Data Science
Please check the current students website (Find a unit of study) for up-to-date information on units of study including availability.
| Unit of study | Credit points | A: Assumed knowledge P: Prerequisites C: Corequisites N: Prohibition | Session |
|---|---|---|---|
DATA SCIENCE |
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Data Science major |
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| A major in Data Science requires 48 credit points from this table including: | |||
| (i) 6 credit points of 1000-level core units | |||
| (ii) 6 credit points of 1000-level units according to the following rules*: | |||
| (a) 6 credit points of selective units OR | |||
| (b) 3 credit points of statistics units and 3 credit points of computation units OR | |||
| (c) 3 credit points of advanced statistics units and 3 credit points of mathematics units OR | |||
| (d) 3 credit points of advanced statistics units and 3 credit points of linear algebra units for students in the Mathematical Sciences program^ | |||
| (iii) 12 credit points of 2000-level core units | |||
| (iv) 6 credit points of 2000-level selective units | |||
| (v) 6 credit points of 3000-level core interdisciplinary project units | |||
| (vi) 6 credit points of 3000-level methodology units | |||
| (vii) 6 credit points of 3000-level methodology or application or interdisciplinary project selective units | |||
| *Students not enrolled in the BSc may substitute ECMT1010 or BUSS1020 | |||
| ^If elective space allows, students may substitute DATA1001/1901 for the advanced statistics unit | |||
Data Science minor |
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| A minor in Data Science requires 36 credit points from this table including: | |||
| (i) 6 credit points of 1000-level core units | |||
| (ii) 6 credit points of 1000-level units according to the following rules*: | |||
| (a) 6 credit points of selective units OR | |||
| (b) 3 credit points of statistics units and 3 credit points of computations units OR | |||
| (c) 3 credit points of advanced statistics units and 3 credit points of calculus and linear algebra units | |||
| (iii) 12 credit points of 2000-level core units | |||
| (iv) 6 credit points of 2000-level selective units | |||
| (v) 6 credit points of 3000-level methodology units | |||
Units of study |
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| The units of study are listed below. | |||
1000-level units of study |
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Core |
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| DATA1002 Informatics: Data and Computation |
6 | N INFO1903 OR DATA1902 |
Semester 2 |
| DATA1902 Informatics: Data and Computation (Advanced) |
6 | A This unit is intended for students with ATAR at least sufficient for entry to the BSc/BAdvStudies(Advanced) stream, or for those who gained Distinction results or better, in some unit in Data Science, Mathematics, or Computer Science. Students with portfolio of high-quality relevant prior work can also be admitted N INFO1903 OR DATA1002 Note: Department permission required for enrolment |
Semester 2 |
Selective |
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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 |
| ENVX1002 Introduction to Statistical Methods |
6 | N ENVX1001 or MATH1005 or MATH1905 or MATH1015 or MATH1115 or DATA1001 or DATA1901 or BUSS1020 or STAT1021 or ECMT1010 Available as a degree core unit only in the Agriculture, Animal and Veterinary Bioscience, and Food and Agribusiness, and Taronga Wildlife Conservation streams |
Semester 1 |
Statistics |
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| MATH1005 Statistical Thinking with Data |
3 | A HSC Mathematics Advanced or equivalent N MATH1015 or MATH1905 or STAT1021 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 or DATA1001 or DATA1901 |
Intensive January Semester 1 Semester 2 |
Computation |
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| MATH1115 Interrogating Data |
3 | P MATH1005 or MATH1015 N STAT1021 or ENVX1001 or ENVX1002 or BUSS1020 or ECMT1010 or DATA1001 or DATA1901 |
Intensive January Semester 2 |
Advanced Statistics |
