Welcome to Python (I), Fall 2026! This page serves as the shared course hub for all sections (分班).

For section-specific announcements and course management, please refer to your class page on the KMU New E-Learning Platform (高雄醫學大學數位學習平台). 

I. Course Description / 課程簡介

This course introduces computational thinking and basic programming using Python. Through step-by-step learning, in-class demonstrations, and practical examples drawn from everyday and clinical contexts, students will learn how to break down problems, write and debug simple programs, and work with basic data.

The course also prepares students to collaborate with engineers and data scientists in clinical and research settings.

By the end of the course, students will be able to write Python programs to solve practical problems and read, organize, and analyze basic data.

II. How We Learn in This Course / 課堂學習方式 (Revisit throughout the semester / 建議於學期中持續回顧)

In this course, mistakes and questions are a normal part of learning. The goal is not to write perfect code on the first try, but to gradually learn how to understand problems, test ideas, read error messages, and improve your code.

1. Mistakes Are Expected

You are not expected to write correct code on the first try. Errors are a normal part of the learning process. Learning how to find, understand, and fix errors is an important programming skill.

2. Code Is Not Personal

When we discuss or debug code, we focus on the program, not the person who wrote it. Your code can be wrong. My code can be wrong. That is normal. Feedback on code is meant to help improve both the program and your understanding.

3. Questions Are Always Welcome

Ask questions whenever something is unclear. You are welcome to ask during class, after class, by email, or through other course support channels announced during the semester. If something is confusing to you, other students may have the same question.

4. Please Tell Me If You Think I Am Wrong

I may occasionally make mistakes while writing code or explaining concepts. If you think something is wrong, please let me know during class or privately afterward. Pointing out a possible mistake is always welcome.

5. When Your Code Does Not Work: Think, Try, Then Ask

When you encounter a programming problem, take a moment to review the code and any error messages before asking for help. Try to identify what may have gone wrong or what part you do not understand. When you ask, explain what you were trying to do, what happened, and what you have already tried or noticed.

6. How to Get Help

You may ask for help in the following ways:

• Ask questions during class.

• Ask the instructor after class.

• Contact the instructor by email or make an appointment if more time is needed.

• If a teaching assistant (TA) is assigned, the TA will also be available to help with course concepts, exercises, and common technical issues.

When asking for help with code, please share your code and describe what you are trying to do and what problem you encountered. The instructor and, if available, the TA can help you: 

• understand Python concepts;

• interpret error messages;

• clarify assignment instructions;

• identify problems in your code; and

• learn how to debug and solve the problem yourself.

However, the instructor and, if available, the TA will not: 

• write homework solutions for you;

• provide complete solution code; or

• complete or debug an entire assignment on your behalf.

Note: When contacting the instructor or TA (if available) by email, please include your name, student ID, department/program, and section (e.g., Section 1 or Section R) so that we can identify you and assist you more efficiently.

III. Course Topics / 課程主題

• Computational Thinking: step-by-step problem solving and basic troubleshooting

• Core Python Basics: variables, expressions, conditionals, functions, and loops

• Text & Data Structures: strings, lists, dictionaries, and tuples

• Files & Data Handling: basic file input/output and working with simple data

• Selected Topics (if time permits): regular expressions and object-oriented programming

IV. Sections / 分班

For section-specific updates, please refer to your class page on the KMU New E-Learning Platform.

• Section 1 (No. 9961200): Room IR503, Tue 1:10–3:00 PM

• Section 2 (No. 9961201): Room IR503, Tue 3:10–5:00 PM

• Section 3 (No. 9961202): Room IR334, Wed 1:10–3:00 PM

• Section 4 (No. 9961203): Room IR334, Wed 10:10 AM–12:00 PM

• Section 5 (No. 9961209): Room IR335, Mon 1:10–3:00 PM

• Section R (Smart Rainforest): Online Learning (primarily through recorded lectures; contact the project administrator for details)

All on-campus classrooms listed above are located in the International Academic Research Building (國際學術研究大樓).

