本課程為資料科學的入門導論，旨在協助學生建立對資料分析的基本認識與邏輯思維，並不要求學生具備程式設計背景。課程內容將涵蓋資料科學的基礎架構、標準作業流程（從資料蒐集、清理到分析），以及常見的商業應用模型。
在教學設計上，本課程以「觀念建立」為主，「工具體驗」為輔。我們將利用 Google Colab 與 Python 套件作為輔助工具，帶領學生進行基礎的資料處理與視覺化練習，目的在於理解資料分析的運作原理，而非訓練複雜的程式開發能力。教學重點在於引導學生學習如何定義問題、解讀數據報表，以及理解模型背後的統計意義。
此外，課程亦包含資料隱私與專業倫理的探討。期能透過本課程，培養學生具備基礎的資料素養（Data Literacy），瞭解資料科學的潛力與侷限，為未來進階學習或跨領域合作奠定基礎。
《 課程簡介 -- English 》
This course serves as an introductory guide to Data Science, aimed at helping students establish a fundamental understanding and logical approach to data analysis. No prior programming background is required. The curriculum covers the foundational frameworks of Data Science, standard workflows (ranging from data collection and cleaning to analysis), and common business application models.
In terms of instructional design, this course prioritizes conceptual understanding, supported by tool experimentation. We will utilize Google Colab and Python libraries as supplementary tools to guide students through basic data processing and visualization exercises. The primary goal is to comprehend the operational principles of data analysis rather than to train students in complex software development. The instructional emphasis is placed on guiding students to define problems, interpret data reports, and understand the statistical significance underlying the models.
Additionally, the course includes discussions on data privacy and professional ethics. Through this course, students are expected to cultivate basic Data Literacy, understanding both the potential and limitations of Data Science, thereby laying a solid foundation for future advanced studies or interdisciplinary collaboration.
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