Data Science 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.
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.
Unit sequence
| 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 |
- Prerequisites:
- Mathematics Applications ATAR
or
MATH1720 Mathematics Fundamentals
or
equivalent
or
higher
|
| S1, S2 |
STAT1400 |
Statistics for Science |
- Prerequisites:
- Mathematics Applications ATAR
or
MATH1720 Mathematics Fundamentals
or
equivalent
or
higher - Incompatibility:
- STAT1520 Economic and Business Statistics
|
Take the following unit:
| Availability |
Unit code |
Unit name |
Unit requirements |
| S2 |
CITS2402 |
Introduction to Data Science |
- Prerequisites:
- CITS1401 Computational Thinking with Python
or
CITS2401 Computer Analysis and Visualisation
|
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 |
|