Course Title: Big Data Processing

Part A: Course Overview

Course Title: Big Data Processing

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC2637

City Campus

Postgraduate

140H Computer Science & Information Technology

Face-to-Face

 Sem 2 2015,
Sem 2 2016

COSC2637

City Campus

Postgraduate

171H School of Science

Face-to-Face

 Sem 2 2018,
Sem 2 2019,
Sem 2 2020,
Sem 2 2021

COSC2637

City Campus

Postgraduate

175H Computing Technologies

Face-to-Face

 Sem 2 2022,
Sem 2 2023,
Sem 2 2024

Course Coordinator: Dr Ke Deng

Course Coordinator Phone: +61 3 9925 3202

Course Coordinator Email: ke.deng@rmit.edu.au

Course Coordinator Location: 14.9.12

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Pre-Requisite Courses

Successful completion of the following course/s:

AND

Note: it is a condition of enrolment at RMIT that you accept responsibility for ensuring that you have completed the prerequisite/s and agree to concurrently enrol in co-requisite courses before enrolling in a course.

For information go to RMIT Course Requisites webpage.

 

If you have completed prior studies at RMIT or another institution that developed the skills and knowledge covered in the above course/s you may be eligible to apply for credit transfer.

Alternatively, if you have prior relevant work experience that developed the skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning.

Please follow the link for further information on how to apply for credit for prior study or experience.


Course Description

This course builds on your database and programming skills. It aims to give you an in-depth understanding of a wide range of fundamental algorithms and processing platforms used in big data management.

The course covers Big Data Fundamentals, including the characteristics of Big Data, the sources Big Data (such as social media, sensor data, and geospatial data), as well as the challenges imposed around information management, data analytics, as well as platforms and architectures. Emphasis will be given to non-relational databases by examining techniques for storing and processing large volumes of structured and unstructured data, streaming data as well as complex analytics on them. Cloud computing and data centres will also be presented as a solution to handling big data and business intelligence applications. 

The course aims to keep a balance between algorithmic and systematic issues. The algorithms discussed in this course involve methods of organising big data for efficient complex computation. In addition, we consider Big Data platforms (such as Hadoop) to present practical applications of the algorithms covered in the course.


Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes

This course contributes to the program learning outcomes for the following program(s):

MC267 - Master of Data Science

PLO 2    Critical Analysis
PLO 3    Problem Solving
PLO 4    Communication

For more information on the program learning outcomes for your program, please see the program guide.


Upon successful completion of this course, you will be able to:

  1. Model and implement efficient big data solutions for various application areas using appropriately selected algorithms and data structures.
  2. Analyse methods and algorithms, to compare and evaluate them with respect to time and space requirements and make appropriate design choices when solving real-world problems.
  3. Motivate and explain trade-offs in big data processing technique design and analysis in written and oral form.
  4. Explain the Big Data Fundamentals, including the evolution of Big Data, the characteristics of Big Data and the challenges introduced.
  5. Apply non-relational databases, the techniques for storing and processing large volumes of structured and unstructured data, as well as streaming data.
  6. Apply the novel architectures and platforms introduced for Big data, i.e., Hadoop, MapReduce, and Spark.


Overview of Learning Activities

You will be actively engaged in a range of learning activities such as lectorials, tutorials, practicals, laboratories, seminars, project work, class discussion, individual and group activities. Delivery may be face to face, online or a mix of both.

You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through myRMIT Studies Course.


Overview of Learning Resources

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course.

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal. 


Overview of Assessment

Note: This course has no hurdle requirements.

Assessment Tasks

Assessment Task 1: MapReduce Programming
Weighting 25% 
This assessment task supports CLOs 1-4 and 6.

Assessment Task 2: Big Data Processing with High-level language
Weighting 25%
This assessment task supports CLOs 1-4 and 6.

Assessment Task 3: Spark Problem Solving 
Weighting 25%
This assessment task supports CLOs 1-4 and 6.

Assessment Task 4: Timed Assessment (online)
Weighting 25%
This assessment task supports CLOs 1, 2, 4, 5, and 6

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.