Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	366648	Big Data Analytics	1	3.0	3	★	Jenq-Haur Wang Cheng, Li-Chen	IMBA GIFM	19	0	資財所和IMBA合開

Course Plan：

Instructor	Jenq-Haur Wang　Office Hours
Email	jhwang@ntut.edu.tw
Last Updated	2026-06-03 12:38:11
Course Objective	This course introduces the methods and applications of big data analytics. The topics include big data management, data mining techniques, big data platforms, and project implementation and presentation. Students are expected to learn the concepts of big data analytics techniques, and their applications and future trends in different case studies.
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) Week #8: Midterm exam (1 wk) Note: I'm responsible for the first part in 6 weeks (Week 2-7).
Evaluation and grading policy	For Part I: - Homework assignments: ~60% - Midterm exam: ~40% The percentage of your score in Part I will be around 1/3 of the total score. It's roughly related to the number of weeks, and will be confirmed with Prof. Cheng.
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, 4th edition, Morgan Kaufmann, 2022. - 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.
The access to curricular consultation	The teacher can be reached via e-mail or messages in Microsoft Teams. The TA will help you join the course group in Facebook where you can also ask questions about the homeworks and exams.
Expanding teaching and resources	● 混成學習 (Blended Learning) 課程結合實體授課與線上學習（同步視訊／非同步活動）。 Combines face-to-face instruction with online learning (including synchronous video sessions or asynchronous activities). ● 數位科技融入 (Digital Technology Integration) 運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。 Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment.
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note	All course sessions will be recorded whenever possible, and put on Microsoft Teams and iSchool+, along with all course materials, homework announcements and submissions. Latest news available soon at: https://chriswjh.github.io/BDA_IFM/ Note: Flexible Learning will be conducted based on project implementation and discussions in groups. Details should be available soon by Prof. Cheng.