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| MATH1905 Statistical Thinking with Data (Advanced) |
3 | A HSC Mathematics Extension 2 or 90 or above in HSC Mathematics Extension 1 or equivalent N MATH1005 or MATH1015 or STAT1021 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 or DATA1001 or DATA1901 |
Semester 2 |
Mathematics |
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| MATH1021 Calculus Of One Variable |
3 | A HSC Mathematics Extension 1 or equivalent N MATH1901 or MATH1906 or ENVX1001 or MATH1001 or MATH1921 or MATH1931 |
Intensive January Semester 1 Semester 2 |
| MATH1921 Calculus Of One Variable (Advanced) |
3 | A (HSC Mathematics Extension 2) OR (Band E4 in HSC Mathematics Extension 1) or equivalent N MATH1001 or MATH1906 or ENVX1001 or MATH1901 or MATH1021 or MATH1931 Note: Department permission required for enrolment |
Semester 1 |
| MATH1931 Calculus Of One Variable (SSP) |
3 | A (HSC Mathematics Extension 2) OR (Band E4 in HSC Mathematics Extension 1) or equivalent N MATH1001 or MATH1901 or ENVX1001 or MATH1906 or MATH1021 or MATH1921 Note: Department permission required for enrolment Enrolment is by invitation only |
Semester 1 Semester 1 |
| MATH1023 Multivariable Calculus and Modelling |
3 | A Knowledge of complex numbers and methods of differential and integral calculus including integration by partial fractions and integration by parts as for example in MATH1021 or MATH1921 or MATH1931 or HSC Mathematics Extension 2 N MATH1013 or MATH1903 or MATH1907 or MATH1003 or MATH1923 or MATH1933 |
Intensive January Semester 1 Semester 2 |
| MATH1923 Multivariable Calculus and Modelling (Adv) |
3 | A (HSC Mathematics Extension 2) OR (Band E4 in HSC Mathematics Extension 1) or equivalent N MATH1003 or MATH1013 or MATH1907 or MATH1903 or MATH1023 or MATH1933 Note: Department permission required for enrolment |
Semester 2 |
| MATH1933 Multivariable Calculus and Modelling (SSP) |
3 | A (HSC Mathematics Extension 2) OR (Band E4 in HSC Mathematics Extension 1) or equivalent N MATH1003 or MATH1903 or MATH1013 or MATH1907 or MATH1023 or MATH1923 Note: Department permission required for enrolment Enrolment is by invitation only. |
Semester 2 |
| MATH1002 Linear Algebra |
3 | A HSC Mathematics or MATH1111. Students who have not completed HSC Mathematics (or equivalent) are strongly advised to take the Mathematics Bridging Course (offered in February) N MATH1012 or MATH1014 or MATH1902 |
Intensive January Semester 1 |
| MATH1902 Linear Algebra (Advanced) |
3 | A (HSC Mathematics Extension 2) OR (90 or above in HSC Mathematics Extension 1) or equivalent N MATH1002 or MATH1014 Note: Department permission required for enrolment |
Semester 1 |
2000-level units of study |
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Core |
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| 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 |
Selective |
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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 |
| COSC2002 Computational Modelling |
6 | A HSC Mathematics; DATA1002, or equivalent programming experience, ideally in Python N COSC1003 or COSC1903 or COSC2902 |
Semester 1 |
| COSC2902 Computational Modelling (Advanced) |
6 | A HSC Mathematics; DATA1002, or equivalent programming experience, ideally in Python P 48 credit points of 1000 level units with an average of 65 N COSC1003 or COSC1903 or COSC2002 Note: Department permission required for enrolment |
Semester 1 |
| GEGE2001 Genetics and Genomics |
6 | A Mendelian genetics; mechanisms of evolution; molecular and chromosomal bases of inheritance; and gene regulation and expression N GENE2002 or MBLG2972 or GEGE2901 or MBLG2072 |
Semester 2 |
| GEGE2901 Genetics and Genomics (Advanced) |
6 | A Mendelian genetics, mechanisms of evolution, molecular and chromosomal bases of inheritance, and gene regulation and expression P Annual average mark of at least 70 N GENE2002 or MBLG2072 or GEGE2001 or MBLG2972 |
Semester 2 |
| QBIO2001 Molecular Systems Biology |
6 | A Basic concepts in metabolism; protein synthesis; gene regulation; quantitative and statistical skills |
Semester 1 |
| STAT2011 Probability and Estimation Theory |