V. Instructor & Contact / 授課教師與聯絡方式

• Instructor: Jacky Chung-Hao Wu (吳崇豪)

• Office: CS924, Chi-Shih Building (濟世大樓)

• Email: jackychwu@gap.kmu.edu.tw

• Office Hours: By appointment (please schedule by email)

VI. Prerequisites / 修課準備

No prior programming experience is required.

Basic computer skills are expected, such as using a web browser, signing in to online services, managing files and folders, extracting files from a ZIP archive before editing them, copying and pasting text, and saving files after making changes.

If you are not comfortable with these tasks or encounter difficulties, please let the instructor know. We can identify the problem together before it becomes a barrier to learning Python.

（如果對於上述任何電腦操作不熟悉或遇到問題，請務必告訴老師，不需要因為覺得這是「基本操作」而不好意思詢問。）

VII. Getting Started / 工具與環境準備

Please complete Items 1–3 during the first week of the semester. Google Colab will be introduced and demonstrated in class later.

1. KMU New E-Learning Platform & Email Notifications

   • Make sure you can log in to the KMU New E-Learning Platform and see the correct Python (I) section.

   • Check that the email address linked to your platform account is correct and accessible.

   • Important section-specific course announcements will be posted on the platform and also sent to the linked email address as a reminder.

   • The platform announcement board (課程公告板) should be used as the primary place to review and retrieve section-specific course information later.

   • Please make sure you can receive email notifications sent through the platform.

If you need information about the KMU email service, please refer to 高雄醫學大學電子郵件服務.

2. KMU Google Workspace Account

   • KMU provides students with a Google Workspace account. Your account typically uses the same username as your KMU student email, but with @gap.kmu.edu.tw as the domain.

   • For example: u123456789@kmu.edu.tw → u123456789@gap.kmu.edu.tw

   • This account will be used for Google Drive and Google Colab, and is recommended for GitHub account registration (see Item 3 below).

   • Please make sure you can sign in successfully.

If you need information about the KMU Google Workspace service, please refer to 高雄醫學大學Google Workspace教育版.

3. GitHub Account

   • A GitHub account will be used for homework assignments and the final exam.

   • If you do not already have a GitHub account, please create one at the GitHub sign-up page.

   • When creating a new account, it is recommended that you use your KMU Google Workspace email (see Item 2 above).

   • Make sure you can sign in successfully and remember your GitHub username.

   • Further instructions for the homework and exam submission workflow will be provided later.

   • HW0 will be used to help you test the submission workflow before graded homework begins.

4. Google Colab

   • Google Colab is a cloud-based programming environment and will be the primary programming environment used in this course.

   • No local Python installation is required.

   • Google Colab will be introduced and demonstrated in class later.

   • Workflow (for creating a blank Google Colab notebook):

(1) Sign in to your KMU Google Workspace account.

(2) Access Google Drive.

(3) Create a folder in Google Drive, for example: Python_I_Fall2026

(4) Open the folder.

(5) Click New (新增) → More (更多) → Google Colaboratory to create a blank notebook.

• If you cannot find Google Colaboratory:
  Click New (新增) → More (更多) → Connect more apps (連結更多應用程式)
  → search “Colaboratory” → install it (as shown in the figure below).

VIII. Weekly Schedule (Subject to change) / 週次進度(視課程進度調整)

The schedule below is a tentative plan and may be adjusted based on class progress.

• Weeks 1–3: Introduction & Basics

Why Program? (Introduction); Variables, Expressions, and Statements

• Weeks 4–6: Conditionals, Functions & Loops

Conditional Execution; Functions; Loops and Iterations

• Weeks 7–9: Text & Files

Strings; Files

• Weeks 10–13: Data Structures

Lists; Dictionaries; Tuples

• Weeks 14–17: Selected Topics (if time permits)

Regular Expressions; Object-Oriented Programming

• Week 18: Final Exam

IX. Materials & Resources / 教材與參考資料

• Textbook

  • Severance, C. (2017). Python for Everybody: Exploring Data in Python 3. Shroff Publishers.

