6145 - Business Analytics and AI Application 英授 Taught in English
Business Analytics and AI Application
教育目標 Course Target
By the end of this course, students will have a solid understanding of how business analytics and artificial intelligence are used in business scenarios. The course introduces Python as a practical tool for data processing and analysis, starting from basic syntax and gradually moving toward more applied use cases. Students will learn how to collect, clean, and organize data using common libraries such as Pandas and NumPy, and how to explore and visualize data to support decision making. The course also introduces the basic ideas behind machine learning and shows how AI models can be applied to business problems using tools like Scikit-learn. Throughout the course, students will practice interpreting results, understanding model limitations, and thinking critically about how data and AI techniques can be used responsibly in business settings.
By the end of this course, students will have a solid understanding of how business analytics and artificial intelligence are used in business scenarios. The course introduces Python as a practical tool for data processing and analysis, starting from basic syntax and gradually moving toward more applied use cases. Students will learn how to collect, clean, and organize data using common libraries such as Pandas and NumPy, and how to explore and visualize data to support decision making. The course also introduces the basic ideas behind machine learning and shows how AI models can be applied to business problems using tools like Scikit-learn. Throughout the course, students will practice interpreting results, understand model limitations, and thinking critically about how data and AI techniques can be used responsibly in business settings.
參考書目 Reference Books
1. AI-Powered Business Intelligence: Improving Forecasts and Decision Making with Machine Learning 1st Edition, Tobias Zwingmann (Author), 2022.
2. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition, William McKinney (Author), 2017.
1. AI-Powered Business Intelligence: Improving Forecasts and Decision Making with Machine Learning 1st Edition, Tobias Zwingmann (Author), 2022.
2. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition, William McKinney (Author), 2017.
評分方式 Grading
| 評分項目 Grading Method |
配分比例 Percentage |
說明 Description |
|---|---|---|
|
Class Participation Class Participation |
20 | |
|
Regular Assignments Regular Assignments |
25 | |
|
Midterm Exam Midterm Exam |
25 | |
|
Final Report Final Report |
30 |
授課大綱 Course Plan
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課程資訊 Course Information
基本資料 Basic Information
- 課程代碼 Course Code: 6145
- 學分 Credit: 0-3
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上課時間 Course Time:Monday/6,7,8[M023]
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授課教師 Teacher:黃尹姿
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修課班級 Class:國企碩學程1
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選課備註 Memo:English-Taught Course for All-Major Senior/Graduate Students
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