Students

COMP3210 – Big Data

2026 – Session 1, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Convenor, Lecturer
Yanqiu Wu
yanqiu.wu@mq.edu.au
Lecturer
Qiongkai Xu
qiongkai.xu@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
130cp at 1000 level or above including COMP2200
Corequisites Corequisites
Co-badged status Co-badged status
COMP6210
Unit description Unit description

Even simple tasks like counting elements can seem impossible when the amount of data to process is huge. This unit explores some of the key aspects related to processing and mining information from large volumes of data. We present technology commonly used in industry such as map-reduce, and show how a range of data processing methods can be realised using map-reduce. Special emphasis will be placed in the adaptation of data mining techniques for large volumes of data and for data streaming.

Learning in this unit enhances student understanding of global challenges identified by the United Nations Sustainable Development Goals (UNSDGs) Industry, Innovation and Infrastructure

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Explain the key Big Data concepts and techniques.
  • ULO2: Apply techniques for storing large volumes of data.
  • ULO3: Apply Map-reduce techniques to a number of problems that involve Big Data.
  • ULO4: Apply techniques for handling high-dimensional big data.

General Assessment Information

Requirements to Pass this Unit

To pass this unit you must:

  • Achieve a total mark equal to or greater than 50%.

Release Dates

  • Assignment 1 – To be released no later than 30th March.
  • Assignment 2 – To be released no later than 1st May.

Late submission Policy

  • 5% penalty per day: If you submit your assessment late, 5% of the total possible marks will be deducted for each day (including weekends), up to 7 days. o
    • Example 1 (out of 100): If you score 85/100 but submit 20 hours late, you will lose 5 marks and receive 80/100.
    • Example 2 (out of 30): If you score 27/30 but submit 1 day late, you will lose 1.5 marks and receive 25.5/30.
  • After 7 days: Submissions more than 7 days late will receive a mark of 0.
  • Extensions:
    • Short Extension: Some assessments are eligible for a short extension. You can only apply for a short extension before the due date.
    • Special Consideration: If you need more time due to serious issues and for any assessments that are not eligible for Short Extension, you must apply for Special Consideration.

Need help? Review the Special Consideration page HERE

Assessments where Late Submissions will be accepted 

  • Assignment 1 – YES, Standard Late Penalty applies 
  • Assignment 2 – YES, Standard Late Penalty applies 

Special Consideration

The Special Consideration Policy aims to support students who have been impacted by short-term circumstances or events that are serious, unavoidable, and significantly disruptive, and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the assessments in this unit on time, please inform the convenor and submit a Special Consideration request through http://connect.mq.edu.au/.

Details for each assignment will be available via iLearn

You are encouraged to:

  • Set your personal deadline earlier than the actual one
  • Keep backups of all your important files
  • Ensure that no-one else picks up your printouts

Assessment Tasks

Name Weighting Hurdle Due Groupwork/Individual Short Extension AI Approach
Assignment 1 30% No 02/05/2026 Individual Yes Open
Assignment 2 40% No 23:55, 06/06/2026 Individual and Group No Open
Final Exam 30% No Examination period Individual No Observed

Assignment 1

Assessment Type 1: Experiential task
Indicative Time on Task 2: 30 hours
Due: 02/05/2026
Weighting: 30%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open

In this assignment, you will implement and discuss scalable algorithms (e.g., using Hadoop and MapReduce) to process Big Data.
On successful completion you will be able to:
  • Explain the key Big Data concepts and techniques.
  • Apply techniques for storing large volumes of data.
  • Apply Map-reduce techniques to a number of problems that involve Big Data.

Assignment 2

Assessment Type 1: Experiential task
Indicative Time on Task 2: 30 hours
Due: 23:55, 06/06/2026
Weighting: 40%
Groupwork/Individual: Individual and Group
Short extension 3: No
AI Approach: Open

In this group assignment, you will  write a project plan, implement big data processing algorithms, and present the results of your work.
On successful completion you will be able to:
  • Explain the key Big Data concepts and techniques.
  • Apply techniques for storing large volumes of data.
  • Apply Map-reduce techniques to a number of problems that involve Big Data.
  • Apply techniques for handling high-dimensional big data.

Final Exam

Assessment Type 1: Examination
Indicative Time on Task 2: 15 hours
Due: Examination period
Weighting: 30%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Observed

The final exam will focus on the theoretical aspects of the unit, including algorithms and implementation issues.
On successful completion you will be able to:
  • Explain the key Big Data concepts and techniques.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • Academic Success for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation.

3 An automatic short extension is available for some assessments. Apply through the Service Connect Portal.

Delivery and Resources

Classes

Basically, each week has two hours of lectures and two hours of workshops. Lectures will start in week 1. Workshops will start in week 1. For details of days, times and rooms consult the timetables webpage.

Required and Recommended Texts

Some of the contents of the unit will be based on the following books:

  • J. Leskovec, A. Rajaraman, J. Ullman, Mining of Massive Datasets. The book is free and available from http://www.mmds.org/, where you can also find links to a MOOC, slides, and videos.
  • C.Coronel, S. Morris. Database Systems: Design, Implementation and Management. 13th edition. Chapter 14 is the most relevant chapter. This chapter will be made available to students attending the classes.

Additional material including lecture notes will be made available during the semester. See the unit schedule for a listing of the most relevant reading for each week.

Technology Used and Required

The following software is used in COMP3210:

This software is installed in the labs; you should also ensure that you have working copies of all the above on your own machine. Note that some of this software requires internet access.

Many packages come in various versions; to avoid potential incompatibilities, you should install versions as close as possible to those used in the labs.

Unit Web Page

The unit web page will be hosted in iLearn, where you will need to log in using your Student One ID and password. The unit will make extensive use of discussion boards also hosted in iLearn. Please post questions there, they will be monitored by the staff on the unit.

Methods of Communication

We will communicate with you via your university email and through announcements on iLearn. Queries to convenors can either be placed on the iLearn discussion board or sent to the unit convenor via the contact email on iLearn.

Unit Schedule

Week 1: Data and Big Data

Week 2: Organizing Big Data

Week 3: Curating Big Data

Week 4: Processing Big Data (Cloud Computing)

Week 5: Processing Big Data (MapReduce)

Week 6: Big Data Platforms (Guest Lecture)

Week 7: Big Data with High Dimensions

Week 8: Indexing Big Data

Week 9: Searching Big Data

Week 10: Multidimensional Divide and Conquer

Week 11: Grid Decomposition in Big Data

Week 12: Advanced Topic in Big Data (Guest Lecture)

Week 13: Unit Review

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Academic Success

Academic Success provides resources to develop your English language proficiency, academic writing, and communication skills.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Macquarie University offers a range of Student Support Services including:

Student Enquiries

Got a question? Ask us via the Service Connect Portal, or contact Service Connect.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Changes from Previous Offering

We value student feedback to be able to continually improve the way we offer our units. As such we encourage students to provide constructive feedback via student surveys, to the teaching staff directly, or via the FSE Student Experience & Feedback link in the iLearn page.

Student feedback from the previous offering of this unit was very positive overall, with students pleased with the clarity around assessment requirements and the level of support from teaching staff. As such, no change to the delivery of the unit is planned, however we will continue to strive to improve the level of support and the level of student engagement. 

Unit information based on version 2026.02 of the Handbook