Year
2021
Units
4.5
Contact
2 x 2-hour lectures per semester
8 x 2-hour laboratories per semester
1 x 8-hour project work per semester
Enrolment not permitted
STEM8005 has been successfully completed
Assessment
Assignment(s), Project, Test(s)
Topic description

This topic will allow students to design a series of computer batch scripts in R and Python that significantly increase productivity and reduce the amount of time which would normally be involved in undertaking repetitive data analysis tasks and/or more complex statistical analysis and computer modelling. This topic is tailored for all students who will need to undertake statistical analysis or computer modelling while studying or after graduation. These skills will provide students with a particular 'edge' in the workplace environment or later research degrees. Midway through the topic students already having had past experience with Geographical Information Systems (GIS), may opt to study particular skills in increasing their productivity and harnessing the power of GIS utilising R and/or Python scripts.

Educational aims

This topic aims to allow students to design a series of computer batch scripts in R and Python that significantly increase productivity and reduce the amount of time which would normally be involved in undertaking repetitive data analysis tasks and/or more complex statistical analysis and computer modelling. Midway through the topic students already having had past experience with Geographical Information Systems (GIS), will design a series of computer batch scripts in R and/or Python, together with a GIS Model Builder that implement several spatial modelling functions in the GIS. The topic finally aims to provide students with an opportunity to apply these skills to a real-world problem in an application area of their choice.

Expected learning outcomes
On completion of this topic you will be expected to be able to:

  1. Describe/identify the advantages in terms or accuracy and productivity of scripting languages such as Python and R for solving complex problems and reducing repetitive tasks
  2. Describe/identify the difference between programing languages and scripting languages
  3. Describe/identify scripting routines in Python and R that successfully input and output data; use while and for loops, execute both third party library and core statistical and mathematical functions
  4. Statistically analyse spatial or aspatial data and output tabular or graphical results from this using R
  5. Replace a normally repetitive spatial or aspatial analysis task using Python
  6. Demonstrate scripting routines in Python and R that successfully input and output data; use while and for loops, execute both third party library and core statistical and mathematical functions
  7. Build a batch of scripts in Python and/or R to analyse spatial or aspatial data for a real or simulated applied problem and report the methods and results

Key dates and timetable

(1), (2)

Each class is numbered in brackets.
Where more than one class is offered, students normally attend only one.

Classes are held weekly unless otherwise indicated.

FULL

If you are enrolled for this topic, but all classes for one of the activities (eg tutorials) are full,
contact your College Office for assistance. Full classes frequently occur near the start of semester.

Students may still enrol in topics with full classes as more places will be made available as needed.

If this padlock appears next to an activity name (eg Lecture), then class registration is closed for this activity.

Class registration normally closes at the end of week 2 of each semester.

Classes in a stream are grouped so that the same students attend all classes in that stream.
Registration in the stream will result in registration in all classes.
  Unless otherwise advised, classes are not held during semester breaks or on public holidays.