Students

ITEC874 – Big Data Technologies

2018 – S2 Day

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Convenor, lecturer
Diego Molla-Aliod
diego.molla-aliod@mq.edu.au
Contact via diego.molla-aliod@mq.edu.au
E6A331
See: http://web.science.mq.edu.au/~diego/
Lecturer
Amin Beheshti
amin.beheshti@mq.edu.au
Contact via amin.beheshti@mq.edu.au
E6A340
Thursday 9-10am
Credit points Credit points
4
Prerequisites Prerequisites
Admission to MInfoTech or MSc or MDataSc or MCyberSec or GradDipInfoTech
Corequisites Corequisites
Co-badged status Co-badged status
COMP733
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:

  • Obtain a high level of technical competency in standard and advanced methods for big data technologies
  • Understand 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
  • Develop a competency with emerging big data technologies, applications and tools
  • Communicate clearly and effectively

General Assessment Information

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

Late submission to the assignments will be penalised with the following deductions:

  • Assignment 1: 1 mark per day late.
  • Assignment 2: 4 marks per day late.
  • Assignment 3: 4 marks per day late.

Assessment Tasks

Name Weighting Hurdle Due
Assignment 1 - Data Lakes 10% No Week 3
Assignment 2 - Apache Hadoop 20% No Week 8
Assignment 3 - Data Analysis 20% No Week 12
Final examination 50% No Examination period

Assignment 1 - Data Lakes

Due: Week 3
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:
  • Obtain a high level of technical competency in standard and advanced methods for big data technologies
  • Understand the current status of and recognize future trends in big data technologies
  • Develop a competency with emerging big data technologies, applications and tools

Assignment 2 - Apache Hadoop

Due: Week 8
Weighting: 20%

In this assignment you will apply Apache Hadoop to solve a problem using Big Data.
On successful completion you will be able to:
  • Obtain a high level of technical competency in standard and advanced methods for big data technologies
  • Understand the current status of and recognize future trends in big data technologies
  • Develop a competency with emerging big data technologies, applications and tools

Assignment 3 - Data Analysis

Due: Week 12
Weighting: 20%

In this assignment you will perform analysis of Big Data.
On successful completion you will be able to:
  • Obtain a high level of technical competency in standard and advanced methods for big data technologies
  • Understand 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
  • Develop a competency with emerging big data technologies, applications and tools
  • Communicate clearly and effectively

Final examination

Due: Examination period
Weighting: 50%

The final exam will focus on the theoretical aspects of the unit.
On successful completion you will be able to:
  • Obtain a high level of technical competency in standard and advanced methods for big data technologies
  • Understand 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
  • Develop a competency with emerging big data technologies, applications and tools
  • Communicate clearly and effectively

Delivery and Resources

Required and Recommended Texts

All required and recommended readings will be provided as part of the lecture material.

Technology Used and Required

The following software is used in ITEC874:

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 login 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

Below is a tentative unit schedule.

Week Lecture Workshop
1 Introduction to Big Data Microsoft Azure
2 Organising Big Data -- Data Lake: From Relational to NoSQL Data Lake Services
3 Curating Big Data -- Knowledge Lake: From Extraction to Linking and Summarization Knowledge Lake Services
4 Processing Big Data -- Hadoop: From HDFS to MapReduce Hadoop
5 Industry Talk -- Cloudera (TBC) Cloudera
6 Processing Big Data -- Cloud Computing SAS, Microsoft, Amazon (TBC)
7 Analysing Big Data SAS University Edition; Microsoft Azure
  RECESS  
8 Analysing Streaming Data SAS Event Stream Processing; Azure Stream Analytics (TBC)
9 Visualising Big Data SAS Visual Statistics
10 Visualising Big Data SAS JMP (TBC)
11 Big Data and Society TBA
12 Industry Talk: Amazon (TBC) Amazon

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Undergraduate students seeking more policy resources can visit the Student Policy Gateway (https://students.mq.edu.au/support/study/student-policy-gateway). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

If you would like to see all the policies relevant to Learning and Teaching visit Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central).

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/study/getting-started/student-conduct​

Results

Results shown in iLearn, 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.

