Double Major Overview

Description

Organisations across all industries and sectors are increasingly using data science in information analysis, storage, communication and distribution. In the Bachelor of Advanced Computer Science (Computing and Data Science) you will acquire the computing and data science knowledge and skills to understand and apply appropriate analytical methods to transform the way an organisation achieves its objectives, to deal effectively with large data management tasks, to master the statistical and machine learning foundations on which data analytics is built, and to evaluate and communicate the effectiveness of new technologies; putting you in high demand in the growing data science job market and providing you with many diverse career options as a graduate. This major will prepare you with practical skills in data science technologies for data collection, cleaning, conversion, analysis, visualisation, interpretation, storage, search, synthesis and cloud management.

Outcomes

Students are able to (1) apply data visualisation, interpretation, storage and synthesis skills in complex real-world settings; (2) use predictive modelling to forecast future trends, outcomes and scenarios; (3) discuss the opportunities and constraints of contemporary data science practice as it applies in various industries; (4) work effectively as a team member and as a team leader for real-world data science projects; (5) communicate data science, modelling and analytics clearly in oral, graphical and written formats; and (6) extend knowledge in data science through research, experimentation and analysis.

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

Incompatibilities

MJD-CMPSC Computer Science (ID 468) MJD-DATSC Data Science (ID 700) MJD-ARTIF Artificial Intelligence (ID 4873) MJD-INTCY International Cybersecurity (ID 4870) MJD-CYBER Cybersecurity (ID 4874)

Courses

Computing and Data Science can only be taken as a degree-specific major in the following degree courses:

Example Study Plans

See study plans for more information.

Units

Key to availability of units:
S1 = Semester 1; S2 = Semester 2; N/A = not available in 2022

Note:Units that are indicated as N/A may be available in 2023 or 2024.

Level 1

Degree-specific major units
Take all units (36 points):

Students are recommended to take MATH1722 Mathematics Foundations: Specialist as an elective.

Availability Unit code Unit name unit requirements
S1, S2 CITS1001 Software Engineering with Java
Prerequisites:
Mathematics Applications ATAR
or
MATH1720 Mathematics Fundamentals
or
equivalent
Co-requisites:
Nil
Incompatibility:
Nil
S1 CITS1003 Introduction to Cybersecurity
Prerequisites:
Nil
Co-requisites:
Nil
Incompatibility:
CITS3004 Cybersecurity
S1, S2 CITS1401 Computational Thinking with Python
Prerequisites:
Mathematics Applications ATAR
or
MATH1720 Mathematics Fundamentals
or
equivalent
Co-requisites:
Nil
Incompatibility:
CITS2401 Computer Analysis and Visualisation
S2 CITS1402 Relational Database Management Systems
Prerequisites:
Mathematics Applications ATAR
or
MATH1720 Mathematics Fundamentals
or
equivalent
Co-requisites:
Nil
Incompatibility:
CITS2232 Databases
S1 PHIL1001 Ethics for the Digital Age: An Introduction to Moral Philosophy
Prerequisites:
Nil.
Co-requisites:
Nil.
Incompatibility:
PHIL1107 Ethics, Free Will and Meaning
S1, S2 STAT1400 Statistics for Science
Prerequisites:
Mathematics Applications ATAR
or
MATH1720 Mathematics Fundamentals
or
equivalent
Co-requisites:
Nil
Incompatibility:
STAT1520 Economic and Business Statistics

Level 2

Degree-specific major units
Take all units (30 points):
Availability Unit code Unit name unit requirements
S2 CITS2002 Systems Programming
Prerequisites:
completion of 6 points of programming-based units
Co-requisites:
Nil
Incompatibility:
CITS1002 Programming and System
S1 CITS2200 Data Structures and Algorithms
Prerequisites:
CITS1001 Software Engineering with Java and ( Mathematics Methods ATAR
or
MATH1721 Mathematics Foundations: Methods
or
equivalent )
Co-requisites:
Nil
Incompatibility:
Nil
S2 CITS2402 Introduction to Data Science
Prerequisites:
CITS1401 Computational Thinking with Python
or
CITS2401 Computer Analysis and Visualisation
Co-requisites:
Nil
Incompatibility:
Nil
S1 STAT2401 Analysis of Experiments
Prerequisites:
Mathematics Applications ATAR
or
MATH1720 Mathematics Fundamentals
or
equivalent
Co-requisites:
Nil
Incompatibility:
Nil
S2 STAT2402 Analysis of Observations
Prerequisites:
Nil
Co-requisites:
Nil.
Incompatibility:
Nil

