本課程專為非資訊專業領域者設計，希望透過本課程，讓未曾深入接觸資料科學的人能夠對於數據分析方法建立正確清楚的基本觀念，並進一步地瞭解數據機器學習分析方法在商管領域的應用策略和相關實務案例。人工智慧與資料科學分析已成為萬眾矚目的焦點，每個領域都需要專精的數據分析人才，以及對商業有高度的敏銳度，找出企業問題協調組織面對，將能成為最有價值的企業高階經理人才。提升EMBA學生在人工智慧技術與巨量資料分析的技能與優勢，更可以讓學生將公司相關數據資料帶入課堂，達成學以致用的最佳發揮舞台。
內容涵蓋資料科學分析中資料的解理與如何收集，實際案例於電商顧客分群、預測電商潛在客戶、開府開放資料運用與視覺化、工廠製程優化、財務金融、零售行銷、社群媒體、文字分析、智慧製造等領域之發展與應用與策略布局。課程兼具學術理論與企業實務，以科技化、數位化的方式，將大數據分析的知識傳遞給所有需要的人。This course is designed for non-information professionals. I hope that through this course, people who have not been deeply involved in data science can establish correct and clear basic concepts about data analysis methods, and further understand the data machine learning analysis methods in the business management field. Application strategies and related practical cases. Artificial intelligence and data science analysis have become the focus of all publics. Each field requires specialized data analysis talents and a high degree of sensitivity to business to find out the corporate problem coordination aspects, which will become the most valuable. Senior management talent in the enterprise. Improving EMBA students’ skills and advantages in artificial intelligence technology and huge data analysis can also allow students to bring company-related data into the classroom, achieving the best stage to apply what they have learned.
The content covers the clarification and how to collect data in data scientific analysis. The actual cases are divided into e-commerce customer groups, predict e-commerce potential customers, open data usage and visualization, factory process optimization, financial and financial, retail marketing, social media, Development, application and strategic layout in fields such as text analysis and intelligent manufacturing. The course combines academic and corporate practice, and uses technological and digital methods to convey the knowledge of large-data analysis to everyone in need.
教科書：
講義
參考書籍: 資料科學的商業運用 碁峰書局
Textbook:
Talk
Reference books: Business use of data science Gefeng Bookstore
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 期中報告期中報告 Midterm Report |
30 | 期中專題企畫書 |
| 期末報告期末報告 Final report |
30 | 商業分析與AI智慧製造 期末專題報告 |
| 平時成績平時成績 Regular achievements |
40 | 課程參與討論與互動 商業分析與AI智慧製造 作業 |
