Course Title: Big Data for Engineering

Part A: Course Overview

Course Title: Big Data for Engineering

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

EEET2574

RMIT University Vietnam

Undergraduate

860H School of Science and Technology

Face-to-Face

Viet2 2019,
Viet2 2020

Course Coordinator: Dr. Minh Dinh

Course Coordinator Phone: +84 28 36 22 44 20

Course Coordinator Email: minh.dinh4@rmit.edu.vn

Course Coordinator Location: 2.4.27

Course Coordinator Availability: TBA


Pre-requisite Courses and Assumed Knowledge and Capabilities

Before commencing this course, you must complete ISYS2077 Database Concepts and COSC2658 Data Structures & Algorithms.


Course Description

This course introduces the topic of Big Data and its keys challenges. In this course the students will discover how these challenges are currently approached in a variety of computer science domains, and how new knowledge can be uncovered from Big Data, and how Big Data solutions can be explored in several application areas.


Objectives/Learning Outcomes/Capability Development

This course contributes to the following program learning outcomes:

1.3. In-depth understanding of specialist bodies of knowledge within the engineering discipline.

2.1. Application of established engineering methods to complex engineering problem solving.

2.2. Fluent application of engineering techniques, tools and resources.

2.3. Application of systematic engineering synthesis and design processes.


On completion of this course, students should be able to:

  1. Distinguish Big Data and understand its key challenges.
  2. Discover modern approaches to data collection and storage.
  3. Discuss scalability and security issues.
  4. Investigate efficient algorithms for data analytics and strategies for knowledge discovery.
  5. Explore data-oriented user interfaces and understand the benefits and challenges of Big Data real world applications.


Overview of Learning Activities

Learning activities will take the form of:

  • Lectures
  • Tutorials/Labs


Overview of Learning Resources

Will be available on Canvas.


Overview of Assessment

This course has no hurdle requirements. There are 3 assessments as follow.

Assessment 1: Labs/Quizzes (20%)

This assessment assesses the following learning outcomes:
PLO 1.3, 2.1, 2.2, 2.3 CLO 1, 2, 4

Assessment 2: Assignments (30%)

This assessment assesses the following learning outcomes:
PLO 1.3, 2.1, 2.2, 2.3 CLO 1, 2, 3

Assessment 3: Project (50%)

This assessment assesses the following learning outcomes:
PLO 1.3, 2.1, 2.2, 2.3 CLO 1, 2, 3, 4, 5