Bachelor of Computer Science

2017 Deakin University Handbook

Note: You are seeing the 2017 view of this course information. These details may no longer be current. [Go to the current version]
Year2017 course information
Award granted Bachelor of Computer Science
Course Map

2017 course map

If you started your course before 2017, please refer to the plan your study page or contact a Student Adviser.

CampusOffered at Burwood (Melbourne)
Cloud CampusYes
Duration3 years full-time or part-time equivalent
CRICOS course code083695K Burwood (Melbourne)
Deakin course codeS306
Approval statusThis course is approved by the University under the Higher Education Standards Framework.
Australian Quality Framework (AQF) recognition

The award conferred upon completion is recognised in the Australian Qualifications Framework at Level 7.

Course sub-headings

Course overview

Deakin’s Bachelor of Computer Science will equip you with the knowledge and practical skills required to design and develop innovative software solutions to complex information and technology problems faced by communities, businesses and industries.

The course is ideally suited to those who are passionate about solving problems and creating solutions, curious about how something works, rather than simply what it does and interested in working at the leading edge of technology innovation and development.

This course provides a comprehensive and systematic study of computer systems and networks, data management and information processes, human computer interaction, programming and software development, computing theory, mathematical methods, and algorithm design and analysis.

Choose from major sequences in Data Science, Robotics and Cyber-Physical Computing, or Cognitive Computing to focus your expertise in areas such as machine learning, artificial intelligence, robotics, smart devices and autonomous systems.

As a student you’ll gain hands-on experience and a practical understanding of theory through learning activities in our modern computing laboratories, working with the latest hardware and software technologies alongside our internationally recognised academic staff. Our world-class research programs in computer science feed directly into our classrooms, meaning that you’ll be learning at the cutting edge of industry expectations and capabilities.

Deakin’s Bachelor of Computer Science has been accredited by the Australian Computer Society (ACS), ensuring a high quality of education and providing you with international recognition as an ICT industry professional.

Computer science graduates are in high demand in Australia and internationally and find employment in a variety of roles, such as data scientist, software developer, software engineer, systems or network administrator, database administrator or developer, solutions architect, systems analyst, or project manager. Computer scientists also work in specialist research and development roles, in both public and private organisations.

Units in the course may include assessment hurdle requirements.

Indicative student workload

You can expect to participate in a range of teaching activities each week. This could include classes, seminars, practicals and online interaction. You can refer to the individual unit details in the course structure for more information. You will also need to study and complete assessment tasks in your own time.

Professional recognition

The Bachelor of Computer Science is professionally accredited with the Australian Computer Society (ACS).

Career opportunities

You will be suited to find employment in organisations engaged in software development, Big Data analysis, cloud computing infrastructure. Initial graduates are typically employed as a software developer, software analyst and design, database and web developer, network and systems manager, and IT consultant.  As your experience develops, you will also be well prepared for progression into project management positions.


Equipment requirements

For information regarding hardware and software requirements, please refer to the School of Information Technology's website, www.deakin.edu.au/information-technology/students or telephone 03 9244 6699.

Fees and charges

Fees and charges vary depending on your course, your fee category and the year you started. To find out about the fees and charges that apply to you, visit the Current students fees website.

Course Learning Outcomes

Deakin Graduate Learning Outcomes (DGLOs)

Course Learning Outcomes (CLOs)

 

1. Discipline-specific knowledge and capabilities: appropriate to the level of study related to a discipline or profession.
  •   Develop a broad, coherent knowledge of the computer science discipline, with detailed knowledge of the application of computer science principles in modern computing systems and software development.
  • Design, develop and implement computing systems that satisfy industry standards and best practices in one or more specialised areas of computer science.
  • Have an in depth knowledge of the concepts and technologies related to computer science and the confidence and ability to communicate to a variety of audiences.
  • Understand the role of computing systems in modern organisations and society in general and apply knowledge of computer science to identify, evaluate, and make recommendations for enhancements.
  • Apply problem solving and knowledge of the practices of computer science and software development to deliver effective and reliable computing systems.

  • Develop a broad, coherent knowledge of the computer science discipline, with detailed knowledge of the application of computer science principles in modern computing systems and software development.

