Students

COMP8210 – Big Data Technologies

2024 – Session 2, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Convener and Lecturer
Amin Beheshti
amin.beheshti@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
COMP6210
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

This unit introduces students to the specialised technologies required for big data applications in business, organisations and scientific research. It covers specialised methods for storing, manipulating, analysing and exploiting the ever-increasing amounts of data that are encountered in practical applications, and provides hands-on training in advanced topics such as distributed computing clusters and 'cloud computing'.

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: Demonstrate a high level of technical competency in standard and advanced methods for big data technologies
  • ULO2: Describe the current status of and recognize future trends in big data technologies
  • ULO3: Reflect on the changes the big data technologies bring to businesses, organisations and society, and critically analyse future trends
  • ULO4: Demonstrate a competency with emerging big data technologies, applications and tools
  • ULO5: Communicate clearly and effectively

General Assessment Information

General Assessment Information

- To successfully pass the unit, a student needs to achieve a minimum total mark of 50% across all assessments and the final exam (Problem Analysis Report).

- None of the assessments and the final exam (Problem Analysis Report) are hurdle requirements.

- All assignments will be submitted using iLearn. The results of all assignments will be available via iLearn.

Late Assessment Submission Penalty 

Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark of the task) will be applied for each day a written report or presentation assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of ‘0’ will be awarded even if the assessment is submitted. The submission time for all uploaded assessments is 11:55 pm. A 1-hour grace period will be provided to students who experience a technical concern. For any late submission of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, please apply for Special Consideration. For example, if the assignment is worth 8 marks (of the entire unit) and your submission is late by 19 hours (or 23 hours 59 minutes 59 seconds), 0.4 marks (5% of 8 marks) will be deducted. If your submission is late by 24 hours (or 47 hours 59 minutes 59 seconds), 0.8 marks (10% of 8 marks) will be deducted, and so on.

Special Considerations:

If a student experiences significant disruptions or extenuating circumstances that affect their ability to complete assessments or perform adequately in the final exam, they may be eligible for special consideration. To apply for special consideration, the student must submit a formal request along with appropriate supporting documentation (e.g., medical certificates and evidence of personal hardship) to the relevant academic office within the specified timeframe. Each application will be assessed on a case-by-case basis, and adjustments may include extended deadlines, alternative assessments, or other accommodations as deemed appropriate by the unit coordinator. It is important to note that the final exam is in a form of Problem Analysis report that will be released in Week 13 on Monday 9am 28th of October and will be due on week 13 on Friday 11:55pm 1st of November.

Important Academic Dates

Information about important academic dates, including deadlines for withdrawing from units, is available at https://students.mq.edu.au/important-dates

 

 

Assessment Tasks

Name Weighting Hurdle Due
Assignment 1 - Data Lakes 10% No Week 4 - Friday, 11:55 pm 16 August
Assignment 2 - Processing Data 25% No Week 8 - Friday, 11:55pm 13 September
Assignment 3 - Data Analysis 25% No Week 12 - Friday, 11:55pm 25 October
Problem Analysis Report 40% No Week 13 - Friday, 11:55pm 1 November

Assignment 1 - Data Lakes

Assessment Type 1: Practice-based task
Indicative Time on Task 2: 10 hours
Due: Week 4 - Friday, 11:55 pm 16 August
Weighting: 10%

 

In this assignment you will explore the management of big data using data lake technology.

 
On successful completion you will be able to:
  • Demonstrate a high level of technical competency in standard and advanced methods for big data technologies
  • Describe the current status of and recognize future trends in big data technologies
  • Demonstrate a competency with emerging big data technologies, applications and tools

Assignment 2 - Processing Data

Assessment Type 1: Practice-based task
Indicative Time on Task 2: 20 hours
Due: Week 8 - Friday, 11:55pm 13 September
Weighting: 25%

 

In this assignment you will apply techniques to index, search and process high-dimensional data.

 
On successful completion you will be able to:
  • Demonstrate a high level of technical competency in standard and advanced methods for big data technologies
  • Describe the current status of and recognize future trends in big data technologies
  • Demonstrate a competency with emerging big data technologies, applications and tools

Assignment 3 - Data Analysis

Assessment Type 1: Practice-based task
Indicative Time on Task 2: 20 hours
Due: Week 12 - Friday, 11:55pm 25 October
Weighting: 25%

 

In this assignment you will perform analysis of Big Data.

 
On successful completion you will be able to:
  • Demonstrate a high level of technical competency in standard and advanced methods for big data technologies
  • Describe the current status of and recognize future trends in big data technologies
  • Reflect on the changes the big data technologies bring to businesses, organisations and society, and critically analyse future trends
  • Demonstrate a competency with emerging big data technologies, applications and tools
  • Communicate clearly and effectively

Problem Analysis Report

Assessment Type 1: Case study/analysis
Indicative Time on Task 2: 25 hours
Due: Week 13 - Friday, 11:55pm 1 November
Weighting: 40%

 

A report on a major problem analysis on Big Data Technologies.

 
On successful completion you will be able to:
  • Demonstrate a high level of technical competency in standard and advanced methods for big data technologies
  • Describe the current status of and recognize future trends in big data technologies
  • Reflect on the changes the big data technologies bring to businesses, organisations and society, and critically analyse future trends
  • Demonstrate a competency with emerging big data technologies, applications and tools
  • Communicate clearly and effectively

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
  • the Writing Centre 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

Delivery and Resources

For details of days, times and rooms consult the timetables webpage.

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.

COVID Information

For the latest information on the University’s response to COVID-19, please refer to the Coronavirus infection page on the Macquarie website: https://www.mq.edu.au/about/coronavirus-faqs. Remember to check this page regularly in case the information and requirements change during the semester. If there are any changes to this unit in relation to COVID, these will be communicated via iLearn.

Required and Recommended Texts

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

  • A. Beheshti, S. Ghodratnama, M. Elahi, H. Farhood, "Social Data Analytics", ISBN 978-1-032-19627-5, CRC Press, 2022
  • 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 MOOCs, 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 listing the most relevant reading for each week.

 

Technology Used and Required

The following software is used in COMP336:

This software is installed in the labs; you should also ensure you have working copies of all the above on your 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 must 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.

Week 1 Classes:

Week 1 will start on Monday, 22 July. The first lecture will be from 11 a.m. to 1 p.m. on Monday, 22 July. We will have 4 practical sessions on Mondays and Wednesdays. And tutorial sessions will be on Tuesdays. Please check the Lecture, Practical, and Tutorial times and locations here: https://publish.mq.edu.au/

Unit Schedule

Week 1: Intro to Big Data

Week 2: Organizing Big Data - NoSQL Database (MongoDB)

Week 3: Organizing Big Data - Graph Database (Neo4j Part I)

Week 4: Organizing Big Data - Graph Database (Neo4j Part II)

Week 05: Data Lakes (Snowflake)

Week 06: Data Lakes (Databricks)

Week 07: Microsoft Data Analysis and Visualisation with PowerBI

Week 08: Google Data & AI

Week 09: Fujitsu AutoML 

Week 10: Fujitsu AutoML

Week 11: Fujitsu AutoML

Week 12: Fujitsu AutoML 

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/

The Writing Centre

The Writing Centre 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 improve the way we offer our units continually. 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 on 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 the 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 2024.02 of the Handbook