S2010 Bachelor of Applied Data Science

The course develops through theme studies in data challenges, techniques drawn from information technologies and mathematics, and applied studies. You will learn to apply the knowledge you gain in specialised information technology and mathematics studies to a series of projects. These skills will come together in a significant project unit in the third year of the course. You will also develop discipline specific skills in selected areas of study.

Part A. Data challenges
These studies will develop your analytical skills and advance your ability to apply key information technology and mathematical concepts and methods. These skills will be elaborated through studio-based learning using authentic case studies sourced from industry partners, and with examples drawn from STEM areas, business, law, the humanities and social sciences. Working in small teams or as individuals, you will realise the application of information technology and mathematical knowledge through authentic projects. Through designed experiences, you will learn to integrate a broad range of skills including collaborative work practices, communication, leadership and entrepreneurship to make you ready to approach the professions of the future.

Part B. Techniques for data science
Through this theme you will gain the technical foundation that underpins this program. You will acquire knowledge and skills in mathematics and the capacity to tackle challenging problems in a variety of situations. Through core data science studies, you will also attain the skills needed to effectively use, develop and manage complex data. These two areas are critical for tackling the diverse problems encountered in Part A of the course.

Part C. Applied studies
These studies will provide the foundation required to advance cross-disciplinary analytical thought. You will undertake a sequence of study in a discipline. The selection of studies available will ensure you can explore new and diverse areas and develop core strengths in studies that relate to data applications. In addition to the disciplinary expertise, you will develop an appreciation for the culture of your selected discipline, the development of ideas in a given subject area, and the contexts in which this is applied. Data applications relating to these subjects can then be incorporated through the learning experiences across Part A.

Part D. Elective

This will enable you to further develop your technical skills or extend your knowledge in your selected applied studies. Alternatively you can select units from across the university in which you are eligible to enrol.

Course progression map

The course progression map provides guidance on unit enrolment for each semester of study.

The course comprises 144 points of which 132 points are focused on Applied Data Science.

The course is structured in four parts: Part A. Data Challenges (36 points), Part B. Techniques for data science (72 points), Part C. Applied studies (24 points) and Part D. Elective (12 points).

No more than 10 units (60 points) are to be completed at level 1 in the course.

Units are 6 points unless otherwise stated.

Part A. Data challenges (36 points)

You complete:

• ADS1001 Data challenges 1
• ADS1002 Data challenges 2
• ADS2001 Data challenges 3
• ADS2002 Data challenges 4
• ADS3001 Advanced data challenges (12 points)

Part B. Techniques for data science (72 points)

You complete:

(i) Six Information Technology units (36 points) from the following:

• FIT1008 Fundamentals of Algorithms
• FIT1045 Introduction to programming
• MAT1830 Discrete mathematics for computer science
• FIT2086 Modelling for data analysis
• FIT3152 Data analytics or FIT3154 Advanced data analysis  
• FIT3181 Deep learning

and

(ii) Six Mathematical Science units (36 points) from the following:

Clayton

• MAT1841 Continuous mathematics for computer science
• MTH2019 Multivariate mathematics for data science
• MTH2051 Introduction to computational mathematics
• MTH2222 Maths of uncertainty or MTH2225 Mathematics of uncertainty (advanced) 
• MTH3241 Random processes in the sciences and engineering or MTH3320 Computational linear algebra
• MTH3330 Optimisation and operations research

Malaysia

Students undertaking their studies at the Malaysia campus complete the following sequence: 

• ENG1090 Foundation mathematics
• ENG1005 Engineering mathematics
• MTH2019 Multivariate mathematics for data science
• MTH2051 Introduction to computational mathematics
• MTH3320 Computational linear algebra
• MTH3330 Optimisation and operations research

Part C. Applied studies (24 points)

You complete 24 points of complementary studies from a defined discipline area listed below.  Normally, you complete:

• 2 units at level 1 (12 points)
• 2 units at level 2, 3 or 4 (12 points)

Clayton:

Anatomy and developmental biology

• BIO1011 Blueprints for life
• BIO1022 Life on earth or BIO1042 Life in the environment
• DEV2011 Early human development from cells to tissues
• DEV2022 Human anatomy and development: Tissues and body systems

Applied and statistical mathematics

• MTH2132 The nature and beauty of mathematics
• STA1010 Statistical methods for science

two of:

• MTH2222  Mathematics of uncertainty or MTH2225 Mathematics of uncertainty (advanced)
• MTH2232 Mathematical statistics
• MTH2032 Differential equations 
• MTH2051 Introduction to computational mathematics

Astronomy

No more than one of

• ASP1010 Earth to cosmos - introductory astronomy
• ASP1022 Life in the universe – astrobiology

And one of

• PHS1011 Classical physics and relativity
• PHS1001 Foundations physics
• PHS1002 Physics for engineering

And

• ASP2011 Astronomy
• ASP2062 Introduction to astrophysics

Biochemical science

• BIO1011 Blueprints for life or CHM1011 Chemistry 1 or CHM1051 Chemistry 1 Advanced
• BIO1022 Life on earth or CHM1022 Chemistry 2 or CHM1052 Chemistry 2 Advanced
• BCH2011 Structure and functions of cellular biomolecules
• BCH2022 Metabolic basis of human diseases

Biological science and genetics

• BIO1011 Blueprints for life
• BIO1022 Life on earth or BIO1042 Life in the environment

One of:

• BIO2011 Ecology and biodiversity and BIO2040 Conservation biology
• GEN2041 Foundations of genetics and GEN2052 Genomics and population genetics

Business analytics

• ETC1000 Business and economic statistics
• ETC1010 Introduction to data analysis
• ETC2420 Statistical thinking
• ETC3550 Applied forecasting

Business information systems

• FIT2001 Systems development
• FIT2094 Databases
• FIT3158 Business decision modelling
• FIT3138 Real time enterprise systems

Chemical sciences

• CHM1011 Chemistry 1 or CHM1051 Chemistry 1 Advanced
• CHM1022 Chemistry 2 or CHM1052 Chemistry 2 Advanced
• CHM2911 Inorganic and organic chemistry
• CHM2922 Spectroscopy and analytical chemistry

Computer systems engineering

• ENG1012 Engineering design
• ECE2072 Digital systems
• ECE3141 Information and networks
• ECE4076 Computer vision

Crime and society

• ATS1421 Understanding crime
• ATS1423 Criminal justice in practice