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, 2016 edition. New York: Springer (Available free online: http://faculty.marshall.usc.edu/gareth-james/ISL/index.html) * K. Hwang and M. Chen, Big-Data Analytics for Cloud, IoT and Cognitive Computing, 1st ed. Wiley Publishing, 2017.
彈性暨自主學習規劃 Alternative learning periods
本門課程是否有規劃實施學生彈性或自主學習內容（每1學分2小時）
Is any alternative learning periods planned for this course (with each credit corresponding to two hours of activity)?
否：教師需於「每週課程內容及預計進度」填寫18週課程進度（每1學分18小時之正課內容）。 No：The instructor will include an 18-week course plan in the weekly scheduled progress (each credit corresponds to 18 hours of instruction)
是：教師需於「每週課程內容及預計進度」填寫16週課程內容（每1學分16小時之正課內容），並於下列欄位填寫每1學分2小時學生彈性或自主學習內容。 Yes：The instructor will include a 16-week course plan in the weekly scheduled progress (each credit corresponds to 16 hours of instruction);the details of the planned alternative learning periods are provided below (each credit corresponds to two hours of activity).
學生彈性或自主學習活動 Alternative learning periods
勾選或填寫規劃內容 Place a check in the appropriate box or provide details
時數 Number of hours
學生分組實作及討論 Group work and discussion
參與課程相關作業、作品、實驗 Participation in course-related assignments, work, or experiments
參與校內外活動（研習營、工作坊、參訪）或競賽 Participation in on- or off-campus activities (e.g., seminars, workshops, and visits) or competitions
課外閱讀 Extracurricular reading
線上數位教材學習 Learning with online digital learning materials
其他（請填寫規劃內容） Other (please provide details)
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
2021/02/21~2021/02/27
Course overview and introduction
2
2021/02/28~2021/03/06
Introduction to Business Analytics
3
2021/03/07~2021/03/13
Data Management I
4
2021/03/14~2021/03/20
Data Management II
5
2021/03/21~2021/03/27
Data Management III
6
2021/03/28~2021/04/03
Fundamentals of Data Analytics I
7
2021/04/04~2021/04/10
Fundamentals of Data Analytics II
8
2021/04/11~2021/04/17
Fundamentals of Data Analytics III
9
2021/04/18~2021/04/24
Midterm Proposal Presentation
10
2021/04/25~2021/05/01
Statistical Learning-Introduction
11
2021/05/02~2021/05/08
Statistical Learning-Supervised Learning I
12
2021/05/09~2021/05/15
Statistical Learning-Supervised Learning II
13
2021/05/16~2021/05/22
Statistical Learning-Supervised Learning III
14
2021/05/23~2021/05/29
Statistical Learning-Unsupervised Learning
15
2021/05/30~2021/06/05
Statistical Learning-Prescriptive Analytics
16
2021/06/06~2021/06/12
Introduction to Scalable Data Analytics
17
2021/06/13~2021/06/19
Final Project Presentation I
18
2021/06/20~2021/06/26
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CM4083 時段2 Time period 2： 時間 Time：星期四10am-12pm 地點 Office/Laboratory：管CM4083
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.