IERG4300/ ESTR4300 Web-Scale Information Analytics / Fall 2023


  • More additional materials for Gradient Descent and Mathematics Pre-requisite [Strange10: Linear Algebra] are posted to the reference.

  • Released: [Homework 4]. Due: Sat, December 9, 11:59 PM.
  • Released: [Homework 3]. Due: Mon, November 20, 11:59 PM.
  • Released: [Homework 2]. Due: Mon, October 30, 11:59 PM.
  • IE DIC is now available for your homework. Detailed information refer to Here.

  • You can find the useful URLs and paths for completing HW#1 on IE DIC Here.

  • Tips on running MapReduce Jobs within DIC resource limits are posted Here. Otherwise you may encounter tasks being killed/ getting stuck/ running extremely slow or disk full while doing HW#1.

  • Released: [Homework 1]. Due: Mon, October 9, 11:59 PM.
  • The time and venue of ESTR4300 are released. Please check the details in the course homepage.

  • Please select your available time slots for ESTR4300 lecture time through this link if you are interested. It will be an additional 1-hour weekly lecture.

  • The due date for HW0 is strict for all. Late-add student will NOT be granted extra time extension for submission.

  • Released: [Homework 0]. Due: Sun, September 17, 05:59 PM.
  • Website account: bigdata, password: Fall2023bigdata

Course Description

The course discusses data-intensive analytics, and automated processing of very large amount of structured and unstructured information. We focus on leveraging the MapReduce and other related paradigms to create parallel algorithms that can be scaled up to handle massive data sets such as those collected from the World Wide Web or other Internet systems and applications. We organize the course around a list of large-scale data analytic problems in practice. The required theories and methodologies for tackling each problem will be introduced. As such, the course only expects students to have solid knowledge in probability, statistics, linear algebra and computer programming skills. Topics to be covered include:

  • The MapReduce computational model and its system architecture and realization in practice;
  • Finding Frequent Item-sets and Association Rules ; Finding Similar Items in high-dimensional data;
  • Dimensionality Reduction techniques ; Clustering ; Recommendation systems;
  • Analysis of Massive Graphs and its applications on the World Wide Web;
  • Large-scale supervised machine learning;
  • Processing and mining of Data Streams and their applications on large-scale network/ online-activity monitoring.

Please check Blackboard for important announcements, assignment submissions, grades, etc.

Course Assessment

  • Homework (5 sets in total): 65%
  • Final Exam: 35%

Student/Faculty Expectations on Teaching and Learning

Academic Honesty

You are expected to do your own work and acknowledge the use of anyone else’s words or ideas. You MUST put down in your submitted work the names of people with whom you have had discussions.

Refer to for details

When scholastic dishonesty is suspected, the matter will be turned over to the University authority for action.

You MUST include the following signed statement in all of your submitted homework, project assignments and examinations. Submission without a signed statement will not be graded.

I declare that the assignment here submitted is original except for source material explicitly acknowledged, and that the same or related material has not been previously submitted for another course. I also acknowledge that I am aware of University policy and regulations on honesty in academic work, and of the disciplinary guidelines and procedures applicable to breaches of such policy and regulations, as contained in the website

Academic Honesty Slides from Associate Dean of Faculty of Engineering

Large Language Models (LLMs) Policy

You are NOT allowed to use any LLMs (e.g., ChatGPT, Claude etc.) in this course. Anyone who uses LLMs for completing the homework will be treated as cheating.

Previous Offerings

Time and Venue

  • Tue 09:30AM - 10:15AM
    SC_L3 @ Science Centre
  • Wed 11:30AM - 01:15PM
    SC_L5 @ Science Centre
  • Tue 11:30AM - 01:15PM (ESTR4300. Starting from Sept 19.)
    LHC G06 @ Y C Liang Hall
  • Tue 10:30AM - 11:15AM
    SC_L3 @ Science Centre


Email: wclau [at]

Office hours: TBD (SHB 818)

Teaching Assistants

Siyue Xie

Email: xiesiyue [at]

Office hours: Thu 9:30 - 10:30 AM (SHB 803)

Kaixuan Luo

Email: luokaixuan [at]

Office hours: Wed 3:00 - 4:00 PM (SHB 803)