COMPSCI 753 : Algorithms for Massive Data
Science
2025 Semester Two (1255) (15 POINTS)
Course Prescription
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, and big data engineers. In particular, techniques to model and answer queries in streaming data are necessary skills for applications such as the Internet of Things. 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 the Honours 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.
Capabilities Developed in this Course
| Capability 1: | People and Place |
| Capability 3: | Knowledge and Practice |
| Capability 4: | Critical Thinking |
| Capability 5: | Solution Seeking |
| Capability 6: | Communication |
| Capability 7: | Collaboration |
Learning Outcomes
- Identify and describe computational bottlenecks of processing, managing, and mining bigdata. (Capability 3 and 4)
- Analyse and apply scalable algorithmic tools to handle big data analytics, such as hashing, sampling, and sketching. (Capability 3 and 4)
- Describe and explain efficient algorithms for analyzing and processing a variety of data, including text, images, graph, stream (Capability 3 and 4)
- Design and implement efficient algorithms for real-world big data analytics (Capability 3, 4 and 5)
- Describe and explain current research directions in big data analytics and knowledge discovery (Capability 3 and 4)
- 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, 3, 4, 5, 6 and 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 3, 4, 5, 6 and 7)
Assessments
| Assessment Type | Percentage | Classification |
|---|---|---|
| Assignments | 20% | Individual Coursework |
| Presentation | 15% | Group Coursework |
| Research | 15% | Group Coursework |
| Final Exam | 50% | Individual Examination |
| 4 types | 100% |
| Assessment Type | Learning Outcome Addressed | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | ||||
| Assignments | ||||||||||
| Presentation | ||||||||||
| Research | ||||||||||
| Final Exam | ||||||||||
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
- Two-hour tutorial for each topic
- An average of 4 hours per week for revising the course content and solving assignments and/or test preparation
Delivery Mode
Campus Experience
A remote version of the course can be made available to students located overseas because of border restrictions, or those with an exemption to study remotely.
Attendance is expected at scheduled activities including tutorials to complete components of the course for onshore students.
Lectures and other learning activities including tutorials will be available as recordings.
The activities for the course are scheduled as a standard weekly timetable.
Learning 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.
- Canvas has more reading materials and lecture slides
- Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman (http://www.mmds.org/).
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.
- Provide more guidance on how to run the research project in a group
- Open up more tutorials, possibly every week.
- Adjusting the assessments if needed, e.g., more contribution for assignment.
- A longer break between the final lectures and the final exam date.
Academic Integrity
The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework, tests and examinations 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. A student's assessed work may be reviewed against electronic source material using computerised detection mechanisms. Upon reasonable request, students may be required to provide an electronic version of their work for computerised review.
Class Representatives
Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.
Copyright
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 http://disability.auckland.ac.nz
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 https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.
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.
The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.
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 https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.
Disclaimer
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.