6 | P (MATH1X21 or MATH1931 or MATH1X01 or MATH1906 or MATH1011) and (DATA1X01 or MATH10X5 or MATH1905 or STAT1021 or ECMT1010 or BUSS1020) N STAT2911 |
Semester 1 |
| STAT2911 Probability and Statistical Models (Adv) |
6 | P (MATH1X21 or MATH1931 or MATH1X01 or MATH1906 or MATH1011) and a mark of 65 or greater in (DATA1X01 or MATH10X5 or MATH1905 or STAT1021 or ECMT1010 or BUSS1020) N STAT2011 |
Semester 1 |
3000-level units of study |
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Core interdisciplinary project |
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| DATA3888 Data Science Capstone |
6 | P DATA2001 or DATA2901 or DATA2002 or DATA2902 or STAT2912 or STAT2012 |
Semester 1 |
Methodology |
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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 |
| STAT3021 Stochastic Processes |
6 | A Students are expected to have a thorough knowledge of basic probability and integral calculus P STAT2X11 N STAT3911 or STAT3011 or STAT3921 or STAT4021 |
Semester 1 |
| STAT3921 Stochastic Processes (Advanced) |
6 | A Students are expected to have a thorough knowledge of basic probability and integral calculus and to have achieved at credit level or above P STAT2X11 N STAT3011 or STAT3911 or STAT3021 or STAT3003 or STAT3903 or STAT3005 or STAT3905 or STAT4021 |
Semester 1 |
| STAT3022 Applied Linear Models |
6 | P STAT2X11 and (DATA2X02 or STAT2X12) N STAT3912 or STAT3012 or STAT3922 or STAT4022 |
Semester 1 |
| STAT3922 Applied Linear Models (Advanced) |
6 | P STAT2X11 and [a mark of 65 or greater in (STAT2X12 or DATA2X02)] N STAT3912 or STAT3012 or STAT3022 or STAT4022 |
Semester 1 |
| STAT3023 Statistical Inference |
6 | A DATA2X02 or STAT2X12 P STAT2X11 N STAT3913 or STAT3013 or STAT3923 |
Semester 2 |
| STAT3923 Statistical Inference (Advanced) |
6 | P STAT2X11 and a mark of 65 or greater in (DATA2X02 or STAT2X12) N STAT3913 or STAT3013 or STAT3023 |
Semester 2 |
| STAT3925 Time Series (Advanced) |
6 | P STAT2X11 and (MATH1X03 or MATH1907 or MATH1X23 or MATH1933) N STAT4025 |
Semester 1 |
| STAT3926 Statistical Consulting (Advanced) |
6 | P At least 12cp from STAT2X11 or STAT2X12 or DATA2X02 or STAT3XXX N STAT4026 |
Semester 1 |
Application |
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| ENVX3001 Environmental GIS |
6 | P 6cp from (ENVI1003 or AGEN1002) or 6cp from GEOS1XXX or 6cp from BIOL1XXX or GEOS2X11 N GEOS3X14 |
Semester 2 |
| ENVX3002 Statistics in the Natural Sciences |
6 | P ENVX2001 or STAT2X12 or BIOL2X22 or DATA2X02 or QBIO2001 |
Semester 1 |
| AMED3002 Interrogating Biomedical and Health Data |
6 | A Exploratory data analysis, sampling, simple linear regression, t-tests, confidence intervals and chi-squared goodness of fit tests, familiar with basic coding, basic linear algebra |
Semester 1 |
| GEGE3004 Applied Genomics |
6 | A Genetics at 2000 level, Biology at 1000 level, algebra P 6cp of (GEGE2X01 or QBIO2XXX or DATA2X01 or GENE2XXX or MBLG2X72 or ENVX2001 or DATA2X02) N ANSC3107 This unit must be taken by all students in the Genetics and Genomics major. |
Semester 2 |
| BCMB3004 Beyond The Genome |
6 | A Biochemistry, genetics, cell and/or molecular biology concepts at 2000-level units P 12 credit points from (AMED3001 or BCHM2X71 or BCHM2X72 or BCHM3XXX or BCMB2X01 or BCMB2X02 or BCMB3XXX or BIOL2X29 or BMED2401 or BMED2405 or GEGE2X01 or MBLG2X01 or MEDS2002 or MEDS2003 or PCOL2X21 or QBIO2001) N BCHM3X92 or BCMB3904 |
Semester 2 |
| BCMB3904 Beyond The Genome (Advanced) |
6 | A Biochemistry, genetics, cell and/or molecular biology concepts at 2000-level units P An average mark of 75 or above in 12 credit points from (AMED3001 or BCHM2X71 or BCHM2X72 or BCHM3XXX or BCMB2X01 or BCMB2X02 or BCMB3XXX or BIOL2X29 or BMED2401 or BMED2405 or GEGE2X01 or MBLG2X01 or MEDS2002 or MEDS2003 or PCOL2X21 or QBIO2001) N BCHM3X92 or BCMB3004 |
Semester 2 |
Selective Interdisciplinary Project |
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| SCPU3001 Science Interdisciplinary Project |
6 | P 96 credit points |
Intensive February Intensive July Semester 1 Semester 2 |
| STAT3888 Statistical Machine Learning |
6 | A STAT3012 or STAT3912 or STAT3022 or STAT3922 P STAT2X11 and (DATA2X02 or STAT2X12) N STAT3914 or STAT3014 |
Semester 2 |