Introduction to Big Data Analytics, Fall 2026

This course offers an introduction to the principles and concepts in big data analytics (BDA), which is gaining more popularity in recent years.
The major topics covered in this course will include: frequent pattern mining, classification, clustering, and distributed computing.
The course is offered at sophomore level.

Notes on Online Courses

All students enrolled in this course have been added to the team in Microsoft Teams for the corresponding course number.
You can attend the online course in Microsoft Teams at the following channel: Team created for Big Data Analytics Course [Course Number: 365939]

Course Information

Instructor: Jenq-Haur Wang
E-mail: mailbox at the domain of our university, and my username is jhwang
Office: R1534, Technology Building (ext. 4238)
Office Hours: Tue. and Thu. 9:10-12:00am
Class Hours: Mon. 10:10-12:00am, Tue. 13:10-14:00pm, R235/R234, Technology Building
TA: Mr. Wang (R1424, Technology Building)
E-mail: (TBA)
Prerequisite: data structures (and algorithms), database systems, discrete math (probability), operating systems, and working experience of high-level programming languages
Textbook: (Selected chapters)
- Jiawei Han, Jian Pei, and Hanghang Tong, Data Mining: Concepts and Techniques, 4th ed., Morgan Kaufmann Publishers, Oct. 2022. ([DM4])
References:
- Tom White, Hadoop: The Definitive Guide, 4th ed., O'Reilly Media, 2015.
- Jimmy Lin and Chris Dyer, Data-Intensive Text Processing with MapReduce, Morgan & Claypool Publishers, 2010.
- Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Mining of Massive Datasets, 3rd Edition, Cambridge University Press, Feb. 2020.
- Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee, Learning Spark: Lightning-Fast Big Data Analysis, 2nd ed., O'Reilly Media, Jul. 2020.
- Others: online documents (Hadoop, Spark), tutorials, and selected academic papers.

Latest News

Sep. 3, 2026: The homepage for Introduction to Big Data Analytics (Fall 2026) has been setup.

(Tentative) Schedule

* All slides can be downloaded at the iSchool+ plaform in NTUT.

Week	Date	Content	Reading	Note
1	Sep. 7 & 8, 2026	Course Overview Ch.1, Introduction	DM4, Ch.1
2	Sep. 14 & 15, 2026	(Leave for Czech project)
3	Sep. 21 & 22, 2026	(Leave for Czech project)
4	Sep. 28 & 29, 2026	(9/28: Teachers' Day) Ch.2, Data, Measurements, and Data Preprocessing	DM4, Ch.2
5	Oct. 5 & 6, 2026	Ch.4, Pattern Mining: Basic Concepts and Methods	DM4, Ch.4	Term Project Proposal HW#1
6	Oct. 12 & 13, 2026	Ch.4		Team Registration
7	Oct. 19 & 20, 2026	Ch.6, Classification: Basic Concepts	DM4, Ch.6	Due: HW#1 HW#2 Due: Team Registration
8	Oct. 26 & 27, 2026	Ch.6 Ch.5, Pattern Mining: Advanced Methods	DM4, Ch.5 (selected sections)
9	Nov. 2 & 3, 2026	Ch.8, Cluster Analysis: Basic Concepts and Methods	DM4, Ch.8	HW#3 Due: HW#2
10	Nov. 9 & 10, 2026	Ch.8 Ch.7, Classification: Advanced Methods	DM4, Ch.7 (selected sections)
11	Nov. 16 & 17, 2026	(Midterm Exam)		Due: HW#3
12	Nov. 23 & 24, 2026	Ch.9, Cluter Analysis: Advanced Methods	DM4, Ch.9 (selected sections)	Due: Proposal
13	Nov. 30 & Dec. 1, 2026	Distribtued Platforms: Hadoop, Spark		HW#4
14	Dec. 7 & 8, 2026	MapReduce Programming Spark Programming
15	Dec. 14 & 15, 2026	Term Project Presentation: Week 1		Due: HW#4
16	Dec. 21 & 22, 2026	Term Project Presentation: Week 2
17	Dec. 28 & 29, 2026	Flexible Learning (Term Project Presentation: Week 3 -- Online)
18	Jan. 4 & 5, 2027	Flexible Learning (Term Project Presentation: Week 4 -- Online)

Programming Assignments and Projects

Please hand in your assignment before deadline according to the following instructions.

Submission Instructions

NOTE: Programs or projects in electronic files must be submitted directly to i-School+.

If you cannot successfully submit your work, please contact with the TA or the instructor.

Homeworks

There will be several written assignments and programming exercises that target at different data analysis tasks.

HW#1 : Ch.2 Data, Measurements, and Data Preprocessing [DM4]
HW#2 : Ch.4 Pattern Mining: Basic Concepts and Methods [DM4]
HW#3 : Ch.6-7 Classification [DM4]
HW#4 : Spark Programming

Projects

Term Project

Item	Description	Time
Proposal	You are required to submit a proposal for term project one week after midterm exam.	Nov. 23, 2026 (Mon.)
Topics	Two options: Project for data analysis or related system development Joining competitions as your term project. You can check the details on recent competitions as potential topics for term project.
Schedule	Due to our time limits, we have to start the term project presentation as early as Dec. 14, 2026 (Mon.). * [NOTE] All presentations must be finished within the scheduled time slots, which will be the last 4 weeks in this semester. No other time slots will be avbailable.	Dec. 14, 15, 21, 22, 28, 29, 2026 & Jan. 4, 5, 2027
Report	Each team is required to upload the final report after finishing your presentation. The final report should contain at least the following: presentation slides, and source code, and documents containing installation/execution instructions, team members and task responsibility	Jan. 8, 2027 (Fri.)

Exams

Midterm Exam: Nov. 2-6, 2026
- Date: Nov. 16, 2026 (Mon.)
- Time: 10:10-12:00am
- Location: R235, Technology Building
- Range: Ch. 2, 4, 6, 8 (TBD)
Final Exam: Dec. 18-24, 2026
- Note: There will be no final exam in this course. Instead, you are required to finish a term project for system development or data analysis.

Scores

Please check the homework submission site for more details.

E-mail: jhwang AT ntut . edu . tw
Created: Sep. 3, 2026.
Last Updated: Sep. 8, 2026.