Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2018 248448 Big Data Analytics 1 3.0 3 ▲ Jenq-Haur Wang
Chen-Shu Wang IMFI 14 0 Required Course

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2018	248448	Big Data Analytics	1	3.0	3	▲	Jenq-Haur Wang Chen-Shu Wang	IMFI	14	0	Required Course

Course Plan：

Instructor Jenq-Haur Wang

Email jhwang@ntut.edu.tw

Last Updated 2018-06-11 11:24:50

Course Objective 本課程介紹大數據在各個領域決策制定的應用，課程內容包括大數據分析與應用、大數據管理、大數據分法與策略、資料探勘介紹、資料探勘方法、大數據平台、大數據行銷實務、大數據個案分析、期末專題製作與報告。學生可以經由多個案例學習大數據的應用實務、目前技術與未來發展方向。

Course Schedule Part I: Data mining concepts (6 wks) 1. Introduction to big data concepts & data preprocessing (1 wk) 2. Frequent pattern mining (2 wks) 3. Data classification (2 wks) 4. Data clustering (1 wk) Midterm exam (1 wk) Part II: Distributed processing (5 wks) 1. Introduction to distributed platforms: Hadoop & Spark (1 wk) 2. Configuration & setup: Hadoop & Spark (1 wk) 3. MapReduce programming: relational algebra, pairs & stripes (1 wk) 4. Case study of Spark programming: frequent itemset, classification, clustering (2 wks)

Evaluation and grading policy Homework assignments and programming exercises: ~30-35% Mid-term exam: ~25-30%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Textbook: Jiawei Han, Micheline Kamber, and Jian Pei, Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011. References: 1. Jimmy Lin and Chris Dyer, Data-Intensive Text Processing with MapReduce, Morgan & Claypool Publishers, 2010. 2. Holden Karau, andy Konwinski, Patrick Wendell, and Matei Zaharia, Learning Spark: Lightning-fast data analysis, O'Reilly, 2015. 3. Recent academic papers, tutorials, and slides. 4. Latest news available soon on: http://www.ntut.edu.tw/~jhwang/IMFI-BDA/

The access to curricular consultation

Note
　

Instructor	Jenq-Haur Wang
Email	jhwang@ntut.edu.tw
Last Updated	2018-06-11 11:24:50
Course Objective	本課程介紹大數據在各個領域決策制定的應用，課程內容包括大數據分析與應用、大數據管理、大數據分法與策略、資料探勘介紹、資料探勘方法、大數據平台、大數據行銷實務、大數據個案分析、期末專題製作與報告。學生可以經由多個案例學習大數據的應用實務、目前技術與未來發展方向。
Course Schedule	Part I: Data mining concepts (6 wks) 1. Introduction to big data concepts & data preprocessing (1 wk) 2. Frequent pattern mining (2 wks) 3. Data classification (2 wks) 4. Data clustering (1 wk) Midterm exam (1 wk) Part II: Distributed processing (5 wks) 1. Introduction to distributed platforms: Hadoop & Spark (1 wk) 2. Configuration & setup: Hadoop & Spark (1 wk) 3. MapReduce programming: relational algebra, pairs & stripes (1 wk) 4. Case study of Spark programming: frequent itemset, classification, clustering (2 wks)
Evaluation and grading policy	Homework assignments and programming exercises: ~30-35% Mid-term exam: ~25-30%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Textbook: Jiawei Han, Micheline Kamber, and Jian Pei, Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011. References: 1. Jimmy Lin and Chris Dyer, Data-Intensive Text Processing with MapReduce, Morgan & Claypool Publishers, 2010. 2. Holden Karau, andy Konwinski, Patrick Wendell, and Matei Zaharia, Learning Spark: Lightning-fast data analysis, O'Reilly, 2015. 3. Recent academic papers, tutorials, and slides. 4. Latest news available soon on: http://www.ntut.edu.tw/~jhwang/IMFI-BDA/
The access to curricular consultation
Note