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課程名稱 |
程式設計
Computer Programming |
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開課學期 |
112-2 |
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授課對象 |
理學院 地理環境資源學系 |
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授課教師 |
亞歷山卓 |
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課號 |
Geog1027 |
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課程識別碼 |
208E11510 |
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班次 |
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學分 |
3.0 |
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全/半年 |
半年 |
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必/選修 |
必帶 |
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上課時間 |
星期四7,8,9(14:20~17:20) |
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上課地點 |
地理電腦室 |
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備註 |
本課程以英語授課。與楊啟見合授
限本系所學生(含輔系、雙修生)
總人數上限：50人 |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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為確保您我的權利,請尊重智慧財產權及不得非法影印
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課程概述 |
Computer programming is a fundamental course for data processing. The course provides basic concepts of coding, from variables and conditional execution, to iterations and function declaration, with 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 real-world applications of computer programming in the field of physical geography. |
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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 mining information from complex digital systems. |
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課程要求 |
The course evaluation consists of a midterm examination and a final examination, where both conceptual and hands-on programming capabilities will be exhaustively assessed.
Students are required to complete weekly assignments. Note that the syntaxes that are not taught in the class are not allowed, and plagiarism is strictly prohibited (including the plagiarizing from ChatGPT). |
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預期每週課後學習時數 |
Students are expected to complete the weekly assignments for enhencing both conceptual and hands-on abilities. Note that the syntaxes that are not taught in the class are not allowed, and plagiarism is strictly prohibited (including the plagiarizing from ChatGPT). |
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Office Hours |
另約時間 備註： By appointment. Please email the instructors or TA. |
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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. |
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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. |
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評量方式
(僅供參考) |
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針對學生困難提供學生調整方式 |
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上課形式 |
提供學生彈性出席課程方式 |
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作業繳交方式 |
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考試形式 |
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其他 |
由師生雙方議定 |
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週次 |
日期 |
單元主題 |
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第1週 |
2/22 |
Course Introduction: Why Should We Learn Coding? |
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第2週 |
2/29 |
Variables and Conditional Execution |
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第3週 |
3/7 |
Lists, Dictionaries, and Tuples |
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第4週 |
3/14 |
Iterations |
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第5週 |
3/21 |
Functions |
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第6週 |
3/28 |
Strings |
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第7週 |
4/4 |
NO CLASS (Tomb Sweeping Festival) |
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第8週 |
4/11 |
Python for Data Analysis: Numpy and Matplotlib |
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第9週 |
4/18 |
Python for Data Analysis: Pandas |
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第10週 |
4/25 |
Comprehensive Practice |
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第11週 |
5/2 |
MIDTERM EXAM |
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第12週 |
5/9 |
Plotting - Topographic Map |
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第13週 |
5/16 |
Data Analysis: Geochemical Data |
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第14週 |
5/23 |
Data Analysis: Geophysical Data |
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第15週 |
5/30 |
Deep Learning: Image Interpretation |
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第16週 |
6/6 |
FINAL EXAM |
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