因應嚴重特殊傳染性肺炎(武漢肺炎)，倘若後續需實施遠距授課，評分方式調整如下：Since COVID-19, if distance learning is necessary, the evaluation would adjust as follows:
尚未建立傳染性肺炎(武漢肺炎)課程評分方式﹝評分標準及比例﹞
課程大綱 Course syllabus
本課程教學大綱已提供完整英文資訊（本選項僅供統計使用，未提供完整英文資訊者，得免勾記）【Provide information of course syllabus in English.(This is for statistical use only. For those who do not provide information of course syllabus in English, do not check this field.)】
This is an advanced analytics course of NSYSU master’s degree program in Business Analytics. Students will be introduced to the latest business data analytics tools and techniques, which include, but are not limited to, R, Apache Hadoop, Apache Spark, and H2O. In this course, we will be discussing the latest business analytics concepts and techniques along with advances of Data Science and its applications in various business fields. Background in related studies including probability & statistics, marketing research, predictive analytics, data engineering, credit risk modeling, and statistical learning, will also be reviewed.
課程目標 Objectives
Prerequisite: Basic understanding of relational databases, SQL, data structures, probability & statistics, college-level calculus, and matrix operations are required. Familiar with at least one high-level programming language. Scientific programming language, such as R, MATLAB, Python, SAS, Julia are preferred.
參考書/教科書/閱讀文獻 Reference book/ textbook/ documents
〔請遵守智慧財產權觀念，不可非法影印。教師所提供之教材供學生本人自修學習使用,不得散播及做為商業用途〕
No copies for intellectual property rights. Textbooks provided by the instructor used only for self-study, can not broadcast or commercial use
* F. Provost, T. Fawcett. Data Science for Business: What you need to know about data mining and data-analytic thinking”, O'Reilly Media, Inc., 2013. * C. O'Neil and R. Schutt, Doing Data Science: Straight Talk from the Frontline,. O'Reilly Media, 2013 * G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning: with Applications in R, 2nd edition. New York: Springer (Available free online: https://www.statlearning.com/) * K. Hwang and M. Chen, Big-Data Analytics for Cloud, IoT and Cognitive Computing, 1st ed. Wiley Publishing, 2017.
每週課程內容及預計進度 Weekly scheduled progress
週次
日期
授課內容及主題
Week
Date
Content and topic
1
2022/02/13~2022/02/19
Course overview and introduction
2
2022/02/20~2022/02/26
Introduction to Business Analytics
3
2022/02/27~2022/03/05
Data Management I
4
2022/03/06~2022/03/12
Data Management II
5
2022/03/13~2022/03/19
Data Management III
6
2022/03/20~2022/03/26
Fundamentals of Data Analytics I
7
2022/03/27~2022/04/02
Fundamentals of Data Analytics II
8
2022/04/03~2022/04/09
Fundamentals of Data Analytics III
9
2022/04/10~2022/04/16
Midterm Proposal Presentation
10
2022/04/17~2022/04/23
Statistical Learning-Introduction
11
2022/04/24~2022/04/30
Statistical Learning-Supervised Learning I
12
2022/05/01~2022/05/07
Statistical Learning-Supervised Learning II
13
2022/05/08~2022/05/14
Statistical Learning-Supervised Learning III
14
2022/05/15~2022/05/21
Statistical Learning-Unsupervised Learning
15
2022/05/22~2022/05/28
Statistical Learning-Prescriptive Analytics
16
2022/05/29~2022/06/04
Introduction to Scalable Data Analytics
17
2022/06/05~2022/06/11
Final Project Presentation I
18
2022/06/12~2022/06/18
Final Project Presentation II
課業討論時間 Office hours
時段1 Time period 1: 時間 Time：星期三10am-12pm 地點 Office/Laboratory：管CM4057 時段2 Time period 2： 時間 Time：星期四10am-12pm 地點 Office/Laboratory：管CM4057
系所學生專業能力/全校學生基本素養與核心能力 basic disciplines and core capabilitics of the dcpartment and the university
系所學生專業能力/全校學生基本素養與核心能力 basic disciplines and core capabilities of the department and the university
課堂活動與評量方式 Class activities and evaluation
本課程欲培養之能力與素養 This course enables students to achieve.
紙筆考試或測驗 Test.
課堂討論︵含個案討論︶ Group discussion (case analysis).
個人書面報告、作業、作品、實驗 Indivisual paper report/ assignment/ work or experiment.
群組書面報告、作業、作品、實驗 Group paper report/ assignment/ work or experiment.
個人口頭報告 Indivisual oral presentation.
群組口頭報告 Group oral presentation.
課程規劃之校外參訪及實習 Off-campus visit and intership.
證照/檢定 License.
參與課程規劃之校內外活動及競賽 Participate in off-campus/ on-campus activities and competitions.
課外閱讀 Outside reading.
※系所學生專業能力 Basic disciplines and core capabilities of the department
1.具備資訊倫理與社會責任實踐的能力。 1. Awareness of information ethics.
2. 具備溝通能力。 2. Communication Skills.
3. 具備解決資訊管理問題之能力。 3. Capabilities to solve IT-related problems.
4. 具備資訊管理之專業知識。 4. Professional knowledge of information technology.
5. 具備國際觀。 5. Global perspective.
※全校學生基本素養與核心能力 Basic disciplines and core capabilities of the university
1.表達與溝通能力。 1. Articulation and communication skills
2.探究與批判思考能力。 2. Inquisitive and critical thinking abilities
3.終身學習能力。 3. Lifelong learning
4.倫理與社會責任。 4. Ethnics and social responsibility
5.美感品味。 5. Aesthetic appreciation
6.創造力。 6. Creativity
7.全球視野。 7. Global perspective
8.合作與領導能力。 8. Team work and leadership
9.山海胸襟與自然情懷。 9. Broad-mindedness and the embrace of nature
Internship: The required or elective courses should include credits and learning hours. Students should participate in the corporative company or institution to practice and learn the real skills. An internship certification must be handed in at the end of internship to get the credits or to fulfil the graduation requirements.