University of Technology, Sydney

Staff directory | Webmail | Maps | Newsroom | What's on

41082 Introduction to Data Engineering

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2019 is available in the Archives.

UTS: Engineering: Electrical and Data Engineering
Credit points: 6 cp

Subject level:

Undergraduate

Result type: Grade and marks

Description

This subject introduces students to the foundations of data engineering and the data industry. Data is all pervasive in modern society. Data engineers both build the infrastructure that enables this, but also participate in the manipulation and analysis of the data.

The subject takes a practical, hands-on approach designed to inspire and motivate students in all facets of data engineering. These include the gathering, processing and analysis of data, as well as decision-making and actuation in relation to data. Students create their own data sets to use in a number of group and individual projects.

MATLAB is core to the subject and students become proficient in its use for modelling and analysis.

This is a field of practice subject undertaken by all students enrolled in a Data Engineering major. Students from other majors or faculties may also enrol.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. Use practical skills to model and simulate complex and complicated systems.
2. Understand, collect and store data.
3. Validate and authenticate data.
4. Integrate the elements of the Data Engineering program, its Stages and its Studios.
5. Create an item of software in the Matlab environment that performs a data gathering task.

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following faculty Course Intended Learning Outcomes (CILOs) and Engineers Australia (EA) Stage 1 competencies:

  • Apply decision-making methodologies to evaluate solutions for efficiency, effectiveness and sustainability, which is linked to EA Stage 1 Competencies: 1.2, 2.1 (B.4)
  • Apply abstraction, mathematics and/or discipline fundamentals to analysis, design and operation, which is linked to EA Stage 1 Competencies: 1.1, 1.2, 2.1, 2.2 (C.1)
  • Develop models using appropriate tools such as computer software, laboratory equipment and other devices, which is linked to EA Stage 1 Competencies: 2.2, 2.3, 2.4 (C.2)
  • Evaluate model applicability, accuracy and limitations, which is linked to EA Stage 1 Competencies: 2.1, 2.2 (C.3)
  • Manage own time and processes effectively by prioritising competing demands to achieve personal goals, which is linked to EA Stage 1 Competencies: 3.5, 3.6 (D.1)

Teaching and learning strategies

The subject is carried out through a series of small and large projects undertaken in the field and in laboratories, supported by a combination of lectures and online resources. Students work in teams on the assignments involving problem definition, research, and analysis.

The faculty expects a commitment of nine hours per week for the subject, some of which occurs during class time. Students are expected to attend all timetabled sessions.

Ultimately learning is the student's responsibility. It is an aim of this subject to help students develop strategies that will enable them to more effectively undertake the responsibility of learning. These strategies will help students throughout the rest of their course and later in practice. In this subject students are encouraged to recognise the resources around them, and to use them.

Specific strategies include:

  • Establishment of study groups to encourage collaborative learning with group members.
  • Plenary resource sessions during which information and guidance regarding the teaching staff’s expectations of students will be presented.
  • A fieldwork and laboratory programs of team and individual projects.
  • Individual reflection.

Team projects help to develop skills such as:

  • Teamwork (skills in working within team dynamics; leadership skills);
  • Analysis and cognition (analysing task requirements; questioning; critically interpreting material; evaluating the work of others);
  • Collaboration (conflict management and resolution; accepting intellectual criticism; flexibility; negotiation and compromise); and
  • Organisational and time management skills

Some of the concepts and ideas that students encounter while studying in this subject may be difficult to understand. A range of supplementary learning materials such as Lynda.com online courses will be made available.

Students will record and present their work through UTSOnline WiKi's.

Content (topics)

  1. Data
    1. What it is
    2. How it is collected
    3. How it is represented and manipulated
    4. How authentic and secure it is
    5. How decisions are made using it.
  2. Data Engineering degree structure
  3. Introduction to Complex Systems and their theory
  4. Systems modelling using Matlab
  5. Systems modelling using Simulink

Assessment

Assessment task 1: Learning Matlab for modeling engineering systems

Intent:

Central to the subject is the ability to model complex systems. In particular, to use Matlab to do this. You will learn Matlab by using the interactive online textbook from zyBooks.

