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

Level 1

Degree-specific major units

Take all units (30 points):

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

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

Availability Unit code Unit name unit requirements
S1 CITS1003 Introduction to Cybersecurity
Incompatibility
CITS3004 Cybersecurity
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

Level 2

Degree-specific major units

Take all units (36 points):

Availability Unit code Unit name unit requirements
S2 CITS2002 Systems Programming
Prerequisites
completion of 6 points of programming-based units
Incompatibility
CITS1002 Programming and System
S1 CITS2005 Object Oriented Programming
Prerequisites
ATAR Mathematics Methods
or MATH1721 Mathematics Foundations: Methods or equivalent
and CITS1401 Computational Thinking with Python
Incompatibility
CITS1001 Software Engineering with Java
S1 CITS2200 Data Structures and Algorithms
Prerequisites
CITS1001 Software Engineering with Java and
Mathematics Methods ATAR
or MATH1721 Mathematics Foundations: Methods or equivalent
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 (42 points):

Availability Unit code Unit name unit requirements
S2 CITS3001 Algorithms, Agents and Artificial Intelligence
Prerequisites
CITS2200 Data Structures and Algorithms
S1 CITS3002 Computer Networks
Prerequisites
CITS1002 Programming and System
or CITS2002 Systems Programming
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
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
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
S1, S2 CITS4010 Computer Science Honours Research Project Part 1
Prerequisites
Enrolment in
in the BH008 Bachelor of Advanced Computer Science [Honours]
S1, S2 CITS4011 Computer Science Honours Research Project Part 2
Prerequisites
Enrolment in
in the BH008 Bachelor of Advanced Computer Science [Honours]
and CITS4010 Computer Science Honours Research Project Part 1
S2 CITS5503 Cloud Computing
Prerequisites

Enrolment in
( HON-CMSSE Computer Science and Software Engineering
or 62510 Master of Information Technology
or 62530 Master of Data Science
or 42630 Master of Business Analytics
or BH008 Bachelor of Advanced Computer Science [Honours]
or MJD-ICYDM International Cybersecurity
or MJD-CDSDM Computing and Data Science

and 12 points of programming-based units )
or Enrolment in 62550 Master of Professional Engineering Software Engineering specialisation
or
Enrolment in
Bachelor of Engineering (Honours) or an associated Combined Degree
and 120 Points
and 12 points of programming-based units
S1 CITS5508 Machine Learning
Prerequisites
Enrolment in
Computer Science and Software Engineering [Honours]
or 62510 Master of Information Technology
or 62530 Master of Data Science
or 63550 Master of Engineering
or 42630 Master of Business Analytics
or 62550 Master of Professional Engineering
or 53560 Master of Physics (specialised in Medical Physics)
or 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
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
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
Incompatibility
STAT3405 Introduction to Bayesian Computing and Statistics