國立中山大學 108學年度第2學期 課程教學大綱

National Sun Yat-sen University 108Academic year Course syllabus

中文名稱
Course name(Chinese)

商業分析實務

課號
Course Code

MIS985

英文名稱
Course name(English)

PRACTICAL BUSINESS ANALYTICS

課程類別
Type of the course

講授類

必選修
Required/Selected

必修

系所
Dept./faculty

電子商務與商業分析數位學習碩士在職專班

授課教師
Instructor

康藝晃    

學分
Credit

3

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,授課方式調整如下:Since COVID-19, if distance learning is necessary, the teaching methods would adjust as follows:

         尚未建立傳染性肺炎(武漢肺炎)授課方式調整

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.


授課方式 Teaching methods

         * Online Lecture
* On-site Group Discussion


評分方式﹝評分標準及比例﹞Evaluation (Criteria and ratio)等第制單科成績對照表 letter grading reference

        
1.Quiz30%
2.Homework30%
3.Midterm20%
4.Final Project20%

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.

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
WeekDateContent and topic
12020/02/27~2020/02/29No class
22020/03/01~2020/03/07Course overview and introduction
32020/03/08~2020/03/14Introduction to Data Management-I
42020/03/15~2020/03/21Introduction to Data Management-II
52020/03/22~2020/03/28Fundamentals of Data Analytics-I
62020/03/29~2020/04/04Fundamentals of Data Analytics-II
72020/04/05~2020/04/11Midterm Proposal Presentation
82020/04/12~2020/04/18Fundamentals of Data Analytics-III
92020/04/19~2020/04/25High-performance Programming-I
102020/04/26~2020/05/02High-performance Programming-II
112020/05/03~2020/05/09Introduction to Predictive Modeling-I
122020/05/10~2020/05/16Introduction to Predictive Modeling-II
132020/05/17~2020/05/23Introduction to Predictive Modeling-III
142020/05/24~2020/05/30Introduction to Big Data Analytics
152020/05/31~2020/06/06Scalable Data Analytics with R, Spark, and H2O-I
162020/06/07~2020/06/13Scalable Data Analytics with R, Spark, and H2O-II
172020/06/14~2020/06/20Final Project Presentation
182020/06/21~2020/06/24Final Project Presentation

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.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            

本課程與SDGs相關項目:The course relates to SDGs items:

         尚未建立SDGS資料

本課程校外實習資訊: This course is relevant to internship:

         本課程無註記包含校外實習

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