  • Design, develop and implement computing systems that satisfy industry standards and best practices in one or more specialised areas of computer science.

  • Have an in depth knowledge of the concepts and technologies related to computer science and the confidence and ability to communicate to a variety of audiences.

  • Understand the role of computing systems in modern organisations and society in general and apply knowledge of computer science to identify, evaluate, and make recommendations for enhancements.

  • Apply problem solving and knowledge of the practices of computer science and software development to deliver effective and reliable computing systems.

2. Communication: using oral, written and interpersonal communication to inform, motivate and effect change.
  • Communicate in a computer science context to inform, motivate and effect change by utilising a range of verbal, graphical and written methods, recognising the needs of diverse audiences.

3. Digital literacy: using technologies to find, use and disseminate information.
  • Utilise a range of digital technologies and information sources to discover, analyse, evaluate, select, process and disseminate both technical and non-technical information.

4. Critical thinking: evaluating information using critical and analytical thinking and judgment.
  • Evaluate specialist computer science information using critical and analytical thinking, technical skills and well-developed judgement to identify problems, analyse requirements and propose solutions.

5. Problem solving: creating solutions to authentic (real world and ill-defined) problems.
  • Apply theoretical constructs and skills and critical analysis to real-world and ill-defined problems and develop innovative computing solutions.

6. Self-management: working and learning independently, and taking responsibility for personal actions.
  • Apply knowledge and skills to new situations in professional practice and/or further learning in the field of computer science with adaptability, autonomy, responsibility and personal accountability for actions as a practitioner and a learner.
  • Apply understanding of reflective practice and self-critique skills within broad parameters to plan for their own future continuing professional development.

7. Teamwork:working and learning with others from different disciplines and backgrounds.
  • Apply the principles of effective teamwork as a member of diverse computer science teams to demonstrate responsibility for own learning within broad parameters.

8. Global citizenship: engaging ethically and productively in the professional context and with diverse communities and cultures in a global context.
  • Apply professional and ethical standards and accountability for own learning to in the development, design, construction and management of localised computing solutions.

 Approved by Faculty Board 14 July 2016

Course rules

To complete the Bachelor of Computer Science, students must attain 24 credit points. Most units (think of units as ‘subjects’) are equal to 1 credit point. So that means in order to gain 24 credit points, you’ll need to study 24 units (AKA ‘subjects’) over your entire degree. Most students choose to study 4 units per trimester, and usually undertake two trimesters each year.

The course comprises a total of 24 credit points, which must include the following:

  • 16 core IT units (which includes a compulsory internship unit SIT306 IT Internship or STP301 Industry Based Learning)
  • 2 elective units
  • Completion of SIT010 Safety Induction Program (0 credit-point compulsory unit)
  • Completion of STP010 Introduction to Work Placements (0 credit-point compulsory unit)
  • At least one 6-credit point Computer Science major sequence from: Cognitive Science, Data Science or Robotics and Cyber-physical Computing.
  • level 1 - maximum of 10 credit points
  • levels 2 and 3 - minimum of 14 credit points over both levels
  • level 3 - minimum of 6 credit points of which at least 4 must be SIT Course Grouped units 

Major sequences

Refer to the details of each major sequence for availability.

Course structure

Core

Level 1 - Trimester 1

SIT010Unit description is currently unavailable (0 credit point unit)

SIT105Unit description is currently unavailable

SIT190Unit description is currently unavailable **

SIT111Unit description is currently unavailable

One SIT-coded unit (major)

Level 1 - Trimester 2

SIT102Unit description is currently unavailable

SIT103Unit description is currently unavailable

SIT192Unit description is currently unavailable

One SIT-coded unit (major)

 

Level 2 - Trimester 1

STP010Unit description is currently unavailable (0 credit point unit)

SIT222Unit description is currently unavailable

SIT223Unit description is currently unavailable

SIT232Unit description is currently unavailable

One SIT-coded unit (major)

Level 2 - Trimester 2

SIT202Unit description is currently unavailable

SIT221Unit description is currently unavailable

One elective

One SIT-coded unit (major)

 

