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, Engineering and Technology |
Face-to-Face |
Viet2 2019, Viet2 2020, Viet2 2021, Viet3 2022, Viet3 2023, Viet3 2024 |
EEET2615 |
RMIT Vietnam Hanoi Campus |
Undergraduate |
860H School of Science, Engineering and Technology |
Face-to-Face |
Viet3 2024 |
Course Coordinator: Dr Arthur Tang
Course Coordinator Phone: +84 28 3776 1300
Course Coordinator Email: arthur.tang@rmit.edu.vn
Course Coordinator Location: SGS 2.4.27
Course Coordinator Availability: 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:
- Describe big data and its key challenges and benefits.
- Describe the modern approaches to collecting and storing big data.
- Discuss scalability and security issues related to big data.
- Discuss efficient algorithms for big data analytics and strategies for knowledge discovery.
- 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