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1 and 3

This assessment task contributes to the development of the following course intended learning outcomes (CILOs):

B.4, C.1, C.2 and C.3

Type: Quiz/test
Groupwork: Individual
Weight: 30%
Criteria linkages:
Criteria Weight (%) SLOs CILOs
Ability to apply Matlab abstractions and structures 25 1 C.1
Correctness of the models used 25 1 C.2
Evaluations of the models used 25 3 C.3
Reflection on usefulness of the models and programs 25 3 B.4
SLOs: subject learning objectives
CILOs: course intended learning outcomes

Assessment task 2: Model and Optimization of a complex system using Matlab

Intent:

A very important element of Data Engineering is the ability to optimize things. Very often this is done through Machine Learning. The motivation for this task is to introduce Machine Learning / Stochastic Optimization using:
· A simple to learn algorithm, on
· A simple well know “benchmark” problem, using
· A programming language that is already familiar (Matlab)

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 3 and 4

This assessment task contributes to the development of the following course intended learning outcomes (CILOs):

B.4, C.1, C.2, C.3 and D.1

Type: Report
Groupwork: Group, individually assessed
Weight: 30%
Criteria linkages:
Criteria Weight (%) SLOs CILOs
Validity and completeness of the abstractions created 20 1 C.1
Correctness of the models used 30 1 C.2
Evaluations of the models used 20 3 C.3
Reflection on usefulness of the Data and your analysis of the Data to potential users 15 3 B.4
Quality of your online document in terms of styling and appeal 15 4 D.1
SLOs: subject learning objectives
CILOs: course intended learning outcomes

Assessment task 3: Create an App using MatLab Appdesigner

Intent:

Create an App using Matlab Appdesigner that provides a GUI for one of the tasks done earlier in the session.

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 3 and 4

This assessment task contributes to the development of the following course intended learning outcomes (CILOs):

B.4, C.1, C.2, C.3 and D.1

Type: Report
Groupwork: Group, individually assessed
Weight: 40%
Criteria linkages:
Criteria Weight (%) SLOs CILOs
Validity and completeness of the abstractions created 20 1 C.1
Correctness of the models used 30 1 C.2
Evaluations of the models used 20 3 C.3
Reflection on usefulness of the Data and your analysis of the Data to potential users 15 1 B.4
Quality of your online document in terms of styling and appeal 15 4 D.1
SLOs: subject learning objectives
CILOs: course intended learning outcomes

Minimum requirements

In order to pass the subject, a student must achieve an overall mark of 50% or more.

Required texts

Interactive e-book - 1 year licence:

Introduction to MATLAB zyBook with Integrated MATLAB Homework System. Rajeevan Amirtharajah and Andre Knoesen, University of California.

Written for undergraduate students, Introduction to MATLAB zyBook is a web-based book that presents a comprehensive introduction to MATLAB and includes an integrated MATLAB homework system. It emphasizes arrays and their applications and includes animations and hundreds of interactive learning questions. Topics include variables, scripts, functions, strings, and arrays.

Key Features:

  • Hundreds of interactive learning questions, animations, and tools
  • MATLAB challenge activities and instant feedback
  • Built-in auto-grading technology
  • Real-time analysis of class and student performance

Recommended texts

Matlab. A Practical Introduction to Programming and Problem Solving. Stormy Attaway. 4E.

Elsevier: ISBN: 978-0-12-804525-1

This is an excellent book on Matlab. It is recommended that students purchase it both for learning Matlab, but also to keep as a reference for the later stages of their DE Data Engineering degree.

Introduction to MATLAB for Engineers, 3E. William J. Palm

McGraw-Hill: ISBN: 978-0-07-353487-9

This is another excellent book on Matlab. It would make a good alternative to the one described above.

Other resources

Matlab is an essential tool used in this subject, as well as the degree overall.

It is part of the process of developing a "mathematical mindset" amongst graduates.

  1. In order to gain the most from the subject and Matlab, all students are expected to create and use a Mathworks account.
  2. The University has a TAH licence with Mathworks.
    1. This means that all students can obtain a Mathworks account using their UTS student email accounts.
    2. Students should have created this account during preparation week before coming to the first formal session.

In addition, the first Session involves collecting real time data off a smartphone. Therefore, if they have a smartphone (iOS or Android) they should have already installed the Matlab App, and used it to log in to their Mathworks account.

It is also recommended that students learn how to access the web version of Matlab using their Mathworks account