Students

COMP7210 – Big Data Technologies

2022 – Session 2, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Convenor/Lecturer
Amin Beheshti
amin.beheshti@mq.edu.au
Lecturer
Nabi Rezvani
nabiallah.rezvani@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
Admission to MRes
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

Important Academic Dates

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

General Assessment Information

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

 

Late Assessment Submission Penalty 

In this unit, No late submissions will be accepted, unless a Special Consideration is Submitted before the assessment submission deadline, and Granted.

Assessment Tasks

Name Weighting Hurdle Due
Assignment 1 - Data Lakes 10% No Week 4
Assignment 2 - Processing Data 25% No Week 7
Assignment 3 - Data Analysis 25% No Week 12
Problem Analysis Report 40% No Week 13

Assignment 1 - Data Lakes

Assessment Type 1: Essay
Indicative Time on Task 2: 10 hours
Due: Week 4
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: Essay
Indicative Time on Task 2: 20 hours
Due: Week 7
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: Essay
Indicative Time on Task 2: 20 hours
Due: Week 12
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
Weighting: 40%

 

The Problem Analysis Report will assess students' understanding of the learning outcomes in the Big Data Problems.

 
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.

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 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 COMP336:

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.

Unit Schedule

Week 01  |  Intro to Big Data 

Week 02  |  Organizing Big Data - NoSQL Database (MongoDB)

Week 03  |  Organizing Big Data - Graph Database (Neo4j)

Week 04  |  Organizing Big Data - Data Lake (AWS/Google/Snowflakes)

Week 05  |  Processing Big Data (Python for Big Data)

Week 06  |  Guest Lecture (Microsoft/IBM/AWS/Databricks)

Week 07  |  Analysing Big Data (Spark SQL)

Week 08  |  Text Analytics (PySpark) 

Week 09  |  Visualising Big Data (Tableau) 

Week 10  |  Visualising Big Data (PowerBI/Snowflakes)

Week 11  |  Analysing Streaming Data (Spark Streaming)

Week 12  |  Guest Lecture (Microsoft/IBM/AWS)

Week 13  |  Exam/Report  

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 ask.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 AskMQ, 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.

Unit information based on version 2022.02 of the Handbook