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 (1) apply computational and statistical techniques to analyse diverse real-world datasets; (2) construct data science analyses in incremental and integrated stages; (3) explain ethical and social aspects and opportunities and constraints of contemporary data science practice.; (4) demonstrate ability to work effectively as a team member and as a team leader; (5) communicate data analytics processes and results clearly in oral and written formats in professional and lay terms; and (6) 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:

If you are unsure what else to study alongside this major, please read our Faculty and Industry recommended electives and second majors.
Example Study Plans

See study plans for more 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)

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
Prerequisites
Mathematics Applications ATAR
or MATH1720 Mathematics Fundamentals or equivalent
Incompatibility
CITS2401 Computer Analysis and Visualisation
S2 CITS1402 Relational Database Management Systems
Prerequisites
Mathematics Applications ATAR
or MATH1720 Mathematics Fundamentals or equivalent
Incompatibility
CITS2232 Databases
S1 PHIL1001 Ethics for the Digital Age: An Introduction to Moral Philosophy
Incompatibility
PHIL1107 Ethics, Free Will and Meaning
S1, S2 STAT1400 Statistics for Science
Prerequisites
Mathematics Applications ATAR
or MATH1720 Mathematics Fundamentals or equivalent
Incompatibility
STAT1520 Economic and Business Statistics
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
Prerequisites
Mathematics Applications ATAR
or MATH1720 Mathematics Fundamentals
or
Mathematics Methods ATAR [with a scaled score of less than 50] or equivalent
Incompatibility
MATH1011 Multivariable Calculus
and MATH1012 Mathematical Theory and Methods
and STAT1520 Economics and Business Statistics

Level 2

Degree-specific major units

Take all units (18 points):

Availability Unit code Unit name unit requirements
S2 CITS2402 Introduction to Data Science
Prerequisites
CITS1401 Computational Thinking with Python
or CITS2401 Computer Analysis and Visualisation
S1 STAT2401 Analysis of Experiments
Prerequisites
Mathematics Applications ATAR
or MATH1720 Mathematics Fundamentals or equivalent
S2 STAT2402 Analysis of Observations
Prerequisites
Mathematics Applications ATAR
or MATH1720 Mathematics Fundamentals or equivalent

Level 3

Degree-specific major units

Take all units (30 points):

Availability Unit code Unit name unit requirements
S2 CITS3200 Professional Computing
Prerequisites
completion of at least 84 points, including
CITS2002 Systems Programming
or CITS2200 Data Structures and Algorithms
or CITS2402 Introduction to Data Science
Incompatibility
CITS5206 Professional Computing
S1 CITS3401 Data Warehousing
Prerequisites
CITS1402 Relational Database Management Systems and completion of 6 points of programming based units
Incompatibility
CITS5504 Data Warehousing
S1 CITS3403 Agile Web Development
Prerequisites
CITS1001 Software Engineering with Java
or CITS1401 Computational Thinking with Python
or CITS2002 Systems Programming or equivalent
Incompatibility
CITS5505 Agile Web Development
S2 STAT3064 Statistical Learning
Prerequisites
Enrolment in
MJD-CDSDM Computing and Data Science
or MJD-MTHST Mathematics and Statistics
or MJD-DATSC Data Science
or MJD-HSDEM Human Sciences and Data Analytics
and
STAT2401 Analysis of Experiments
and STAT2402 Analysis of Observations
or
STAT2062 Fundamentals of Probability with Applications
or STAT2063 Probabilistic Methods and their Applications
Incompatibility
STAT4067 Applied Statistics and Data Visualisation
and STAT5061 Statistical Data Science
S2 STAT3405 Introduction to Bayesian Computing and Statistics
Prerequisites

Course Enrolment in
the MJD-DATSC Data Science major
or the MJD-QTMTD Quantitative Methods major
and STAT2401 Analysis of Experiments
and STAT2402 Analysis of Observations
or STAT2062 Fundamentals of Probability with Applications
Incompatibility
STAT4066 Bayesian Computing and Statistics