6121 - Business Analytics and AI Application 英授 Taught in English
Business Analytics and AI Application
教育目標 Course Target
Students in this course will develop a comprehensive understanding of business analysis and AI fundamentals, covering topics such as business requirement analysis, data collection, and machine learning. Through hands-on exercises, students will gain proficiency in Python programming, from basic syntax to advanced techniques essential for business data and AI projects. Additionally, students will acquire data analysis skills using tools like Pandas and NumPy for data cleaning, analysis, and visualization, aiding in informed business decision-making. They will also learn web data extraction techniques, including HTML, CSS, and Beautiful Soup. With an introduction to machine learning tools such as Scikit-Learn, participants will be equipped to comprehend, implement, and evaluate machine learning models. Lastly, ethical considerations in AI projects and risk management approaches in business analysis and AI will be explored to ensure responsible and effective application of these technologies.
Students in this course will develop a comprehensive understanding of business analysis and AI fundamentals, covering topics such as business requirement analysis, data collection, and machine learning. Through hands-on exercises, students will gain profitency in Python programming, from basic syntax to advanced techniques essential for business data and AI projects. Additionally, students will acquire data analysis skills using tools like Pandas and NumPy for data cleaning, analysis, and visualization, aiding in informed business decision-making. They will also learn web data extraction techniques, including HTML, CSS, and Beautiful Soup. With an introduction to machine learning tools such as Scikit-Learn, participants will be equipped to comprehensive, implement, and evaluate machine learning models. Lastly, ethical considerations in AI projects and risk management approaches in business analysis and AI will be explored to ensure responsible and effective application of these technologies.
參考書目 Reference Books
1. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition, William McKinney (Author), 2017.
2. AI-Powered Business Intelligence: Improving Forecasts and Decision Making with Machine Learning 1st Edition, Tobias Zwingmann (Author), 2022.
3. Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners 2nd, Al Sweigart (Author), 2015.
1. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition, William McKinney (Author), 2017.
2. AI-Powered Business Intelligence: Improving Forecasts and Decision Making with Machine Learning 1st Edition, Tobias Zwingmann (Author), 2022.
3. Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners 2nd, Al Sweigart (Author), 2015.
評分方式 Grading
評分項目 Grading Method |
配分比例 Percentage |
說明 Description |
---|---|---|
Course Participation and Interaction Course Participation and Interaction |
30 | Students are required to attend class |
Assignment Assignment |
30 | |
Final exam/presentation Final exam/presentation |
40 |
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: 6121
- 學分 Credit: 3-0
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上課時間 Course Time:Wednesday/6,7,8[M023]
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授課教師 Teacher:金泰星
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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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