Course Title: Big Data Processing

Part A: Course Overview

Course Title: Big Data Processing

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


140H Computer Science & Information Technology


Sem 2 2015,
Sem 2 2016


City Campus


171H School of Science


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

Course Coordinator: Dr Ke Deng

Course Coordinator Phone: +61 3 9925 3202

Course Coordinator Email:

Course Coordinator Location: 14.9.12

Course Coordinator Availability: By appointment, by email

Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Prerequisites:

COSC1295 Advanced Programming 


ISYS1055 Database Concepts

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, privacy and security, 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. Data warehouses 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 systems 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 is a specialisation course that contributes to the following Program Learning Outcomes (PLOs) for MC061 Master of Computer Science and MC208 Master of Information Technology:

Critical Analysis:

You will learn to accurately and objectively examine, and critically investigate computer science and information technology (IT) concepts, evidence, theories or situations, in particular to:

-- analyse and model complex requirements and constraints for the purpose of designing and implementing software artefacts and IT systems

-- evaluate and compare designs of software artefacts and IT systems on the basis of organisational and user requirements.

Problem Solving: 

Your capability to analyse complex problems and synthesise suitable solutions will be extended as you learn to: design and implement software solutions that accommodate specified requirements and constraints, based on analysis or modelling or requirements specification.


You will learn to communicate effectively with a variety of audiences through a range of modes and media to: interpret abstract theoretical propositions, choose methodologies, justify conclusions and defend professional decisions to both IT and non-IT personnel via technical reports of professional standard and technical presentations.

Course Learning Outcomes

Upon successful completion of this course you should have gained an understanding of Big Data concepts, including cloud and big data architectures, an overview of Big Data analytics, implementation of Big Data platforms, and be able to apply these concepts using an industry standard non-relational database environment.

The key course learning outcomes are:

  • CLO 1: model and implement efficient big data solutions for various application areas using appropriately selected algorithms and data structures.
  • CLO 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.
  • CLO 3: motivate and explain trade-offs in big data processing technique design and analysis in written and oral form.
  • CLO 4: explain the Big Data Fundamentals, including the evolution of Big Data, the characteristics of Big Data and the challenges introduced.
  • CLO 5: apply non-relational databases, the techniques for storing and processing large volumes of structured and unstructured data, as well as streaming data.
  • CLO 6: apply the novel architectures and platforms introduced for Big data, i.e. Hadoop, MapReduce, and Spark.

Overview of Learning Activities

The learning activities included in this course are:

  • Key concepts will be explained in pre-recorded lecture videos, lectorials, classes or online, where syllabus material will be presented and the subject matter will be illustrated with demonstrations and examples;
  • Tutorials and/or labs and/or group discussions (including online forums) focused on projects and problem solving will provide practice in the application of theory and procedures, allow exploration of concepts with teaching staff and other students, and give feedback on your progress and understanding;
  • Assignments, as described in Overview of Assessment (below), requiring an integrated understanding of the subject matter; and
  • Private study, working through the course as presented in classes and learning materials, and gaining practice at solving conceptual and technical problems.

Total study hours
A total of 144 hours of study is expected during this course, comprising:

Teacher-directed hours (48 hours): Lectorials and laboratory sessions. Each week there will be 2 hour of lectorial and 2 hours of laboratory work. You are encouraged to participate during lectorials through asking questions, commenting on the lecture material based on your own experiences, and through presenting solutions to written exercises. The laboratory sessions will introduce you to the tools necessary to undertake the assignment work.

Student-directed hours (96 hours): You are expected to be self-directed, studying outside class independently. You are expected to study by yourself the weekly lecture materials by reading the lecture notes and watching the pre-recorded lecture video clips before attending a lectorial of each week. 

Overview of Learning Resources

The course is supported by the Canvas learning management system which provides specific learning resources. See also the RMIT Library Guide at

You will make use of computer laboratories and relevant software provided by the School. You will be able to access course information and learning materials through myRMIT and may be provided with copies of additional materials in class or via email. Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided.

Overview of Assessment

Overview of Assessment 

The assessment for this course comprises three major assignments.
Note: This course has no hurdle requirements.

Assessment Tasks

Assessment Task 1: Assignment 1 
In this task, you will solve a data analysis problem on the big data processing platform. 
Weighting 30% 
This assessment task supports CLOs 1, 2, 4.

Assessment Task 2: Assignment 2 
In this task, you will implement, critically analyse and report a substantial big data solution of an existing data mining algorithm on a large data set. 
Weighting 40% 
This assessment task supports CLOs 1, 2, 3, 4, 5 and 6.

Assessment Task 3: Assignment 3 
In this task, you will design, implement, and report on a data stream analysis project. 
Weighting 30% 
This assessment task supports CLOs 1, 2, 3, 4, 5 and 6.