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.
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.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| Course Participation and InteractionCourse Participation and Interaction Course Participation and Interaction |
30 | Students are required to attend class |
| AssignmentAssignment Assignment |
30 | |
| Final exam/presentationFinal exam/presentation Final exam/presentation |
40 |
