115 學年度教育大數據微學程
115 Academic year Interdisciplinary micro program for Big Data in Education |
一、依重要相關事項，修滿下列科目達 10.0 學分，完成本學程
I、according to important notes, Complete 10.0 credits in the following subjects to complete the program |
二、課程明細：
II、Course lists: |
科目名稱
Course Title(Chinese) | 英文科目名稱
Course Title(English) | 科目代碼
Course Number | 學分
Credit | 年級
Department & Grade | 學期
Semester | 修別
Type | *先修科目或#背景科目
*Prerequisite course or #Background course | 備註
Remarks | 課群/學群
Course Group |
| 以下科目 7 選 2，至少需修習 5.0 學分(select 2 courses from 7 courses, at least 5.0 credits) |
| 初級程式設計-R語言 | Fundamental Computer Programming-R Language | GC__64640 | 2.0 | 一(Freshman) | 上(Fall) | 選(elective) | | 基礎課程foundational course | |
| 人工智慧概論 | Introduction to Artificial Intelligence | GC__67530 | 3.0 | 一(Freshman) | 上(Fall) | 選(elective) | | 基礎課程foundational course | |
| 物聯網、創客與教材設計 | IoT, Maker, and Teaching Materials Design | GC__65430 | 3.0 | 一(Freshman) | 下(Spring) | 選(elective) | | 基礎課程foundational course | |
| 人工智慧應用於教育數據分析 | Artificial Intelligence Applications in Education Data Analysis | SE__@0060 | 3.0 | 二(Sophomore) | 上(Fall) | 選(elective) | | 基礎課程foundational course | |
| 人工智慧 | Artificial Intelligence | IM__41600 | 3.0 | 三(Junior) | 下(Spring) | 選(elective) | | 基礎課程foundational course | |
| 人工智慧 | Artifical Intelligence | CSIE51600 | 3.0 | 三(Junior) | 上(Fall) | 選(elective) | | 基礎課程foundational course | |
| 以下科目 5 選 1，至少需修習 3.0 學分(select 1 course from 5 courses, at least 3.0 credits) |
| 資料庫管理 | Database Management | IM__20900 | 3.0 | 二(Sophomore) | 下(Spring) | 選(elective) | | 進階課程，建議至少修一門教育大數據微學程之基礎課程再選修。Advanced course -- it is recommended that students complete at least one foundational course in the Interdisciplinary Micro Program for Big Data in Education before enrolling. | |
| 大數據分析 | Big Data Analytics | CSIE59960 | 3.0 | 四(Senior) | 下(Spring) | 選(elective) | | 進階課程(研究所課程)advanced course(Graduate Level) | |
| 教育統計 | Educational Statistics | EAM_21200 | 3.0 | 二(Sophomore) | 下(Spring) | 選(elective) | | 進階課程advanced course | |
| 大數據與商業智慧 | Big Data and Business Intelligence | IM__50160 | 3.0 | 三(Junior) | 上(Fall) | 選(elective) | | 進階課程，建議至少修一門教育大數據微學程之基礎課程再選修。Advanced course — it is recommended that students complete at least one foundational course in the Interdisciplinary Micro Program for Big Data in Education before enrolling. | |
| 大數據分析導論 | Introduction to Big Data Analytics | CSIE35420 | 3.0 | 三(Junior) | 下(Spring) | 選(elective) | | 進階課程，建議至少修一門教育大數據微學程之基礎課程再選修。Advanced course — it is recommended that students complete at least one foundational course in the Interdisciplinary Micro Program for Big Data in Education before enrolling. | |
| 以下科目 2 選 1，至少需修習 1.0 學分(select 1 course from 2 courses, at least 1.0 credits) |
| 教育大數據產學合作與專題實作 | Industry-Academy Cooperation and Practical Project of the Big Data in Education | SE__11150 | 2.0 | 二(Sophomore) | 上(Fall) | 選(elective) | | 實務課程，建議先修習過任一門教育大數據微學程之基礎課程與進階課程Practical course — it is recommended that students complete at least one foundational course and one advanced course in the Interdisciplinary Micro Program for Big Data in Education before enrolling. | |
| 職涯探索與產業實習 | Introduction of Carcer planning and internships | IM__33610 | 1.0 | 三(Junior) | 下(Spring) | 選(elective) | | 實務課程practical course | |
三、重要相關事項：
III、Important notes:
一、應於畢業前修畢包含基礎課程5學分、進階課程3學分及實務課程1學分，至少修習10學分。
二、依本校「學士班學生程式設計能力畢業標準及實施辦法」實施，修畢資訊科技必修2學分或同等內容程度的2學分課程，得採認並抵免本微學程之基礎課程2學分。
三、修畢本校各系所開設之人工智慧相關3學分課程，得採認並抵免本微學程之基礎課程3學分。
四、採認並抵免本微學程之修畢本校各系所開設課程限基礎課程一門課程。
Interdisciplinary micro program for Big Data in Education
1. Students must complete at least 10 credits before graduation, including 5 credits in foundational courses, 3 credits in advanced courses, and 1 credit in practical courses.
2. In accordance with the university’s **"Graduation Standards and Implementation Measures for Programming Skills in Undergraduate Programs"**, students who complete 2 required credits in information technology or an equivalent 2-credit course may apply for credit recognition and exemption of 2 foundational course credits in this micro program.
3. Students who complete a 3-credit course related to artificial intelligence offered by any department of the university may apply for credit recognition and exemption of 3 foundational course credits in this micro program.
4. For courses not listed in this micro program to be accepted , students must apply to the offering department. Such course can be recognized only after review and approval ,with a limit of one course. |