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


171H School of Science


Sem 2 2018

Course Coordinator: Dr Yongli Ren

Course Coordinator Phone: +61 3 9925 2859

Course Coordinator Email:

Course Coordinator Location: 14.9.7

Course Coordinator Availability: By appointment, by email

Pre-requisite Courses and Assumed Knowledge and Capabilities

Expected prior study:

Databases: this prerequisite knowledge can be attained by completing ISYS1057 Database Concepts
Extensive programming skills: this prerequisite knowledge can be attained by completing COSC1076 Advanced Programming Techniques.

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

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, in particular Hadoop and MapReduce.

Overview of Learning Activities

The learning activities included in this course are:

  • Key concepts will be explained in lectures, 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 You, 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 120 hours of study is expected during this course, comprising:

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

Student-directed hours (72 hours): You are expected to be self-directed, studying independently outside class.

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

The assessment for this course comprises weekly on-line tests or tasks, a major assignment and a formal written exam.
Note: This course has no hurdle requirements.

Assessment Tasks

Assessment Task 1: Weekly On-line Tests or Tasks 
These tests or tasks complement lecture topics and tutorial/laboratory sessions.
Weighting 20%
This assessment task supports CLOs 1, 2 and 4

Assessment Task 2: Major Assignment 
In this task, you will design, implement, critically analyse and report on a substantial big data project.
Weighting 30%
This assessment task supports CLOs 1, 2, 3, 5 and 6

Assessment Task 3: End-of-semester Examination 
The 2 hour end-of-semester examination will cover all course learning outcomes.
Weighting 50%
This assessment task supports CLOs 1, 2, 3, 4, 5, and 6