Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2025	341218	Application of artificial intelligence in cross-domain management	1	3.0	3	★	Ching-Wen Chen	4BM2	40	0

Course Plan：

Instructor	Ching-Wen Chen　Office Hours
Email	ccw@ntut.edu.tw
Last Updated	2024-12-17 14:58:08
Course Objective	課程目標： 本課程旨在深入教授Pandas在經營管理領域的商業數據分析中的應用，讓學生掌握如何使用Pandas進行高效的數據處理與分析。學生將學習Pandas的基本操作，包括資料結構的建立、操作、數據清理技巧，以及如何使用Pandas進行高級數據分析技術。期末會讓學生自行實作2個實際商業數據分析的side project，學生將能夠獨立進行商業數據的分析，並解決實際商業問題。 課程描述： 這是一門針對經營管理領域的商業數據分析的高級Pandas課程。從Python的基礎知識開始，本課程將引導學生逐步熟悉Pandas的核心功能，包括Series和DataFrame的操作，數據清理技巧，以及數據分析方法。學生將學習如何處理缺失值、合併數據、進行分群分析，以及應用高級函數處理數據。透過實際案例分析，如系所生源分析和業務銷售分析，學生將能夠將理論知識應用於實際問題。課程強調實際操作和案例研究，旨在提高學生的數據分析技能，為未來職場中的數據分析工作做好準備。 Course Objectives: This course is designed to provide an in-depth teaching of Pandas in business data analysis within the field of business management, enabling students to master efficient data processing and analysis using Pandas. Students will learn the basics of Pandas operations, including the establishment and manipulation of data structures, data cleaning techniques, and the application of advanced data analysis techniques with Pandas. By the end of the course, students will be expected to independently undertake two side projects on practical business data analysis, equipping them with the skills to analyze business data and solve real-world business problems. Course Description: This advanced Pandas course focuses on business data analysis in the field of business management. Starting with the fundamentals of Python, the course will guide students through the core functionalities of Pandas, including the manipulation of Series and DataFrames, data cleaning techniques, and data analysis methods. Students will learn how to handle missing values, merge data, perform cluster analysis, and apply advanced functions for data processing. Through practical case studies, such as departmental student source analysis and business sales analysis, students will be able to apply theoretical knowledge to practical issues. The course emphasizes hands-on practice and case studies, aiming to enhance students' data analysis skills and prepare them for future data analysis roles in the workplace.
Course Schedule	第1週：chp1.課程簡介，與安裝使用python，python基礎 第2週：Pandas基礎入門練習 第3週：Pandas一維資料結構：Series 第4週：Pandas二維資料結構：DataFrame，DataFrame的5個屬性 第5週：查缺值NAN，DataFrame取值，三種格式，新增一欄/一列 第6週：用軸axis來輔助函數計算(mean,sum,max,min)， 第7週：篩選資料2種方法(條件查詢，query)，DataFrame的3種合併：concat，append，merge 第8週：DataFrame的常用函數，chp6-11.函數處理日期資料型態 第9週：期中考 第10週：map,applymap,apply迴圈函數計算每個資料格,輸出到excel，建立sheet 第11週：分群分析groupby，建立交叉分析表/樞紐分析表 第12週：二層列索引鍵，跨層級的資料索引 第13週：商業數據五大分析型態 第14週：各種資料清理方法：字串處理，正規表達式，日期處理 第15週：各種資料清理方法：電話處理，Email處理，分割字串 第16週：實例：系所生源分析 第17週：實例：業務銷售分析 第18週：期末考 **Week 1**: chp1. Course Introduction and Python Installation, Python Basics **Week 2**: Introduction to Pandas – Basic Exercises **Week 3**: Pandas One-Dimensional Data Structure: Series **Week 4**: Pandas Two-Dimensional Data Structure: DataFrame, Five Attributes of DataFrame **Week 5**: Checking Missing Values (NAN), DataFrame Value Extraction (Three Formats), Adding Columns/Rows **Week 6**: Using the `axis` Parameter to Assist with Function Calculations (mean, sum, max, min) **Week 7**: Filtering Data with Two Methods (Conditional Query, Query), Three DataFrame Merging Methods: concat, append, merge **Week 8**: Common DataFrame Functions, chp6-11. Handling Date Data Types with Functions **Week 9**: Midterm Exam **Week 10**: map, applymap, apply Loop Functions for Cell-Level Calculations, Export to Excel, Create Sheets **Week 11**: Group Analysis with groupby, Create Cross-Tabulation Tables/Pivot Tables **Week 12**: Multi-Level Row Indexing and Cross-Level Data Indexing **Week 13**: Five Types of Business Data Analysis **Week 14**: Various Data Cleaning Methods: String Processing, Regular Expressions, Date Handling **Week 15**: Various Data Cleaning Methods: Phone Processing, Email Processing, String Splitting **Week 16**: Case Study: Department Source Analysis **Week 17**: Case Study: Business Sales Analysis **Week 18**: Final Exam
Evaluation and grading policy	1.上課實作範例加分25%，作業分數25%，期中考25%，期末考25% 2.735教室不是電腦教室，是一般教室。要自己帶筆電。 1. **Class Practical Examples**: 25% bonus, **Assignments**: 25%, **Midterm Exam**: 25%, **Final Exam**: 25%. 2. **Room 735** is not a computer lab; it is a regular classroom. Students need to bring their own laptops.
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No 1.自編教學網站：https://acupun.site/lecture/pandas 2.教科書：一行指令學Python－用Pandas掌握商務大數據分析 ，作者： 徐聖訓 ，出版社：全華圖書 3.參考書目：Python商業數據分析：零售和電子商務案例詳解，作者： 零一，電子工業出版社 1. **Self-Developed Teaching Website**: [https://acupun.site/lecture/pandas](https://acupun.site/lecture/pandas) 2. **Textbook**: *Learn Python with One Command – Master Business Big Data Analysis with Pandas* - **Author**: Xu Sheng-Hsun - **Publisher**: Quan Hua Publishing 3. **Reference Book**: *Python Business Data Analysis: Detailed Cases on Retail and E-Commerce* - **Author**: Ling Yi - **Publisher**: Electronics Industry Press
The access to curricular consultation	1.可以在office hours時段來諮詢(科研大樓537室) 2.可以在北科i學園PLUS平台，在課程內留言，諮詢問題 3.老師的email：ccw@ntut.edu.tw，可以用email諮詢問題 3. The teacher's **email**: ccw@ntut.edu.tw — feel free to email for inquiries.
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth）
Note	建議同學們上課要繫帶筆記本電腦 因應疫情發展，本學期教學及授課方式請依照學校網頁所公布之訊息為準： (https://oaa.ntut.edu.tw/p/404-1008-98622.php?Lang=zh-tw) 若因疫情而暫停實體上課時，將使用學校的Microsoft Teams 課程名稱: 大數據分析 Students are advised to bring their laptops to class.