Bachelor of Computing Science (Honours)

Program Overview

This Bachelor of Computing Science (Honours) program provides a comprehensive education in computing science and information technology, with a focus on research and scientific-oriented computing. Students gain strong technical skills, problem-solving abilities, and research experience, preparing them for careers as researchers, data scientists, or computing professionals in various industries. The program emphasizes a practice-based approach, combining theory and practical applications, and offers a wide range of elective courses in specialized areas such as artificial intelligence, data analytics, and quantum computing.

Program Outline

Degree Overview:

This course offers a sound education in all aspects of computing science and information technology. It is intended for students who aspire to become researchers or who want a career in a more scientific-oriented computing area. As such it provides a pathway to postgraduate research study. This course adopts a practice-based approach to computing science education and the course content is a mix of theory and practice, with a stronger focus on the mathematics appropriate for computing science and research projects. As well as gaining strong technical skills in computing science and IT, students gain skills in problem-solving, teamwork and communication. Students undertake research projects with UTS researchers. Employers look for graduates with strong computing science skills and, in this course, students are exposed to real research problems in computing science and IT. UTS: Information Technology continues to support part-time study and some subjects can be taken in the evening as well as during the day.

Objectives:

The course aims to produce graduates who are able to apply, in the context of any organisation, the knowledge and skills required of: • information technology professionals who develop systems from first principles • computing specialists for technical research careers, both in industry and academia, or • data scientists who interrogate complex datasets.

Program Structure:

Year 1

Autumn session Subject Code Credit Points Mathematics 1 33130 6 Discrete Mathematics 37181 6 Introduction to Information Systems 31266 6 Programming 1 41039 6 Spring session Subject Code Credit Points Communication for IT Professionals 31265 6 Mathematics 2 33230 6 Database Fundamentals 31271 6

Year 2

Autumn session Subject Code Credit Points Network Fundamentals 41092 6 Web Systems 31268 6 Programming 2 48024 6 Computing Science Studio 1 41078 6 Spring session Subject Code Credit Points Theory of Computing Science 41080 6 Introduction to Data Analytics 31250 6 Select 6 credit points from the following: • Social and Information Network Analysis 42913 6 • Image Processing and Pattern Recognition 31256 6 • Data Visualisation and Visual Analytics 32146 6 • Deep Learning and Convolutional Neural Network 42028 6 • Data Driven and Intelligent Robotics 41077 6 • Introduction to Computational Intelligence 43024 6 • Natural Language Processing 41043 6 • Emerging Topics in Artificial Intelligence 43023 6 • Machine Learning 31005 6 • Introduction to Quantum Computing 43025 6 • AI/Analytics Capstone Project B 31243 6

Year 3

Autumn session Subject Code Credit Points Data Structures and Algorithms 31251 6 Computing Science Studio 2 41079 6 Select 6 credit points from the following: • Social and Information Network Analysis 42913 6 • Image Processing and Pattern Recognition 31256 6 • Data Visualisation and Visual Analytics 32146 6 • Deep Learning and Convolutional Neural Network 42028 6 • Data Driven and Intelligent Robotics 41077 6 • Introduction to Computational Intelligence 43024 6 • Natural Language Processing 41043 6 • Emerging Topics in Artificial Intelligence 43023 6 • Machine Learning 31005 6 • Introduction to Quantum Computing 43025 6 • AI/Analytics Capstone Project B 31243 6 Spring session Subject Code Credit Points Technology Research Preparation 32144 6 Select 6 credit points from the following: • Social and Information Network Analysis 42913 6 • Image Processing and Pattern Recognition 31256 6 • Data Visualisation and Visual Analytics 32146 6 • Deep Learning and Convolutional Neural Network 42028 6 • Image Processing and Pattern Recognition 31256 6 • Data Visualisation and Visual Analytics 32146 6 • Deep Learning and Convolutional Neural Network 42028 6 • Data Driven and Intelligent Robotics 41077 6 • Introduction to Computational Intelligence 43024 6 • Natural Language Processing 41043 6 • Emerging Topics in Artificial Intelligence 43023 6 • Machine Learning 31005 6 • Introduction to Quantum Computing 43025 6 • AI/Analytics Capstone Project B 31243 6

Year 4

Autumn session Subject Code Credit Points Technology Research Methods 32931 6 Project Management and the Professional 31272 6 AI/Analytics Capstone Project 41004 6 Select 6 credit points from the following: • Social and Information Network Analysis 42913 6 • Image Processing and Pattern Recognition 31256 6 • Data Visualisation and Visual Analytics 32146 6 • Deep Learning and Convolutional Neural Network 42028 6 • Data Driven and Intelligent Robotics 41077 6 • Introduction to Computational Intelligence 43024 6 • Natural Language Processing 41043 6 • Emerging Topics in Artificial Intelligence 43023 6 • Machine Learning 31005 6 • Introduction to Quantum Computing 43025 6 • AI/Analytics Capstone Project B 31243 6 Spring session Subject Code Credit Points Honours Project 31482 12 Select 6 credit points from the following: • Social and Information Network Analysis 42913 6 • Image Processing and Pattern Recognition 31256 6 • Data Visualisation and Visual Analytics 32146 6 • Deep Learning and Convolutional Neural Network 42028 6 • Image Processing and Pattern Recognition 31256 6 • Data Visualisation and Visual Analytics 32146 6 • Deep Learning and Convolutional Neural Network 42028 6 • Data Driven and Intelligent Robotics 41077 6 • Introduction to Computational Intelligence 43024 6 • Natural Language Processing 41043 6 • Emerging Topics in Artificial Intelligence 43023 6 • Machine Learning 31005 6 • Introduction to Quantum Computing 43025 6 • AI/Analytics Capstone Project B 31243 6

Assessment:

Assessment methods may include: • assignments • quizzes • tests • examinations • projects • presentations • laboratory reports • research papers

Teaching:

The course is taught by a team of academic staff who are active researchers in their field. The teaching methods used in the course include: • lectures • tutorials • practical sessions • workshops • research projects • guest lectures

Careers:

Graduates of the Bachelor of Computing Science (Honours) are qualified to work as software developers, systems analysts, data scientists, or professional computing science researchers. They may work in a variety of industries, including: • information technology • finance • health care • manufacturing • government • education