Unit convenor and teaching staff |
Unit convenor and teaching staff
Convenor/Lecturer
Amin Beheshti
Lecturer
Nabi Rezvani
|
---|---|
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'.
|
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
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.
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 |
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.
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.
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.
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.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
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:
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.
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
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.
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 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
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.
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
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.
Macquarie University offers a range of Student Support Services including:
Got a question? Ask us via AskMQ, or contact Service Connect.
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