    • Available online: Python for Everybody

• References

  • Downey, A. B. (2024). Think Python: How to Think Like a Computer Scientist (3rd ed.). O’Reilly Media.

    • Available online: Think Python

  • Scientific Python Lectures

  • Python documentation

• Lecture Materials

Lecture slides are adapted from materials by Charles R. Severance, Python for Everybody (CC BY).

Lecture materials will be added here progressively throughout the semester.

• Lecture 1: Why Program? (Slides | Colab)

• Lecture 2: Variables, Expressions, and Statements (Slides | Colab)

X. Grading / 評分方式

• Homework Assignments (60%)

Four graded homework assignments will be given during the semester, each worth 20 points (80 points total). 

A maximum of 60 homework points will count toward the final course grade. Any homework points earned beyond 60 will not increase the final grade further.

Students are encouraged to complete all assignments because the homework is designed to gradually build programming skills and help prepare for the final exam.

Please refer to XI. Homework Assignments / 作業 for details.

• Final Exam (40%)

The final exam will assess your understanding of the core programming concepts covered in this course.

Please refer to XII. Final Exam / 期末考 for details.

XI. Homework Assignments / 作業

There will be one ungraded assignment (HW0) and four graded programming assignments (HW1–HW4) during the semester. 

Release dates and deadlines will be announced later.

HW0 is a setup and practice assignment. It will not be graded. Its purpose is to help you become familiar with the homework submission workflow before the graded homework assignments begin.

HW1–HW4 are programming assignments designed to reinforce the Python concepts covered in class and gradually build your problem-solving and coding skills.

Please complete HW0 as early as possible once it is released.

• HW0: Autograding Setup & Confirmation (PDF)
  Deadline: Friday, October 2, 2026 at 6:00 PM (UTC+8)

XII. Final Exam / 期末考

The final exam will be conducted online. Further details will be announced later.

• Date: January 5, 2027 (Tue)

• Time: 6:30–9:00 PM

• Duration: 2 hours 30 minutes

• Format: Online exam

• Location: To be announced later.

XIII. Academic Integrity & AI Use / 學術誠信與AI工具使用

• Academic Integrity

  • Submit your own work for all homework assignments and exams.

  • Do not copy code from others or share complete solutions with other students.

  • You should be able to explain the code you submit and the reasoning behind your solution.

  • Violations of academic integrity will be handled in accordance with KMU policies.

• AI Use

  • Students are strongly encouraged to work through programming problems on their own before using AI tools. AI tools may be used as a supplementary aid, but they should not replace your own thinking, problem-solving, or understanding.

  • AI tools should not be used simply to generate complete solutions to homework problems.

  • If you use AI-generated content, you are responsible for reviewing, understanding, and verifying it before submitting your work. Any use of AI tools must be clearly acknowledged, for example, in code comments when appropriate.

  • You should be able to explain any AI-assisted code or content that you submit.

  • Recommended News: 開學演講破百萬瀏覽！陽明交大教授為何勸大學生少用AI？

XIV. Frequently Asked Questions (FAQ) / 常見問題

• Q1: Do I need to install Python on my computer?

A1: No. Google Colab will be the primary programming environment used in this course, so no local Python installation is required.

• Q2: What Python version should I use for this course?

A2: Use Python 3 only. Python 2 is outdated and no longer supported.

Since we mainly use Google Colab, you usually do not need to worry about the exact Python version.

If needed, you can check the current version in Colab by running: !python --version

If you choose to practice locally, use a current stable version of Python 3 from the official Python website.

• Q3: What Google account should I use for this course?

A3: Please use your KMU Google Workspace account for Google Drive and Google Colab.

Your account typically uses the same username as your KMU student email, but with @gap.kmu.edu.tw as the domain.

For example: u123456789@kmu.edu.tw → u123456789@gap.kmu.edu.tw

For more information, please refer to the official guide: 高雄醫學大學Google Workspace教育版.

• Q4: Are there any recommended tools for a better coding experience?

A4: Using VS Code with Anaconda is a good option if you would like to practice Python locally in a more flexible development environment.

This setup is optional and not required for this course.