Level 3 - Trimester1

SIT365Unit description is currently unavailable

SIT322Unit description is currently unavailable

SIT374Unit description is currently unavailable

One SIT-coded unit (major)

Level 3 - Trimester 2

SIT302Unit description is currently unavailable

One elective

One SIT-coded unit (major)

Plus one unit in:

SIT306Unit description is currently unavailable ^

STP301Unit description is currently unavailable

 

** Students who have completed Mathematical Methods 3 and 4 or equivalent may choose to replace SIT190 with an elective unit

^ Offered in Trimester 1, trimester 2 and trimester 3

Course structure

Electives

Select from a range of elective units offered across many courses. In some cases you may even be able to choose elective units from a completely different discipline area (subject to meeting unit requirements).

Work experience

You will have an opportunity to undertake a discipline-specific Industry-Based Learning placement as part of your course. This will provide you with the opportunity to apply and consolidate what you are learning in your course, experience workplace culture and workplace practices, explore career options and develop a professional network before you graduate. Please refer to deakin.edu.au/sebe/wil.

Details of major sequences

Cognitive Science

Campuses

Burwood (Melbourne), Cloud (online)

Unit set code

MJ-S000073

Overview

Effective navigation through the current flood of large-volume, unstructured data requires a new era of computing known as cognitive systems. Cognitive systems learn and interact with people to extend what either humans or machines could do on their own. IBM Watson* is just one example of a cognitive system demonstrating the capacity to answer natural language questions, acquire information, process large amounts of disparate data and learn through repeat interaction.

This major sequence provides students with the fundamental knowledge and technical skills required to design and develop new cognitive computing applications fuelled by Watson’s intelligence (as well as other types of smart machines).

Subject areas include: psychology, understanding the mind, thinking systems and cognition science, human behaviour and computer interaction, and data visualisation and decision making.

Units

HPS111Unit description is currently unavailable

HPS121Unit description is currently unavailable

HPS203Unit description is currently unavailable

SIT205Unit description is currently unavailable

SIT308Unit description is currently unavailable ^

SIT309Unit description is currently unavailable ^

^not available in 2018, replaced by MIS372 Predictive Analytics.

Details of major sequences

Robotics and Cyber-physical Computing

Campuses

Burwood (Melbourne), Cloud (online)

Unit set code

MJ-S000083

Overview

Robotics and cyber-physical systems have emerged as a major commercial technology sector, combining software and hardware to enable products from autonomous vehicles to fitness trackers and smart homes. Specialists in robotics and cyber-physical computing work alongside hardware engineers and generalist application developers, employing specific skills and knowledge to integrate and control diverse hardware devices; collect, communicate and analyse sensor data streams; and develop and employ novel algorithms that allow these systems to act in response to their environment. Common development practices in this field involve rapid prototyping and iterative refinement and demand new skill sets from computing professionals.

Units

SIT122Unit description is currently unavailable

SIT123Unit description is currently unavailable

SIT210Unit description is currently unavailable ^

SIT213Unit description is currently unavailable ^

SIT310Unit description is currently unavailable #

SIT312Unit description is currently unavailable #

^ available from 2018

# available from 2019

Details of major sequences

Data Science

Campuses

Burwood (Melbourne), Cloud (online)

Unit set code

MJ-S000060

Overview

Data analytics is an integral part of decision making in all sectors of the society: business, finance, government, medicine, research and beyond. The Data Science major sequence provides you with the skills and knowledge that are essential for data analytics professionals. You will learn the theory, methodologies, and techniques that allow you to interpret datasets and uncover hidden patterns in order to make predictions, draw conclusions, drive successful initiatives, and make better decisions. Particular focus will be paid to meaningful analyses in the face of huge amounts of data, where traditional approaches may be impractical.

Subject areas include a wide spectrum of analytics: data science concepts, statistics and data analysis, computational decision analysis, data mining and machine learning, and advanced data science.

Units

SIT191Unit description is currently unavailable

SIT112Unit description is currently unavailable

SIT208Unit description is currently unavailable

SIT292Unit description is currently unavailable

SIT399Unit description is currently unavailable

SIT307Unit description is currently unavailable