Introduction to Big Data Analytics, Fall 2022

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: data mining, distributed computing, and parallel programming.
The course is offered at undergraduate 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: 308849]

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 Fri. 9:10-12:00am
Class Hours: Mon. 13:10-15:00pm, Wed. 15:10-16:00pm, R427, 6th Teaching Building
TA: Mr. Chuang (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, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann Publishers, July 2011. ([DM3])
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. 11, 2022: The homepage for Introduction to Big Data Analytics (Fall 2022) has been setup.

(Tentative) Schedule

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

Week	Date	Content	Reading	Note
1	Sep. 12 & 14, 2022	Course Overview
2	Sep. 19 & 21, 2022	Introduction to Big Data Analytics Ch.2, Getting to Know Your Data	DM3, Ch.1 DM3, Ch.2
3	Sep. 26 & 28, 2022	Ch.3, Data Preprocessing	DM3, Ch.3 (selected)
4	Oct. 3 & 5, 2022	Ch.3		HW#1
5	Oct. 10 & 12, 2022	(Compensation Leave for National Day) Ch.6, Frequent Pattern Mining	DM3, Ch.6	Term Project Proposal
6	Oct. 17 & 19, 2022	Ch.8, Classification: Basic Concepts	DM3, Ch.8	Due: HW#1 Team Registration
7	Oct. 24 & 26, 2022	Ch.8		HW#2 Due: Team Registration
8	Oct. 31 & Nov. 2, 2022	Ch.9, Classification: Advanced Methods	DM3, Ch.9 (selected sections)
9	Nov. 7 & 9, 2022	Ch.10, Cluster Analysis: Basic Concepts and Methods	DM3, Ch.10	HW#3 Due: HW#2
10	Nov. 14 & 16, 2022	(Midterm Exam)
11	Nov. 21 & 23, 2022	Distribtued Platforms: Hadoop, Spark Ref: Notes on installation, configuration, and management of Hadoop & Spark clusters		Due: HW#3 Due: Proposal
12	Nov. 28 & 30, 2022	Parallel Programming Paradigms & Concepts
13	Dec. 5 & 7, 2022	MapReduce Programming (Lab: Spark cluster demo)		HW#4
14	Dec. 12 & 14, 2022	Spark Programming (Lab: classification using Spark)
15	Dec. 19 & 21, 2022	(Leave for IEEE BigData 2022) Term Project Presentation (Week 1)		Due: HW#4
16	Dec. 26 & 28, 2022	(12/26: Leave for IEEE BigData 2022) Term Project Presentation (Week 2)
17	Jan. 2 & 4, 2023	Term Project Presentation (Week 3)
18	Jan. 9 & 11, 2023	Term Project Presentation (Week 4)

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.2-3 Data Preprocessing
HW#2 : Ch.6 Frequent Pattern Mining
HW#3 : Ch.8-9 Classification
HW#4 : MepReduce Programming

Projects

Term Project

Item	Description	Time
Proposal	You are required to submit a proposal for term project one week after midterm exam.	Nov. 21, 2022 (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. 19, 2022 (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. 19, 21, 26, 28, 2022 & Jan. 2, 4, 9, 11, 2023
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. 13, 2023 (Fri.)

Exams

Midterm Exam: Nov. 7-11, 2022
- Date: Nov. 14, 2022 (Mon.)
- Time: 13:10-15:00pm
- Location: R427, 6th Teaching Building
- Range: Ch. 2, 3, 6, 8, 9, 10 (TBD)
Final Exam: Jan. 9-13, 2023
- Note: There will be no final exam in this course. Instead, you are required to finish a term project for system development or paper presentation.

Scores

Please check the homework submission site for more details.

E-mail: jhwang AT ntut . edu . tw
Created: Sep. 11, 2022.
Last Updated: Nov. 23, 2022.