Data Science major

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.

Degree-specific major

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

BP004 Bachelor of Science
BH005 Bachelor of Philosophy (Honours)

Overview of unit sequence

Data Science is a degree-specific single major comprising:

  • two Level 1 units
  • two Level 2 units
  • four Level 3 units
  • one Level 1 complementary unit
  • one Level 2 complementary unit
  • one Level 3 complementary unit
Key to availability of units:
S1 = Semester 1; S2 = Semester 2

Level 1

Degree-specific major units
Take all units (12 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).

Availability Unit code Unit name unit requirements
S1, S2 CITS1401 Computational Thinking with Python
Prerequisites:
Mathematics Applications ATAR
or
WACE Mathematics 2C/2D
or
MATH1720 Mathematics Fundamentals
or
equivalent
or
higher
S2 CITS1402 Relational Database Management Systems
Prerequisites:
Mathematics Applications ATAR
or
WACE Mathematics 2C/2D
or
MATH1720 Mathematics Fundamentals
or
equivalent
or
higher
Incompatibility:
CITS2232 Databases
Complementary units
Take all complementary units from this group (6 points):
Availability Unit code Unit name unit requirements
S1, S2 STAT1400 Statistics for Science
Prerequisites:
Mathematics Applications ATAR
or
Mathematics Methods ATAR
or
WACE Mathematics 2C/2D
or
MATH1720 Mathematics Fundamentals
or
equivalent
or
higher
Incompatibility:
STAT1520 Economic and Business Statistics

Level 2

Degree-specific major units
Take all units (12 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:
STAT1400 Statistics for Science
or
STAT1520 Economic and Business Statistics
or
MATH1002 Mathematical Methods 2
or
MATH1012 Mathematical Theory and Methods
or
MATH1020 Calculus, Probability and Statistics (students enrolled in the Master of Data Science may take one of these units as a co-requisite)
Complementary units
Take the following complementary unit:
Availability Unit code Unit name unit requirements
S2 STAT2402 Analysis of Observations
Prerequisites:
STAT1400 Statistics for Science
or
STAT1520 Economic and Business Statistics
or
MATH1002 Mathematical Methods 2
or
MATH1012 Mathematical Theory and Methods
or
MATH1020 Calculus, Probability and Statistics (students enrolled in the Master of Data Science may take one of these units as a co-requisite)

Level 3

Degree-specific major units
Take all units (24 points):
Availability Unit code Unit name unit requirements
S2 CITS3200 Professional Computing
Prerequisites:
completion of 12 points from: CITS2002 Systems Programming; CITS2200 Data Structures and Algorithms; CITS2401 Computer Analysis and Visualisation
or
CITS2402 Introduction to Data Science
S1 CITS3401 Data Warehousing
Prerequisites:
CITS1402 Relational Database Management Systems (formerly CITS1402 Introduction to Databases)
or
CITS2232 Databases;
for pre-2012 courses: CITS1402 Relational Database Management Systems (formerly CITS1402 Introduction to Databases)
or
CITS2232 Databases
Incompatibility:
CITS4243 Advanced Databases
S1 CITS3403 Agile Web Development
Prerequisites:
CITS1002 Programming and Systems
or
CITS1401 Computational Thinking with Python (formerly Problem Solving and Programming)
or
CITS1001 Software Engineering with Java (formerly Object-oriented Programming and Software Engineering)
or
CITS2002 Systems Programming
Incompatibility:
CITS4230 Internet Technologies
S2 STAT3064 Statistical Learning
Prerequisites:
STAT2401 Analysis of Experiments
or
STAT2062 Fundamentals of Probability with Applications
and
MATH1720 Mathematics Fundamentals
or
MATH1011 Multivariable Calculus
Complementary units
Take the following complementary unit:
Availability Unit code Unit name unit requirements
S2 CITS3004 Cybersecurity
Prerequisites:
completion of 12 points of the following: CITS1001 Object-oriented Programming and Software Engineering
or
CITS1401 Problem Solving and Programming
or
CITS2002 Systems Programming
or
CITS2200 Data Structures and Algorithms
or
CITS2401 Computer Analysis and Visualisation
or
equivalent.
Incompatibility:
CITS3002 Networks and Security prior to 2018

Choosing your degree-specific major

You must satisfy the requirements of a degree-specific major before you complete your course. The flexible structure of an undergraduate course allows you to try out a number of different subjects to see what interests you before nominating your degree-specific major. You have the choice to either nominate your degree-specific major when you first enrol in the course or delay nominating it until your second year.

