Statistics
Unit of study table (Table A)
Statistics major |
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| A major in Statistics requires 48 credit points from this table including: |
| (i) 12 credit points of 1000-level units according to the following rules: |
| (a) For students in the Mathematical Sciences Program, 6 credit points of core units and 6 credit poitns of Mathematics units; |
| (b) For all other students, 6 credit points of core units and 6 credit points of Mathematics units or Data Science units |
| (ii) 12 credit points of 2000-level core units |
| (iii) 12 credit points of 3000-level core units |
| (iv) 6 credit points of 3000-level interdisciplinary project units |
| (v) 6 credit points of 3000-level selective units |
| *Students not enrolled in the BSc may substitute ECMT1010 or BUSS1020 |
Statistics minor |
| A minor in Statistics requires 36 credit points from this table including: |
| (i) 6 credit points of 1000 level core units and 6 credit points of 1000 level Mathematics units or Data Science units; |
| (ii) 12 credit points of 2000-level core units |
| (iii) 12 credit points of 3000-level selective units |
| Unit of study | Credit points | A: Assumed knowledge P: Prerequisites C: Corequisites N: Prohibition |
|---|---|---|
1000-level units of study |
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Core units |
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MATH1061 |
6 | A NSW HSC Extension 1 Mathematics or equivalent |
| MATH1961 Mathematics 1A (advanced) |
6 | A (HSC Mathematics Extension 2) or (Band E4 in HSC Mathematics Extensions 1) or equivalent N MATH1901 or MATH1902 or MATH1921 or MATH1906 or MATH1931 or MATH1001 or MATH1021 or MATH1061 or MATH1971 or MATH1002 or MATH1014 |
| MATH1971 Mathematics 1A (SSP) |
6 | A (at least Band E4 in HSC Mathematics Extension 2) or equivalent Departmental Permission is required to enrol in this unit |
Mathematics units |
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MATH1062 Mathematics 1B |
6 | A Knowledge of complex numbers and methods of differential and integral calculus including integrations by partial fractions and integration by parts as for example in MATH1021 or MATH1921 or MATH1931 or MATH1061 or HSC Mathematics Extension 2 |
| MATH1962 Mathematics 1B (advanced) |
6 | A (HSC Mathematics Extension 2) or equivalent N MATH1905 or MATH1903 or MATH1923 or MATH1907 or MATH1933 or MATH1062 or MATH1972 or MATH1003 or MATH1023 or MATH1005 or MATH1015 P None C None |
| MATH1972 Mathematics 1B (SSP) |
6 | A (HSC Mathematics Extension 2) or (Band E4 in HSC Mathematics Extension 1) or equivalent Departmental Permission is required to enrol in this unit |
Data science 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 |
| 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 |
2000-level units of study |
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Core |
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| 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 BUSS1020 or ECMT1010 or [MATH1X05 and (MATH1001 or MATH1002 or MATH1003 or MATH1004 or MATH1021 or MATH1023 or MATH1115 or MATH19XX)] N STAT2012 or STAT2912 or DATA2902 |
| 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 BUSS1020 or ECMT1010 or [MATH1X05 and (MATH1001 or MATH1002 or MATH1003 or MATH1004 or MATH1021 or MATH1023 or MATH1115 or MATH19XX)]) N STAT2012 or STAT2912 or DATA2002 |
| 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 |
| 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 |
3000-level units of study |
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Major core |
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| STAT3022 Applied Linear Models |
6 | A Introductory knowledge to linear algebra (MATH1X02 or equivalent) P STAT2X11 and (DATA2X02 or STAT2X12) N STAT3912 or STAT3012 or STAT3922 or STAT4022 |
| 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 |
| STAT3023 Statistical Inference |
6 | A DATA2X02 or STAT2X12 P STAT2X11 N STAT3913 or STAT3013 or STAT3923 |
| STAT3923 Statistical Inference (Advanced) |
6 | P STAT2X11 and a mark of 65 or greater in (DATA2X02 or STAT2X12) N STAT3913 or STAT3013 or STAT3023 |
Interdisciplinary projects |
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| SCPU3001 Science Interdisciplinary Project |
6 | P 96 credit points N HSBH3026 |
| STAT3888 Statistical Machine Learning |
6 | A STAT3012 or STAT3912 or STAT3022 or STAT3922 P STAT2X11 and (DATA2X02 or STAT2X12) N STAT3914 or STAT3014 |
Major selective |
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| 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 |
| 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 |
| STAT3925 Time Series (Advanced) |
6 | P STAT2X11 and (MATH1X03 or MATH1907 or MATH1X23 or MATH1933) N STAT4025 |
| STAT3926 Statistical Consulting (Advanced) |
6 | P At least 12cp from STAT2X11 or STAT2X12 or DATA2X02 or STAT3XXX N STAT4026 |
Minor selective |
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| 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 |
| 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 |
| STAT3022 Applied Linear Models |
6 | A Introductory knowledge to linear algebra (MATH1X02 or equivalent) P STAT2X11 and (DATA2X02 or STAT2X12) N STAT3912 or STAT3012 or STAT3922 or STAT4022 |
| 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 |
| STAT3023 Statistical Inference |
6 | A DATA2X02 or STAT2X12 P STAT2X11 N STAT3913 or STAT3013 or STAT3923 |
| STAT3923 Statistical Inference (Advanced) |
6 | P STAT2X11 and a mark of 65 or greater in (DATA2X02 or STAT2X12) N STAT3913 or STAT3013 or STAT3023 |
| STAT3888 Statistical Machine Learning |
6 | A STAT3012 or STAT3912 or STAT3022 or STAT3922 P STAT2X11 and (DATA2X02 or STAT2X12) N STAT3914 or STAT3014 |
| STAT3925 Time Series (Advanced) |
6 | P STAT2X11 and (MATH1X03 or MATH1907 or MATH1X23 or MATH1933) N STAT4025 |
| STAT3926 Statistical Consulting (Advanced) |
6 | P At least 12cp from STAT2X11 or STAT2X12 or DATA2X02 or STAT3XXX N STAT4026 |
