Unit of Study Table
| 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: | |||
| 12 credit points of 1000-level core units; | |||
| 18 credit points of 2000-level core units; | |||
| 6 credit points of 3000-level core units; | |||
| 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: | |||
| 12 credit points of 1000-level core units; | |||
| 18 credit points of 2000-level core units; | |||
| 6 credit points of 3000-level selective units. | |||
Units of Study |
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| The relevant units of study are listed below | |||
1000-level units of study |
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Core units |
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| DATA1001 Foundations of Data Science |
6 | N MATH1005 or MATH1905 or MATH1015 or MATH1115 or ENVX1001 or ENVX1002 or ECMT1010 or BUSS1020 or STAT1021 |
Semester 1 Semester 2 |
| INFO1110 Introduction to Programming |
6 | Intensive July 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 INFO1113 OR DATA1002 OR INFO1103 OR INFO1903 N INFO1105 OR INFO1905 OR COMP2823 |
Semester 1 |
| COMP2823 Data Structures and Algorithms (Adv) |
6 | A Distinction-level result in at least one the listed 1000 level programming units P Distinction level result in at least one of INFO1110 OR INFO1113 OR DATA1002 OR INFO1103 OR INFO1903 N INFO1105 OR INFO1905 OR COMP2123 Note: Department permission required for enrolment |
Semester 1 |
| DATA2001 Data Science: Big Data and Data Diversity |
6 | P DATA1002 OR INFO1110 OR INFO1903 OR INFO1103 |
Semester 1 |
| DATA2002 Data Analytics: Learning from Data |
6 | A (Basic Linear Algebra and some coding) or QBUS1040 P [DATA1001 or ENVX1001 or ENVX1002] or [MATH10X5 and MATH1115] or [MATH10X5 and STAT2011] or [MATH1905 and MATH1XXX (except MATH1XX5)] or [BUSS1020 or ECMT1010 or STAT1021] N STAT2012 or STAT2912 |
Semester 2 |
3000-level units of study |
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Core units |
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| DATA3001 Data Science Capstone Project will be available from 2019. | |||
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 COMP2123 OR COMP2823 OR INFO1105 OR INFO1905 N COMP2007 OR COMP2907 OR COMP3027 Note: Department permission required for enrolment |
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 some 2nd year COMP or MATH or SOFT units. N COMP3308 COMP3308 and COMP3608 share the same lectures, but have different tutorials and assessment (the same type but more challenging). |
Semester 1 |
| DATA3404 Data Science Platforms |
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 ISYS2120 OR INFO2120 OR INFO2820 N INFO3504 OR INFO3404 |
Semester 1 |
| DATA 3406 Human-in-the-Loop Data Analytics will be available from 2019. | |||
For a standard enrolment plan for the Bachelor of Advanced Computing with a major in Computational Data Science visit CUSP https://cusp.sydney.edu.au.