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:
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.
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
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.
每週課程內容及預計進度 Weekly scheduled progress
Content and topic
Course overview and introduction
Introduction to Business Analytics
Data Management I
Data Management II
Data Management III
Fundamentals of Data Analytics I
Fundamentals of Data Analytics II
Fundamentals of Data Analytics III
Midterm Proposal Presentation
Statistical Learning-Supervised Learning I
Statistical Learning-Supervised Learning II
Statistical Learning-Supervised Learning III
Statistical Learning-Unsupervised Learning
Statistical Learning-Prescriptive Analytics
Introduction to Scalable Data Analytics
Final Project Presentation I
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.
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.
參與課程規劃之校內外活動及競賽 Participate in off-campus/ on-campus activities and competitions.