本課程目標是讓學生能夠了解AI大數據相關技術與議題，包含數位匯流的概念與趨勢，探討不同領域之間異質資料整合的方法，以及生成式AI應用與介紹，數據資料倉儲到資料分析技術。並延伸介紹數據科學應用領域社群計算與社群媒體分析方法，並提供大數據應用實例探討模擬並進行小組專題製作，藉以加強學生在大數據時代的實作能力，讓學生實際接觸AI與大數據應用。
The goal of this course is to enable students to understand the technologies and issues related to AI big data, including the concepts and trends of digital convergence, exploring methods of integrating heterogeneous data between different fields, and the application and introduction of generative AI, from data warehousing to data analysis technology. . It also extends the introduction of social computing and social media analysis methods in the field of data science applications, provides big data application examples to discuss simulations, and conducts group project production, so as to strengthen students' practical ability in the big data era and allow students to have practical contact with AI and big data. Data applications.
1. [Coursera] 大數據分析：商業應用與策略管理 (Big Data Analytics: Business Applications and Strategic Decisions)
2. [Book] O'Neil, Cathy,Schutt, Rachel,Doing data science :Sebastopol, CA O'Reilly, 2014.
3. [Book] McKinney, Wes. Python資料分析 第三版 (Python for Data Analysis, 3rd Edition). Translated by 賴屹民, 歐萊禮出版社, 2023.
1. [Coursera] Big Data Analytics: Business Applications and Strategic Decisions
2. [Book] O'Neil, Cathy,Schutt, Rachel,Doing data science :Sebastopol, CA O'Reilly, 2014.
3. [Book] McKinney, Wes. Python for Data Analysis, 3rd Edition. Translated by Lai Yimin, O'Reilly Publishing, 2023.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 平時成績(出席、參與及作業)平時成績(出席、參與及作業) Daily results (attendance, participation and homework) |
40 | |
| 期末論文報告/專題發表期末論文報告/專題發表 Final thesis report/topic presentation |
60 |
