Bachelor of Data Science
2025 Deakin University Handbook
| Year | 2025 course information |
|---|---|
| Award granted | Bachelor of Data Science |
| Deakin course code | S379E |
| Faculty | Faculty of Science, Engineering and Built Environment |
| Campus | ERC Institute, Singapore |
| Duration | 3 years full-time or part-time equivalent |
| Course Map - enrolment planning tool | The course maps are for new students commencing from Trimester 1 2025: Course maps for commencement in previous years are available on the Course Maps webpage or please contact a Student Adviser in Student Central. |
| Australian Qualifications Framework (AQF) recognition | The award conferred upon completion is recognised in the Australian Qualifications Framework at Level 7 |
New course from Trimester 1, 2025. This course is intended for students studying onshore in Singapore, with located learning support provided by ERC Institute. This course is not available to domestic and international students studying online or onshore at campuses in Australia. | |
Course sub-headings
- Course overview
- Indicative student workload
- Career opportunities
- Course Learning Outcomes
- Course rules
- Minors
- Course structure
- Details of minor sequences
Course overview
Propel yourself into a thriving field with Deakin's Bachelor of Data Science.
With every click, swipe, search, share and stream data is created at a phenomenal rate. Its volume and complexity give rise to considerable opportunities as businesses strive to harness the power of big data to remain competitive. Throughout this course you will explore the entire lifecycle of data. You will develop a deep understanding of how information is created, gathered, processed, and analysed as well as how it is used to generate insights and inform strategic decisions.
You will study innovative course content covering the latest data science trends, and insights. This ensures you graduate with a specialist, technical and highly relevant skill set that is sought after by employers across the globe. Explore different analytical methods, tools and techniques as you learn key concepts and deep dive into advanced topics in machine learning, AI and predictive analytics.
Want to hone your analytical skills for a rewarding career in data science?
Designed in collaboration with industry, the Bachelor of Data Science gives you ample opportunity to sharpen your skill set under the guidance and direction of supportive teaching staff. You’ll explore fundamental concepts across maths, stats and programming at the beginning of the course, before diving into more advanced topics in data wrangling, capture and mining, machine learning, deep learning and AI.
To differentiate your studies and focus your career towards the area that interests you most, you will also have the opportunity to undertake minor studies in a topic of your choosing.
Indicative student workload
You can expect to participate in a range of teaching activities each week. This could include lectures, 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.
Career opportunities
Data professionals are in high demand as organisations increasingly rely on skilled specialists to unlock hidden patterns in big data. This provides meaningful insights that inform decisions, drive business growth and increase their strategic advantage in the competitive business world.
No longer found solely amongst the big tech giants, data analysts are needed across every industry, opening a world of opportunities for your career.
As a graduate, you will have the skills, knowledge and industry connections to build a varied and sustainable career as a data analyst, data scientist, business strategist, data engineer, data architect, data visualisation specialist, information analyst or reporting analyst in the public and private sectors. Depending on your chosen industry or sector, you could be optimising digital marketing campaigns, developing new and innovative products and services, predicting customer sales patterns, or increasing productivity in areas such as sales or supply chain management.
Course location
This program, delivered by Deakin University and ERC Institute is an exciting partnership between two quality institutions. It provides an opportunity for international students to experience the best of Australian teaching and learning practices while based in Singapore. This course is not available to international students studying online or onshore at campuses in Australia.
Equipment requirements
The learning experiences and assessment activities within this course require that students have access to a range of technologies beyond a desktop computer or laptop. Students will be required to purchase minor equipment, such as small single board computers, microcontrollers and sensors, which will be used within a range of units in this course. This equipment is also usable by the student beyond their studies. Equipment requirements and details of suppliers will be provided on a per-unit basis. The indicative cost of this equipment for this course is AUD$500.
For information regarding hardware and software requirements, please refer to the Bring your own device (BYOD) guidelines via the School of Information Technology website in addition to the individual unit outlines in the Handbook.
