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,
Viet2 2021

Course Coordinator: Dr Nhi Vo Ngoc Yen

Course Coordinator Phone: +842836223603

Course Coordinator Email: nhi.vongocyen@rmit.edu.vn

Course Coordinator Location: 2.4.27, Saigon South Campus, RMIT Vietnam

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced pre-requisites:

053790 Practical Database Concepts AND (004302 Algorithms & Analysis OR 051198 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.


Course Learning Outcomes (CLOs)

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

  1. Describe big data and its key challenges and benefits.
  2. Describe the modern approaches to collecting and storing big data.
  3. Discuss scalability and security issues related to big data.
  4. Discuss efficient algorithms for big data analytics and strategies for knowledge discovery.
  5. Apply big data technologies and toolkits for diverse applications.


Overview of Learning Activities

The learning activities included in this course are:

Students are expected to read and listen to the relevant materials before the class.

Key concepts will be demonstrated in lectures. Lecturer will also solve a number of problems while thinking aloud. Students will be spending the rest of the time solving programming problems, taking quizzes and doing class tests using their own laptops.

Tute-lab sessions focus on problem solving for specific requirements while providing hands-on practices on various programming tasks.

A total of 105 hours of study is expected during this course, comprising:

  • Teacher-directed hours (33 hours): lectures, tutorials and laboratory sessions. Each week there will be 1.5 hours of lecture plus 1.5 hours of tute-lab session (a combination of tutorial and laboratory session). You are encouraged to participate during lectures through asking questions and presenting solutions to in-class exercises. In tute-lab sessions, you will do hands-on programming exercises under the guidance of the tutor to consolidate your understanding of what you’ve learnt during lectures.
  • Student-directed hours (72 hours): You are expected to study in a self-directed manner outside class. Whenever encountering problems, you can use discussion forums in the course Canvas to get timely help from the teaching team and/or your study peers.


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: Individual (20%)

This assessment assesses the following learning outcomes: CLO 1, 2, 3

Assessment 2: Individual (40%)

This assessment assesses the following learning outcomes: CLO 1, 2, 3, 4

Assessment 3: Group Project (40%)

This assessment assesses the following learning outcomes: CLO 1, 2, 3, 4, 5