Major Overview
- Description
Strong computing and data analysis skills are becoming necessary in an ever-increasing number of disciplines and workplace contexts. This major focuses on data and scientific computation including technologies for efficient and effective data collection, conversion, analysis, visualisation, interpretation, storage, search, synthesis and provision through the internet. Many professional organisations use computing resources extensively for information analysis, storage, communication and distribution, providing you with many diverse career options as a graduate. The Data Science major provides students with practical computing and information technology skills.
- Outcomes
Students are able to:
- apply computational and statistical techniques to analyse diverse real-world datasets
- construct data science analyses in incremental and integrated stages
- explain ethical and social aspects and opportunities and constraints of contemporary data science practice.
- demonstrate ability to work effectively as a team member and as a team leader
- communicate data analytics processes and results clearly in oral and written formats in professional and lay terms
- assess critically alternative solutions for the same data science project.
- Broadening guidelines
All students studying towards a Bachelor's Degree at UWA are required to Broaden their studies by completing a minimum of four units (24 points) of study outside their degree specific major. Broadening is your opportunity to explore other areas of interest, investigate new disciplines and knowledge paradigms and to shape your degree to suit your own aspirations and interests. Many of you will be able to undertake more than this minimum amount of broadening study and we encourage you to do so if this suits your aspirations. Over the next few months you will find here some broadening suggestions related to your degree-specific major. While we know that many students value guidance of this sort, these are only suggestions and students should not lose sight of the opportunity to explore that is afforded by your Broadening Choices. Advice can also be sought from your Allocated Student Advising Office.
- Prerequisites
Mathematics Methods ATAR OR Mathematics Applications ATAR with a mathematics unit taken in the first year.
Students without ATAR Mathematics will take two first-year mathematics units.
- Courses
Data Science can be taken as a degree-specific major in the following degree courses:
Example Study Plans
See study plans for more information.
Additional information
Units
Key to availability of units:
- S1
- Semester 1
- S2
- Semester 2
Level 1
Degree-specific major units
Take all units (24 points):
Students taking Data Science in conjunction with Engineering Science will gain credit for CITS2401 Computer Analysis and Visualisation (Engineering Science) by completing (CITS1401 Computational Thinking with Python and CITS2402 Introduction to Data Science). Students replace STAT1400 (Data Science major) with MATH1011 and MATH1012 (Engineering Science major)
| Availability | Unit code | Unit name | unit requirements |
|---|---|---|---|
| S1, S2 | CITS1401 | Computational Thinking with Python | |
| S1, S2 | CITS1402 | Relational Database Management Systems |
|
| S1 | PHIL1001 | Ethics for the Digital Age: An Introduction to Moral Philosophy |
|
| S1, S2 | STAT1400 | Statistics for Science |
Bridging units
Take units from this group (6 points) as directed by the School.
| Availability | Unit code | Unit name | unit requirements |
|---|---|---|---|
| S1, S2 | MATH1721 | Mathematics Foundations: Methods |
Level 2
Degree-specific major units
Take all units (18 points):
Level 3
Degree-specific major units
Take all units (30 points):
| Availability | Unit code | Unit name | unit requirements |
|---|---|---|---|
| S2 | CITS3200 | Professional Computing |
|
| S1 | CITS3401 | Data Warehousing |
|
| S1 | CITS3403 | Agile Web Development | |
| S2 | STAT3064 | Statistical Learning | |
| S2 | STAT3405 | Introduction to Bayesian Computing and Statistics |