To plan the first year of your study without nominating a degree-specific major, you are advised to choose units that will pave the way to two or more degree-specific majors that are of interest to you. For examples of the choice of units available in first year, search the first-year study plans .

To fully understand the structure of an undergraduate course, read the course structure information and the Undergraduate Degree Course Rules.

The following example illustrates how the Data Science degree-specific major can be included in the Bachelor of Science course.

Example: Course Study Plan: CSP004-DATSC

There are more choices open to you. For more examples, search the study plans .

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

Choosing a second major

You also have the option to choose a second major from those available in the Bachelor of Arts, Bachelor of Biomedical Science, Bachelor of Commerce, Bachelor of Design Only available to re-enrolling students. or Bachelor of Science course, giving you the opportunity to pursue your interests no matter how different they are.

The following example illustrates how the Data Science degree-specific major can be combined with a second major in the Bachelor of Science course.

Example: Course Study Plan: CSP004-DATSC-Generic

For more examples of combinations of majors, search the study plans .

Data Science can also be taken as a second major.

Second major

Data Science can be taken as a second major in the following degree courses:

Overview of unit sequence

The Data Science second major is a single major comprising:

  • two Level 1 units
  • two Level 2 units
  • four Level 3 units
  • one Level 1 additional unit
Key to availability of units:
S1 = Semester 1; S2 = Semester 2

Level 1

Second major units
Take all units (12 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).

Availability Unit code Unit name unit requirements
S1, S2 CITS1401 Computational Thinking with Python
Prerequisites:
Mathematics Applications ATAR
or
WACE Mathematics 2C/2D
or
MATH1720 Mathematics Fundamentals
or
equivalent
or
higher
S2 CITS1402 Relational Database Management Systems
Prerequisites:
Mathematics Applications ATAR
or
WACE Mathematics 2C/2D
or
MATH1720 Mathematics Fundamentals
or
equivalent
or
higher
Incompatibility:
CITS2232 Databases
Second major units
Take all additional units from this group (6 points):
Availability Unit code Unit name unit requirements
S1, S2 STAT1400 Statistics for Science
Prerequisites:
Mathematics Applications ATAR
or
Mathematics Methods ATAR
or
WACE Mathematics 2C/2D
or
MATH1720 Mathematics Fundamentals
or
equivalent
or
higher
Incompatibility:
STAT1520 Economic and Business Statistics

Level 2

Second major units
Take all units (12 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:
STAT1400 Statistics for Science
or
STAT1520 Economic and Business Statistics
or
MATH1002 Mathematical Methods 2
or
MATH1012 Mathematical Theory and Methods
or
MATH1020 Calculus, Probability and Statistics (students enrolled in the Master of Data Science may take one of these units as a co-requisite)

Level 3

Second major units
Take all units (24 points):
Availability Unit code Unit name unit requirements
S2 CITS3200 Professional Computing
Prerequisites:
completion of 12 points from: CITS2002 Systems Programming; CITS2200 Data Structures and Algorithms; CITS2401 Computer Analysis and Visualisation
or
CITS2402 Introduction to Data Science
S1 CITS3401 Data Warehousing
Prerequisites:
CITS1402 Relational Database Management Systems (formerly CITS1402 Introduction to Databases)
or
CITS2232 Databases;
for pre-2012 courses: CITS1402 Relational Database Management Systems (formerly CITS1402 Introduction to Databases)
or
CITS2232 Databases
Incompatibility:
CITS4243 Advanced Databases
S1 CITS3403 Agile Web Development
Prerequisites:
CITS1002 Programming and Systems
or
CITS1401 Computational Thinking with Python (formerly Problem Solving and Programming)
or
CITS1001 Software Engineering with Java (formerly Object-oriented Programming and Software Engineering)
or
CITS2002 Systems Programming
Incompatibility:
CITS4230 Internet Technologies
S2 STAT3064 Statistical Learning
Prerequisites:
STAT2401 Analysis of Experiments
or
STAT2062 Fundamentals of Probability with Applications
and
MATH1720 Mathematics Fundamentals
or
MATH1011 Multivariable Calculus

Choosing a second major

The flexible structure of an undergraduate course allows you the option of including a second major. You have the choice to either nominate your second major when you first enrol in the course or delay it until your second year. For a major to be recorded on your academic record it must be nominated before the requirements of the course are completed.

To fully understand the structure of an undergraduate course, read the course structure information and the Undergraduate Degree Course Rules.

The following example illustrates how Data Science can be included as a second major in an undergraduate degree course.

Example: Course Study Plan: CSPGeneric-DSMGeneric-DATSC

For more examples of combinations of majors, search the study plans .

Data Science can also be taken as a degree-specific major.