STEM2002 Applied Problem Solving with Computers

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.

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.

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