Level 3

Degree-specific major units
Take all units (42 points):
Availability Unit code Unit name unit requirements
S2 CITS3001 Algorithms, Agents and Artificial Intelligence
Prerequisites:
CITS2200 Data Structures and Algorithms
Co-requisites:
Nil
Incompatibility:
Nil
S1 CITS3002 Computer Networks
Prerequisites:
CITS1002 Programming and System
or
CITS2002 Systems Programming
Co-requisites:
Nil
Incompatibility:
Nil
S2 CITS3200 Professional Computing
Prerequisites:
completion of at least 84 points, including ( CITS1401 Computational Thinking with Python
or
CITS1001 Software Engineering with Java ) and ( CITS2002 Systems Programming
or
CITS2200 Data Structures and Algorithms
or
CITS2402 Introduction to Data Science )
Co-requisites:
Nil
Incompatibility:
CITS5206 Professional Computing
S1 CITS3401 Data Warehousing
Prerequisites:
CITS1402 Relational Database Management Systems
Co-requisites:
Nil
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
Co-requisites:
Nil
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 )
Co-requisites:
Nil
Incompatibility:
STAT4067 Applied Statistics and Data Visualisation and STAT5061 Statistical Data Science
S1 STAT3401 Advanced Data Analysis
Prerequisites:
( Course Enrolment in the MJD-QTMTD Quantitative Methods major
or
the MJD-CDSDM Computing and Data Science major
or
the MJD-HSDEM Human Sciences and Data Analytics major ) and ( STAT2401 Analysis of Experiments and STAT2402 Analysis of Observations )
or
STAT2062 Fundamentals of Probability with Applications
Co-requisites:
Nil
Incompatibility:
STAT4065 Multilevel and Mixed-Effects Modelling

Level 4

Degree-specific major units
Take all units (48 points):
Availability Unit code Unit name unit requirements
N/A CITS4010 Computer Science Honours Research Project Part 1
Prerequisites:
enrolment in the Bachelor of Advanced Computer Science [Honours]
Co-requisites:
Nil
Incompatibility:
Nil
N/A CITS4011 Computer Science Honours Research Project Part 2
Prerequisites:
CITS4010 Computer Science Honours Research Project Part 1
Co-requisites:
Nil
Incompatibility:
Nil
S2 CITS5503 Cloud Computing
Prerequisites:
Enrolment in in the ( HON-CMSSE Computer Science and Software Engineering [Honours]
or
the 62510 Master of Information Technology
or
the 62530 Master of Data Science
or
the 42630 Master of Business Analytics )
or
( the 62550 Master of Professional Engineering and the EP-SOFT Software Engineering specialisation )
or
( the BH008 Bachelor of Advanced Computer Science [Honours]
or
the MJD-ICYDM International Cybersecurity major
or
the MJD-CDSDM Computing and Data Science major ) and completion of 12 points of programming-based units
Co-requisites:
Nil
Incompatibility:
Nil
S1 CITS5508 Machine Learning
Prerequisites:
Enrolment in in the ( HON-CMSSE Computer Science and Software Engineering [Honours]
or
the 62510 Master of Information Technology
or
the 62530 Master of Data Science
or
the 63550 Master of Engineering
or
the 42630 Master of Business Analytics )
or
( the 62550 Master of Professional Engineering and the SP-ESOFT Software Engineering specialisation )
or
( the 53560 Master of Physics and the SP-MEDPH Medical Physics specialisation )
or
( the BH008 Bachelor of Advanced Computer Science [Honours] and the MJD-CDSDM Computing and Data Science major
or
the MJD-ARIDM Artificial Intelligence major ) and completion of 12 points of programming-based units
Co-requisites:
Nil
Incompatibility:
Nil
S1 STAT4062 Statistical Modelling and Inference
Prerequisites:
( Course Enrolment in the HON-MTHST Mathematics and Statistics [Honours]
or
the MJD-CDSDM Computing and Data Science major ) and ( STAT3062 Statistical Science
or
STAT3401 Advanced Data Analysis ) and STAT3064 Statistical Learning
Co-requisites:
Nil
Incompatibility:
Nil
S2 STAT4066 Bayesian Computing and Statistics
Prerequisites:
( Course Enrolment in the MJD-CDSDM Computing and Data Science major
or
the HON-MTHST Mathematics and Statistics [Honours]
or
the 62530 Master of Data Science ) and ( STAT2401 Analysis of Experiments and STAT2402 Analysis of Observations )
or
STAT2062 Fundamentals of Probability with Applications
Co-requisites:
Nil
Incompatibility:
STAT3405 Introduction to Bayesian Computing and Statistics