本課程目的在：
1. 帶領行政管理暨政策學系碩士班學生進入大數據、問題解決與決策分析的理論與實務殿堂，提供學生一場域，藉由R程式語言為工具、以議題為導向、結合跨領域專長的專題形式，學習有關大數據資料科學與循證(evidence-based)公共決策結合的基礎概念、與相關技術等，也為同學將來成為資料科學家做準備。
2. 幫助同學認識人工智慧基本原理與操作、以及在公共事務中之應用。
3. 藉由實地參訪，了解大數據與人工智慧目前在政府機關中的應用與未來規劃。
課程內容主題包括：大數據與資料分析（Big Data Analysis）、政府開放資料（Open Data）、R語言、人工智慧的概念與應用。The purpose of this course is to:
1. Lead students from the Department of Administrative Management and Policy to enter the theories and practice hall of large data, problem solving and decision analysis, and provide students with a field. Through the R program language as a tool, topic-oriented, and combining cross-domain specialist topics, learn basic concepts and related technologies that combine large data science and evidence-based public decision-making, and also prepare students to become data scientists.
2. Help students understand the basic principles and operations of artificial intelligence and their application in public affairs.
3. Through local visits, learn about the current application and future planning of large data and artificial intelligence in government agencies.
The course content topics include: Big Data and Data Analysis, Open Data, R language, concepts and applications of artificial intelligence.
1. R語言--邁向 Big Data之路 (最新版)。洪錦魁, 蔡桂宏，出版商:上奇資訊，出版日期:2017-06-18。
2. 中、英文書籍與期刊論文。
3. 人工智能基础(高中版)，陳玉琨、湯曉鷗(主編)，出版商：華東師範大學出版社，出版日期：2018-04-01。
1. R language-Mi's road to Big Data (latest version). Hong Jinkui, Cai Guihong, Publisher: Shangqi Information, Publication Date: 2017-06-18.
2. Chinese and English books and journals.
3. Basics of Artificial Intelligence (High School Edition), Chen Yukun, Tom Hokage (Editor), Publisher: Huadong Teachers University Press, Publication Date: 2018-04-01.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 上課參與和討論上課參與和討論 Participate in and discuss in class |
10 | |
| 平時讀書報告與作業平時讀書報告與作業 Reading reports and operations on the fly |
30 | |
| 個人專題1（口頭10%；書面20%）個人專題1（口頭10%；書面20%） Personal topic 1 (10% mouth; 20% book) |
30 | |
| 個人專題2（口頭10%；書面20%）個人專題2（口頭10%；書面20%） Personal topic 2 (10% mouth; 20% book) |
30 |
