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 Introduction to computer science
• FIT1045 Algorithms and programming fundamentals in python
• MAT1830 Discrete mathematics for computer science
• FIT2086 Modelling for data analysis
• FIT3154 Advanced data analysis
• FIT3181 Deep learning

and

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

• MTH1020 Analysis of change^
• MTH1030 Techniques for modelling or MTH1035 Techniques for modelling (advanced)
• MTH2019 Multivariate mathematics for data science
• MTH2222 Maths of uncertainty or MTH2225 Mathematics of uncertainty (advanced) or MTH2051 Introduction to computational mathematics
• MTH3241 Random processes in the sciences and engineering or MTH3320 Computational linear algebra
• MTH3330 Optimisation and operations research

^ If you have completed VCE Specialist maths and achieved a raw score of 30 or higher replace the first three units in the sequence with the following:

• MTH1030 Techniques for modelling or MTH1035 Techniques for modelling (advanced)
• MTH2010 Multivariable calculus or MTH2015 Multivariable calculus (advanced)
• MTH2021 Linear algebra with applications or MTH2025 Linear algebra (advanced)

Note 1: Students admitted into the course from Gaokao must complete MTH1010 Functions and their applications prior to MTH1020 Analysis of change. MTH1010 will count as an elective.