Course Learning Outcomes
| Deakin Graduate Learning Outcomes | Course Learning Outcomes |
|---|---|
| Discipline-specific knowledge and capabilities | Develop a broad and coherent knowledge of data science, with detailed knowledge of the data analytics principles and approaches and knowledge, skills, tools, and methodologies for professional practice. |
| Communication | Communicate in a professional context to inform, motivate, and effect change, and to drive sustainable innovation, utilising a range of verbal, graphical and written methods, recognising the needs of diverse audiences. |
| Digital literacy | Utilise a range of digital technologies and information sources to discover, analyse, evaluate, select, process, and disseminate both technical and non-technical information in data science projects. |
| Critical thinking | Evaluate information and evidence, applying critical and analytical thinking and reasoning, technical skills, personal judgement, and values, in decision making processes. |
| Problem solving | Apply theoretical constructs and skills and critical analysis to real-world and ill-defined problems and develop innovative data analytics solutions. |
| Self-management | Work independently to apply knowledge and skills to new situations in professional practice and/or further learning in the field of data science with adaptability, autonomy, responsibility, and personal accountability for actions as a practitioner and a learner. |
| Teamwork | Contribute effectively as a skilled and knowledgeable individual to the processes and output of a work unit or team, applying specific knowledge and skills and using professional practices associated with the information technology industry. |
| Global citizenship | Apply professional and ethical standards and accountability in the preparation, handling, and analysis of data. |
Course rules
To complete the Bachelor of Data Science students must pass 24 credit points and meet the following course rules to be eligible to graduate:
- DAI001 Academic Integrity and Respect at Deakin (0-credit-point compulsory unit) in their first study period
- SIT010 Safety Induction Program (0-credit-point compulsory unit)
- STP010 Career Tools for Employability (0-credit-point compulsory unit)
- 17 credit points of core units
- 3 credit points of data science capstone units
- 1 minor (4 credit points)
- a maximum of 10 credit points at level 1
- a minimum of 14 credit points over levels 2 and 3
- a minimum of 6 credit points at level 3
Students are required to meet the University's academic progress and conduct requirements. See the enrolment codes and terminology to help make sense of the University’s vocabulary.
Minors
Refer to the details of each minor sequence for availability.
Course structure
Core
Year 1 - Trimester 1
| DAI001 | Academic Integrity and Respect at Deakin (0 credit points) |
| SIT010 | Safety Induction Program (0 credit points) |
| STP010 | Career Tools for Employability (0 credit points) |
| SIT111 | Computer Systems |
| SIT192 | Discrete Mathematics |
| SIT112 | Introduction to Data Science and Artificial Intelligence |
| SIT102 | Introduction to Programming |
Year 1 - Trimester 2
| SIT191 | Introduction to Statistics and Data Analysis |
| SIT232 | Object-Oriented Development |
| SIT292 | Linear Algebra for Data Analysis |
Year 1 Trimester 3
| SIT103 | Database Fundamentals |
Year 2 - Trimester 1
| SIT220 | Data Wrangling |
| SIT221 | Data Structures and Algorithms |
Plus 1 of:
| SIT202 | Computer Networks and Communication |
1 minor unit (1 credit point) OR 2 minor units (2 credit points)
Year 2 - Trimester 2
| SIT223 | Professional Practice in Information Technology # |
| SIT343 | Feature Generation and Engineering |
| SIT225 | Data Capture Technologies |
Plus one of
| SIT202 | Computer Networks and Communication |
Year 2 - Trimester 3
One (1) capstone unit (one (1) credit point):
| SIT306 | IT Placements and Industry Experience ^+ |
Year 3 - Trimester 1
| SIT330 | Natural Language Processing |
| SIT307 | Machine Learning |
Plus one (1) minor unit (1credit point)
Plus one (1) capstone unit (1credit point):
| SIT374 | Team Project (A) - Project Management and Practices ^ |
Year 3 - Trimester 2
| SIT319 | Deep Learning |
Plus one (1) minor unit (1credit point)
Plus one (1) capstone unit (1 credit point):
| SIT378 | Team Project (B) - Execution and Delivery ^ AND |
^ Offered in Trimester 1, Trimester 2 and Trimester 3.
# Corequisite of STP010 Career Tools for Employability (0-credit point compulsory unit).
+ Students must have completed STP010 Career Tools for Employability (0-credit point compulsory unit) and SIT223 Professional Practice in IT.
Details of minor sequences
Cloud technologies
Campuses
Burwood (Melbourne), Online
Unit set code
MN-S000011
Overview
In today’s data-driven digital world, cloud technologies are an area of significant business interest and their adoption and integration into business practices is growing at a rapid pace. This minor focuses on providing you with the knowledge, skills and expertise required to construct solutions using virtualisation, enterprise networks, system security and cloud infrastructure.
Units
| SIT233 | Cloud Computing |
| SIT226 | Cloud Automation Technologies |
| SIT323 | Cloud Native Application Development |
| SIT314 | Software Architecture and Scalability for Internet-Of-Things |
Full stack development
Campuses
Burwood (Melbourne), Online
Unit set code
MN-S000012
Overview
Web development is one of the fastest-growing careers in today’s economy, with growing demand for full stack web developers who are proficient in both front-end and back-end web development. Throughout this minor sequence, you will explore responsive web apps, full stack development across frontend applications and backend services, and mobile programming for Android and iOS.
Units
| SIT120 | Introduction to Responsive Web Apps |
| SIT331 | Full Stack Development: Secure Backend Services |
| SIT313 | Full Stack Development: Secure Frontend Applications |
| SIT305 | Mobile Application Development |