Bachelor of Advanced Computing
Computational Data Science major
Please check the current students website (Find a unit of study) for up-to-date information on units of study including availability.
For a standard enrolment plan for the Bachelor of Advanced Computing with a major in Computational Data Science visit CUSP.
| Unit of study | Credit points | A: Assumed knowledge P: Prerequisites C: Corequisites N: Prohibition | Session |
|---|---|---|---|
Computational Data Science |
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Computational Data Science major |
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| Achievement of a major in Computational Data Science requires 48 credit points from this table including: | |||
| (i) 12 credit points of 1000-level core units | |||
| (ii) 18 credit points of 2000-level core units | |||
| (iii) 6 credit points of 3000-level core units | |||
| (iv) 12 credit points of 3000-level selective units | |||
Computational Data Science minor |
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| Achievement of a minor in Computational Data Science requires 36 credit points from this table including: | |||
| (i) 12 credit points of 1000-level core units. | |||
| (ii) 18 credit points of 2000-level core units. | |||
| (iii) 6 credit points of 3000-level selective units. | |||
Units of Study |
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1000-level units of study |
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Core units |
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| DATA1001 Foundations of Data Science |
6 | N DATA1901 or MATH1005 or MATH1905 or MATH1015 or MATH1115 or ENVX1001 or ENVX1002 or ECMT1010 or BUSS1020 or STAT1021 |
Semester 1 Semester 2 |
| DATA1901 Foundations of Data Science (Adv) |
6 | A An ATAR of 95 or more N MATH1005 or MATH1905 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 or DATA1001 or MATH1115 or MATH1015 or STAT1021 |
Semester 1 Semester 2 |
| INFO1110 Introduction to Programming |
6 | N INFO1910 or INFO1103 or INFO1903 or INFO1105 or INFO1905 or ENGG1810 |
Semester 1 Semester 2 |
| INFO1910 Introduction to Programming (Advanced) |
6 | A ATAR sufficient to enter Dalyell program, or passing an online programming knowledge test, which will be administered during the O-week prior to the commencement of the semester N INFO1110 OR INFO1103 OR INFO1903 OR INFO1105 OR INFO1905 Note: Department permission required for enrolment |
Semester 1 Semester 2 |
2000-level units of study |
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Core units |
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| COMP2123 Data Structures and Algorithms |
6 | P INFO1110 OR INFO1910 OR INFO1113 OR DATA1002 OR DATA1902 OR INFO1103 OR INFO1903 N INFO1105 OR INFO1905 OR COMP2823 |
Semester 1 |
| COMP2823 Data Structures and Algorithms (Adv) |
6 | P Distinction level results in (INFO1110 OR INFO1910 OR INFO1113 OR DATA1002 OR DATA1902 OR INFO1103 OR INFO1903) N INFO1105 OR INFO1905 OR COMP2123 |
Semester 1 |
| DATA2001 Data Science, Big Data and Data Variety |
6 | P DATA1002 OR DATA1902 OR INFO1110 OR INFO1910 OR INFO1903 OR INFO1103 or ENGG1810 N DATA2901 |
Semester 1 |
| DATA2901 Big Data and Data Diversity (Advanced) |
6 | P 75% or above from (DATA1002 OR DATA1902 OR INFO1110 OR INFO1903 OR INFO1103 or ENGG1810) N DATA2001 |
Semester 1 |
| DATA2002 Data Analytics: Learning from Data |
6 | A Successful completion of a first-year or second-year unit in statistics or data science including a substantial coding component. The content from STAT2X11 will help but is not considered essential. Students who are not comfortable using the R software for statistical analysis should familiarise themselves before attempting the unit, e.g. taking OLET1632: Shark Bites and Other Data Stories P DATA1X01 or ENVX1002 or [MATH1X05 and MATH1XXX (excluding MATH1X05)] or BUSS1020 or ECMT1010 N STAT2012 or STAT2912 or DATA2902 |
Semester 2 |
| DATA2902 Data Analytics: Learning from Data (Adv) |
6 | A Successful completion of a first-year or second-year unit in statistics or data science including a substantial coding component. The content from STAT2X11 will help but is not considered essential. Students who are not comfortable using the R software for statistical analysis should familiarise themselves before attempting the unit, e.g. taking OLET1632: Shark Bites and Other Data Stories P A mark of 65 or above in (DATA1X01 or ENVX1002 or [MATH1X05 and MATH1XXX (excluding MATH1X05)] or BUSS1020 or ECMT1010) N STAT2012 or STAT2912 or DATA2002 |
Semester 2 |
3000-level units of study |
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Core units |
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| DATA3888 Data Science Capstone |
6 | P DATA2001 or DATA2901 or DATA2002 or DATA2902 or STAT2912 or STAT2012 |
Semester 1 |
Selective units |
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| COMP3027 Algorithm Design |
6 | A MATH1004 OR MATH1904 OR MATH1064 P COMP2123 OR COMP2823 OR INFO1105 OR INFO1905 N COMP2007 OR COMP2907 OR COMP3927 |
Semester 1 |
| COMP3927 Algorithm Design (Adv) |
6 | A MATH1004 OR MATH1904 OR MATH1064 P Distinction level results in (COMP2123 OR COMP2823 OR INFO1105 OR INFO1905) N COMP2007 OR COMP2907 OR COMP3027 |
Semester 1 |
| COMP3308 Introduction to Artificial Intelligence |
6 | A Algorithms. Programming skills (e.g. Java, Python, C, C++, Matlab) N COMP3608 |
Semester 1 |
| COMP3608 Introduction to Artificial Intelligence (Adv) |
6 | A Algorithms. Programming skills (e.g. Java, Python, C, C++, Matlab) P Distinction-level results in at least one 2000 level COMP or MATH or SOFT unit N COMP3308 COMP3308 and COMP3608 share the same lectures, but have different tutorials and assessment (the same type but more challenging). |
Semester 1 |
| DATA3404 Scalable Data Management |
6 | A This unit of study assumes that students have previous knowledge of database structures and of SQL. The prerequisite material is covered in DATA2001 or ISYS2120. Familiarity with a programming language (e.g. Java or C) is also expected P DATA2001 OR DATA2901 OR ISYS2120 OR INFO2120 OR INFO2820 N INFO3504 OR INFO3404 |
Semester 1 |
| DATA3406 Human-in-the-Loop Data Analytics |
6 | A Basic statistics, database management, and programming P (DATA2001 OR DATA2901) AND (DATA2002 OR DATA2902) |
Semester 2 |