Software Engineering stream
Engineering Data Science specialisation
Engineering Data Science specialisation |
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| Students in the Software stream must complete 30 credit points to achieve this specialisation. |
| Unit of study | Credit points | A: Assumed knowledge P: Prerequisites C: Corequisites N: Prohibition |
|---|---|---|
| Students must complete 18 credit points from the following: | ||
| 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 |
| DATA2901 Big Data and Data Diversity (Advanced) |
6 | P 75% or above from (DATA1002 or DATA1902 or INFO1110 or INFO1910 or INFO1903 or INFO1103 or ENGG1810) N DATA2001 |
| 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 MATH1062 or MATH1962 or MATH1972 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 greater in (DATA1X01 or ENVX1002 or BUSS1020 or ECMT1010 or MATH1062 or MATH1962 or MATH1972 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 (MATH1X61 or MATH1971 or MATH1X21 or MATH1931 or MATH1X01 or MATH1906 or MATH1011) and (DATA1X01 or MATH1X62 or MATH1972 or MATH10X5 or MATH1905 or STAT1021 or ECMT1010 or BUSS1020) N STAT2911 |
| STAT2911 Probability and Statistical Models (Adv) |
6 | P (MATH1X61 or MATH1971 or 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 or MATH1X62 or MATH1972) N STAT2011 |
| Students must complete 12 credit points from the following: | ||
| COMP3308 Introduction to Artificial Intelligence |
6 | A Data structures and algorithms as covered in COMP2123 or COMP2823. N COMP3608 P INFO1110 or INFO1910 or ENGG1801 or ENGG1810 or DATA1002 or DATA1902 |
| COMP3608 Introduction to Artificial Intelligence (Adv) |
6 | A Data structures and algorithms as covered in COMP2123 or COMP2823. P (INFO1110 or INFO1910 or ENGG1810 or DATA1002 or DATA1902) and 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). |
| 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 |
| DATA3406 Human-in-the-Loop Data Analytics |
6 | A Basic statistics, database management, and programming P (DATA2001 or DATA2901) and (DATA2002 or DATA2902) |
| Units taken for the specialisation will also count toward requirements of the Software stream. | ||