Major Overview
 Description
Strong computing and data analysis skills are becoming necessary in an everincreasing 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 realworld 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 degreespecific 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 firstyear mathematics units.
 Courses
Data Science can be taken as a degreespecific 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
Degreespecific 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)
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
Degreespecific major units
Take all units (18 points):
Level 3
Degreespecific 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 