Program Overview

Program Overview

The program is titled "Python程式設計" (Python Programming Design), with the course number Data5006 and course identification code 946 U0060.

Course Details

• Course Credits: 3 credits
• Class Time: Tuesday, 6, 7, 8
• Classroom: 新403
• Category: 3 類
• Enrollment Limit: 60 students
• Language of Instruction: 中文 (Chinese)
• NTU COOL: Available

Course Description

Python is a widely used programming language in various fields due to its clear, elegant, and concise syntax structure and code readability. This course starts with setting up the Python environment and teaches various syntax structures and their usage. The course progresses by introducing basic knowledge of Python, accompanied by practical exercises to help students accumulate experience in writing Python programs. The course content includes:

1. Introduction to Python and the Colab platform
2. Basic variable types, syntax structures, and advanced topics in Python
3. Exploration of popular Python packages
4. Final project: Selecting a problem and presenting how to solve it using Python

Course Objectives

The course aims to teach Python programming from the basics to practical applications, enabling students to use Python to solve problems in various fields such as data science, artificial intelligence, and computational social networks. Through the final project, students will solidify their knowledge by selecting a problem they are interested in and presenting how to solve it using Python.

Course Requirements

The course is recommended for individuals who:

1. Fear writing programs
2. Want to write programs but do not know where to start
3. Have a small application or personal project they want to complete but lack the time or a platform to present it The course strongly advises against enrollment for individuals who:
4. Have any programming experience, as the teaching style may cause discomfort
5. Are not sociable, as the course includes a group final project
6. Want to pass the course easily without putting in sufficient effort

Evaluation Method

• Attendance: 10%
  • At least three attendance records per week
  • Accepts leave of absence only before the class starts, with exceptions for specific reasons
• Homework: 70%
  • Approximately 10-12 homework assignments, including programming homework and online quizzes
• Group Project: 20%
  • Final project in groups, with each group limited to a certain number of students
  • Failure to participate in the group project will result in an F grade

Additional Information

• Office Hour: Not specified
• Required Reading: To be supplemented
• Reference Books:
  1. Fluent Python: Clear, Concise, and Effective Programming (1st Edition) by Luciano Ramalho
  2. Introduction to Machine Learning with Python: A Guide for Data Scientists (1st Edition) by Andreas C. Muller, Sarah Guido
  3. 少年Py的大冒險：成為Python數據分析達人的第一門課 by 蔡炎龍, 季佳琪, 陳先灝, 全華圖書
• Course Schedule:
  • Week 1: Introduction to the instructor, Python, and basic settings of Colab
  • Week 2: Writing the first Python program
  • Week 3: No class (online video) - Basic object types in Python
  • Week 4: Containers in Python (list, set), custom functions
  • Week 5: Flow control (if-else, for loop, while loop)
  • Week 6: File reading, string processing, and output
  • Week 7: Nested structures
  • Week 8: Introduction to NumPy
  • Week 9: Introduction to Pandas
  • Week 10: Data visualization
  • Week 11: Exception handling
  • Week 12: Guest lecture
  • Week 13-16: Final project preparation and presentation

Notes

• The course is limited to non-electrical and computer science students.
• Students are required to bring their own computers for practical exercises.
• The course includes a group final project, and students are expected to participate actively.