### Minor Overview

- About this minor
- The ability to understand and critically evaluate the assumptions which underlie data and its presentation are invaluable. A solid grasp of data science offers a key pathway to success in a broad range of careers across all sectors of endeavour. The Minor in Data Science provides a grounding in statistics, data analysis and computational thinking and is suitable for students in both non-STEM and STEM related fields.
**For students taking a minor which shares units with their other unit sets (majors or minors):**in order for minors to be recognised on academic and graduation documents, students may only have a maximum of**one unit**overlapping between their unit sets. - Outcomes
- Students are able to (1) critically assess the application and outcome of Data Science workflow to a problem; (2) select the appropriate statistical techniques for use in particular real-world settings

; (3) apply statistical techniques to data analysis using computational methods; (4) devise and implement computational models and simple numerical methods in a modern programming language; and (5) demonstrate the power of statistical concepts in the interpretation of data. - Incompatibility
MJD-DATSC Data Science;

MJD-CDSDM Computing and Data Science;

MNR-ASTAT Applied Statistical Learning

### Units

##### Key to availability of units:

- S1
- Semester 1
- S2
- Semester 2

##### Take all units (12 points):

Availability | Unit code | Unit name | Unit requirements |
---|---|---|---|

S1, S2 | CITS1401 | Computational Thinking with Python | |

S1, S2 | STAT1400 | Statistics for Science |

##### Take the following unit:

Availability | Unit code | Unit name | Unit requirements |
---|---|---|---|

S2 | CITS2402 | Introduction to Data Science |

##### Take unit(s) to the value of 6 points:

Availability | Unit code | Unit name | Unit requirements |
---|---|---|---|

S1 | STAT2401 | Analysis of Experiments | |

S2 | STAT2402 | Analysis of Observations |