|
課程名稱 |
程式設計
Computer Programming |
|
開課學期 |
113-2 |
|
授課對象 |
理學院 地理環境資源學系 |
|
授課教師 |
亞歷山卓 |
|
課號 |
Geog1027 |
|
課程識別碼 |
208E11510 |
|
班次 |
|
|
學分 |
3.0 |
|
全/半年 |
半年 |
|
必/選修 |
必帶 |
|
上課時間 |
星期四7,8,9(14:20~17:20) |
|
上課地點 |
地理電腦室 |
|
備註 |
本課程以英語授課。
限本系所學生(含輔系、雙修生)
總人數上限：50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 |
The course provides basic concepts of coding, from variables and conditional execution, to iterations and function declaration, including the use of popular Python libraries for advanced data analysis. The first part of the course introduces the standard elements of programming, aiming to deliver an overall practical understanding of the Python coding environment; the second part focuses on data-oriented applications of computer programming, especially related to data processing and data visualization. |
|
課程目標 |
The course focuses on the logic of programming, aiming to help students develop essential coding skills for future digital-oriented geo-related problems. At the end of the course, students are expected to be able to master advanced data processing tools for building automatic workflows and extracting information from complex digital systems. |
|
課程要求 |
Students are required to complete designated assignments. Note that the syntaxes that are not taught in the class are not allowed, and plagiarism is strictly prohibited (including the plagiarizing from any AI tools). |
|
預期每週課前或/與課後學習時數 |
|
|
Office Hours |
另約時間 備註： by appointments |
|
指定閱讀 |
Students are strongly advised to refer to the instructor’s slides, which will be shared on NTU COOL on a regular basis. Nevertheless, reference books for a more in-depth investigation of the coding reality are listed in the “References” section. |
|
參考書目 |
- Severance (2016), Python for Everybody: Exploring Data in Python 3, CreateSpace Independent Publishing Platform.
- Deitel and Deitel (2021), Intro to Python for Computer Science and Data Science, Pearson FT Press
- McKinney (2017), Python for Data Analysis, 2nd edition, O'Reilly Media. |
|
評量方式
(僅供參考) |
|
No. |
項目 |
百分比 |
說明 |
|
1. |
Assignments |
15% |
|
2. |
Midterm Exam |
50% |
|
3. |
Final Exam |
35% |
|
|
|
針對學生困難提供學生調整方式 |
|
上課形式 |
提供學生彈性出席課程方式 |
|
作業繳交方式 |
學生與授課老師協議改以其他形式呈現 |
|
考試形式 |
|
|
其他 |
由師生雙方議定 |
|
|
週次 |
日期 |
單元主題 |
|
第1週 |
2/20 |
Course Introduction |
|
第2週 |
2/27 |
Algorithms |
|
第3週 |
3/06 |
Variables & Conditional Execution |
|
第4週 |
3/13 |
Iterations |
|
第5週 |
3/20 |
Function & Recursion |
|
第6週 |
3/27 |
Lists, Tuples, Sets, Dictionaries |
|
第7週 |
4/03 |
NO CLASS (Children's Day) |
|
第8週 |
4/10 |
Numpy Arrays & Strings |
|
第9週 |
4/17 |
Comprehensive Review |
|
第10週 |
4/24 |
Midterm Exam |
|
第11週 |
5/01 |
Pandas DataFrame |
|
第12週 |
5/08 |
Matplotlib Visualization |
|
第13週 |
5/15 |
Processing Time-series Data |
|
第14週 |
5/22 |
Processing Image Data |
|
第15週 |
5/29 |
Comprehensive Review |
|
第16週 |
6/5 |
Final Exam |
|