Late Submission

No extensions will be granted without an approved application for Special Consideration. There will be a deduction of 10% of the total available marks made from the total awarded mark for each 24 hour period or part thereof that the submission is late. For example, 25 hours late in submission for an assignment worth 10 marks – 20% penalty or 2 marks deducted from the total.  No submission will be accepted after solutions have been posted.

Supplementary Exam

If you receive Special Consideration for the final exam, a supplementary exam will be scheduled after the normal exam period, following the release of marks. 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.

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:

• Java 8
  • Download: http://download.oracle.com/otn-pub/java/jdk/8u162-b12/0da788060d494f5095bf8624735fa2f1/jdk-8u162-windows-x64.exe
  • Installation instructions to set JAVA_HOME:
    • https://www.java.com/en/download/help/download_options.xml
    • https://docs.oracle.com/cd/E19182-01/820-7851/inst_cli_jdk_javahome_t/
• VirtualBox
  • Download and installation:  https://www.virtualbox.org/wiki/Downloads
• Hadoop
  • VirtualBox image: https://downloads.cloudera.com/demo_vm/virtualbox/cloudera-quickstart-vm-5.13.0-0-virtualbox.zip download and set up as a VBox virtual machine
• Python 3.7 (Anaconda version)
  • Download: https://www.anaconda.com/download
• MongoDB 4.0 Community Edition
  • Installation instructions: https://docs.mongodb.com/manual/installation/
• Robomongo
  • Download and installation:  https://robomongo.org/
• SAS Viya for Learners
  • ​Online system; registration instructions will be provided. https://www.sas.com/en_us/software/viya-for-learners.html

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.

Below is a tentative unit schedule.

There are no significant changes from the previous offering.

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 2019, the thresholds are:

• High Distinction: 85 - 100 You are an exceptional student who shows consistent evidence of deep and critical understanding in relation to the learning outcomes. Assignment solutions are outstanding and they show substantial originality and insight. You provide a critical evaluation of problems, their solutions and their implications.
• Distinction: 75 - 84 There is evidence of learning that goes beyond replication of content knowledge or skills relevant to the learning outcomes. You are able to apply the technologies covered to new problems or in new ways. Assignment solutions are well presented, implementing extended features or displaying high quality work. You show good skills in the use of means of communication appropriate to the discipline and the audience.
• Credit: 65 - 74  There is evidence of learning that goes beyond replication of content knowledge or skills relevant to the learning outcomes. You communicate ideas fluently and clearly in terms of the conventions of the discipline.
• Pass: 50 - 64 There is sufficient evidence that you attained the learning outcomes. You demonstrate an understanding of fundamental concepts covered in the unit. You are able to apply core big data technology.
• Fail: 0 - 49 There is no evidence you attained the learning outcomes. There is missing or partial or superficial or faulty understanding and application of the fundamental concepts covered in the unit.

Criteria for passing the different assessment tasks will reflect the above standards and be made clear in the guidelines distributed with the task descriptions.