Special Consideration

If you receive special consideration for the final exam, a supplementary exam will be scheduled in the week of December 17-21 2018. By making a special consideration application for the final exam you are declaring yourself available for a resit during the supplementary examination period and will not be eligible for a second special consideration approval based on pre-existing commitments. Please ensure you are familiar with the policy prior to submitting an application. Approved applicants will receive an individual notification one week prior to the exam with the exact date and time of their supplementary examination.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Learning Skills

Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to improve your marks and take control of your study.

Student Services and Support

Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.

Student Enquiries

For all student enquiries, visit Student Connect at ask.mq.edu.au

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.

Graduate Capabilities

PG - Capable of Professional and Personal Judgment and Initiative

Our postgraduates will demonstrate a high standard of discernment and common sense in their professional and personal judgment. They will have the ability to make informed choices and decisions that reflect both the nature of their professional work and their personal perspectives.

This graduate capability is supported by:

Learning outcomes

  • Obtain a high level of technical competency in standard and advanced methods for big data technologies
  • Understand 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
  • Develop a competency with emerging big data technologies, applications and tools

Assessment tasks

  • Assignment 1 - Data Lakes
  • Assignment 2 - Apache Hadoop
  • Assignment 3 - Data Analysis
  • Final examination

PG - Discipline Knowledge and Skills

Our postgraduates will be able to demonstrate a significantly enhanced depth and breadth of knowledge, scholarly understanding, and specific subject content knowledge in their chosen fields.

This graduate capability is supported by:

Learning outcomes

  • Obtain a high level of technical competency in standard and advanced methods for big data technologies
  • Understand 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
  • Develop a competency with emerging big data technologies, applications and tools

Assessment tasks

  • Assignment 1 - Data Lakes
  • Assignment 2 - Apache Hadoop
  • Assignment 3 - Data Analysis
  • Final examination

PG - Critical, Analytical and Integrative Thinking

Our postgraduates will be capable of utilising and reflecting on prior knowledge and experience, of applying higher level critical thinking skills, and of integrating and synthesising learning and knowledge from a range of sources and environments. A characteristic of this form of thinking is the generation of new, professionally oriented knowledge through personal or group-based critique of practice and theory.

This graduate capability is supported by:

Learning outcomes

  • Understand 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
  • Develop a competency with emerging big data technologies, applications and tools

Assessment tasks

  • Assignment 1 - Data Lakes
  • Assignment 2 - Apache Hadoop
  • Assignment 3 - Data Analysis
  • Final examination

PG - Research and Problem Solving Capability

Our postgraduates will be capable of systematic enquiry; able to use research skills to create new knowledge that can be applied to real world issues, or contribute to a field of study or practice to enhance society. They will be capable of creative questioning, problem finding and problem solving.

This graduate capability is supported by:

Learning outcomes

  • Understand 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
  • Develop a competency with emerging big data technologies, applications and tools

Assessment tasks

  • Assignment 1 - Data Lakes
  • Assignment 2 - Apache Hadoop
  • Assignment 3 - Data Analysis
  • Final examination

PG - Effective Communication

Our postgraduates will be able to communicate effectively and convey their views to different social, cultural, and professional audiences. They will be able to use a variety of technologically supported media to communicate with empathy using a range of written, spoken or visual formats.

This graduate capability is supported by:

Learning outcomes

  • Reflect on the changes the big data technologies bring to businesses, organisations and society, and critically analyse future trends
  • Communicate clearly and effectively

Assessment tasks

  • Assignment 3 - Data Analysis
  • Final examination

PG - Engaged and Responsible, Active and Ethical Citizens

Our postgraduates will be ethically aware and capable of confident transformative action in relation to their professional responsibilities and the wider community. They will have a sense of connectedness with others and country and have a sense of mutual obligation. They will be able to appreciate the impact of their professional roles for social justice and inclusion related to national and global issues

This graduate capability is supported by:

Learning outcome

  • Reflect on the changes the big data technologies bring to businesses, organisations and society, and critically analyse future trends

Assessment task

  • Final examination

Changes from Previous Offering

This is the first offering of the unit.

Grading Standards

This unit does not have hurdle assessments. The final mark of the unit will be obtained by summing the marks of all the assessment tasks for a total mark of 100. The final grade will be determined based on the final mark according to the thresholds established by Macquarie University. As per July 2018, the thresholds are:

  • High Distinction: 85 - 100
  • Distinction: 75 - 84
  • Credit: 65 - 74
  • Pass: 50 - 64
  • Fail: 0 - 49