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)

COSC2633

City Campus

Undergraduate

171H School of Science

Face-to-Face

 Sem 2 2019,
Sem 2 2020,
Sem 2 2021

COSC2633

City Campus

Undergraduate

175H Computing Technologies

Face-to-Face

 Sem 2 2022,
Sem 2 2023

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


Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Pre-requisite courses

Successful completion of:

ISYS1055 - Database Concepts (Course ID 004083)
OR
ISYS3412/ISYS3414 - Practical Database Concepts (Course ID 053790)
OR
COSC2803 - Programming Studio 1 (Course ID 054081)

AND

COSC2391/COSC2440 - Further Programming (Course ID 014052)
OR
COSC2815 - Advanced Programming in Python (Course ID 054117)
OR
COSC2802 - Programming Bootcamp 2 (Course ID 054080)
OR
COSC2800 - IT Studio 2 (Course ID 054075)

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 your information go to RMIT Course Requisites webpage.


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

This course is a specialisation course that contributes to the following Program Learning Outcomes (PLOs) for BP340/BP340P23 Bachelor of Data Science:

Enabling Knowledge (PLO1)

You will gain skills as you apply knowledge with creativity and initiative to new situations. In doing so, you will:

  • Demonstrate mastery of a body of knowledge that includes recent developments in computer science, information technology and statistics;
    • Understand and use appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools;
    • Recognise and use research principles and methods applicable to data science.

Critical Analysis (PLO2)

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

  • Analyse and manage large amounts of data arising from various sources
    • Evaluate and compare solutions to data analysis problems on the basis of organisational and user requirements;
    • Bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of statistical problems.

Problem Solving (PLO3)

Your capability to analyse complex problems and synthesise suitable solutions will be extended as you learn to:

  • Design and implement data analytic techniques that accommodate specified requirements and constraints, based on analysis or modelling or requirements specification;
    • Apply an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.

Communication (PLO4)

You will learn to communicate effectively with a variety of audiences through a range of modes and media, in particular to:

  • Interpret abstract theoretical propositions, choose methodologies, justify conclusions and defend professional decisions to both technical and non-technical personnel via technical reports of professional standard and technical presentations.


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

The assessment for this course comprises four assignments.

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: Online Timed Assessment
Weighting 25%
This assessment task supports CLOs 1, 2, 4, 5, 6.
This is a 2-hour assessment that may be taken at any time within a 24-hour period.