Statistics Table
Please check the current students website (Find a unit of study) for up-to-date information on units of study including availability.
Unit outlines will be available through Find a unit outline two weeks before the first day of teaching for 1000-level and 5000-level units, or one week before the first day of teaching for all other units.
| Unit of study | Credit points | A: Assumed knowledge P: Prerequisites C: Corequisites N: Prohibition | Session |
|---|---|---|---|
STATISTICS |
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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) 6 credit points of calculus and 3 credit points of linear algebra and 3 credit points of statistics*. (Students in the Mathematical Sciences program must choose this option^); | |||
| (b) 3 credit points of calculus and 3 credit points of linear algebra and 6 credit points of data science* | |||
| (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 | |||
| ^If elective space allows, students may substitute DATA1001/1901 for the statistics unit | |||
Statistics minor |
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| A minor in Statistics requires 36 credit points from this table including: | |||
| (i) 12 credit points of 1000-level units according to the following rules: | |||
| (a) 6 credit points of calculus and 3 credit points of linear algebra and 3 credit points of statistics; or | |||
| (b) 3 credit points of calculus and 3 credit points of linear algebra and 6 credit points of data science | |||
| (ii) 12 credit points of 2000-level core units | |||
| (iii) 12 credit points of 3000-level selective units | |||
Units of study |
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| The units of study are listed below. | |||
1000-level units of study |
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Calculus |
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| MATH1011 Applications of Calculus |
3 | A HSC Mathematics. Students who have not completed HSC Mathematics (or equivalent) are strongly advised to take the Mathematics Bridging Course (offered in February). Please note: this unit does not normally lead to a major in Mathematics or Statistics or Financial Mathematics and Statistics N MATH1001 or MATH1901 or MATH1906 or BIOM1003 or ENVX1001 or MATH1021 or MATH1921 or MATH1931 |
Intensive January Semester 1 |
| 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 |
Linear algebra |
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| 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 |
| MATH1014 Introduction to Linear Algebra |
3 | A Coordinate geometry, basic integral and differential calculus, polynomial equations and algebraic manipulations, equivalent to HSC Mathematics N MATH1002 or MATH1902 |
Intensive January Semester 2 |
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 |
| 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 |
Data science |
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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 |
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 [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 |
| 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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Major core |
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| 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 |
Interdisciplinary projects |
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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 |
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 |
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 |
| 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 |
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 |
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 |
| 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 |
| 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 |