements I 
第3週
9/19  Python Comparison Operators and Statements II 
第4週
9/26  Methods and Functions 
第5週
10/03  Modules and Packages 
第6週
10/10  National Day (Double Ten Day, Holiday, class cancelled) 
第7週
10/17  Errors and Exceptions Handling 
第8週
10/24  Midterm 
第9週
10/31  Web Scraping with Python 
第10週
11/07  Images Handling with Python 
第11週
11/14  Images Handling with Python/Data Handling and Visualization with Python 
第12週
11/21  Data Handling and Visualization with Python 
第13週
11/28  Lecture/Talk (topic: TBD) 
第14週
12/05  Machine Learning with Python (concept) 
第15週
12/12  Deep Learning with Python (concept) 
第16週
12/19  Final Project Presentation