COMPSCI 753 : Algorithms for Massive Data


2021 Semester Two (1215) (15 POINTS)

Course Prescription

Modern enterprises and applications such as electronic commerce, social networks, location services, and scientific databases are generating data on a massive scale. Analysis of such data must be carried out by scalable algorithms. This course exposes data science practitioners and researchers to various advanced algorithms for processing and mining massive data, and explores best-practices and state-of-the-art developments in big data. Recommended preparation: COMPSCI 320

Course Overview

Modern applications such as electronic commerce, social networks, and location services are expecting efficient big data solutions. This course exposes practitioners to computational bottlenecks of processing, managing, and mining big data. It introduces a wide spectrum of advanced algorithmic techniques that underpin big data analytics and knowledge discovery. Learning advanced algorithms of big data will prepare students for a career as data scientists, big data engineers. In particular, techniques to model and answer queries in streaming data are necessary skills to deal with the Internet of Things applications. Algorithms to scale up machine learning models used in recommender systems or social network analytics are key algorithmic ingredients in big data analytics.

This is one of the core courses in the MProf Studs Data Science and Master of Data Science programs. Students of Honour program and the Master of Information Technology can also participate in this course. There are no formal prerequisites required, but we recommend that students should already take COMPSCI 320  or relevant courses since it helps with the understanding and contextualization of the principles, techniques, and algorithm complexity covered in this course.

Course Requirements

Prerequisite: Approval of the Academic Head or nominee

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity
Capability 6: Social and Environmental Responsibilities

Learning Outcomes

By the end of this course, students will be able to:
  1. Identify and describe computational bottlenecks of processing, managing, and mining big data. (Capability 1 and 2)
  2. Analyse and apply scalable algorithmic tools to handle big data analytics, such as hashing, sampling, and sketching. (Capability 1, 2 and 3)
  3. Describe and explain efficient algorithms for analyzing and processing a variety of data, including text, images, graph, stream (Capability 1, 2 and 3)
  4. Design and implement efficient algorithms for real-world big data analytics (Capability 1, 2 and 3)
  5. Describe and explain current research directions in big data analytics and knowledge discovery (Capability 1 and 2)
  6. Present as a group to fellow students and teachers slides on your joint understanding about the state-of-the-art knowledge on algorithms for massive data (Capability 1, 2, 4, 5 and 6)
  7. Communicate their understanding of the state-of-the-art knowledge on a research topic about big data in the form of a written report, including the fair use of this knowledge in business and society (Capability 1, 2, 4, 5 and 6)


Assessment Type Percentage Classification
Assignments 20% Individual Coursework
Presentation 15% Group Coursework
Research 15% Group Coursework
Final Exam 50% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7
Final Exam

Special Requirements

The programming language used in this course is Python.

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 3 hours of lectures per week, an hour tutorial for each topic. Furthermore, you might need on average 4 hours per week for revising the course content and solving assignments and/or test preparation.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities including tutorials to complete components of the course.
Lectures will be available as recordings. Other learning activities including tutorials will be available as recordings.
The course will not include live online events.
Attendance on campus is required for the exam.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

- Canvas with more reading materials and lecture slides.
- Textbook: Mining of Massive Datasets by Jure Leskovec, Anand
Rajaraman, Jeff Ullman (

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

Level 1:  Delivered normally as specified in delivery mode.
Level 2 / 3 / 4: All teaching activities and assessments are delivered remotely.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 26/01/2021 10:21 a.m.