RS4009 Python and R Programming Syllabus:
RS4009 Python and R Programming Syllabus – Anna University PG Syllabus Regulation 2021
OBJECTIVES:
To expose students to various concepts and capabilities of python scripting language
To familiarize students to write simple programs in python for spatial data storage and analysis
To expose students to concepts and capabilities of R programming
UNIT I INTRODUCTION TO PYTHON
Scripting, Introduction to Python, Numbers and operators, Variables and Data types, Expressions, Decisions and Loops, Modules, File Access, loading Vector & Raster layers
UNIT II PROGRAMMING USING PYTHON
List, Dictionaries, Simple Functions, Simple Graphics, Image Processing, Design of Simple GUI, Instance Variables, functions for vector to raster conversion, georeferencing raster layer, creating a hillshade map
UNIT III OBJECT ORIENTATION IN PYTHON
Objects and Classes, Data-Modeling, Building a New Data structure, Inheritance and Polymorphism, Data Encryption, Threads and Processes, Search Algorithms, Basic Sort Algorithms
UNIT IV R PROGRAMMING BASICS
Introduction, Data types, Variables, Vectors, Scalars, Conclusion, Data Frames, Lists, Matrices, Arrays, Classes, Arithmetic and Boolean Operators and values, Structures, Control Statements, Loops, Recursion, Scoping Rules, Loop functions, Array and Matrices, Spatial programming
UNIT V DATA MANIPULATION AND DATA VISUALISATION
Functions, Math Functions, Linear Algebra Operation, Probability Distributions: Normal, Binomial, Poisson, Graphics, Creating Graphs, Customizing Graphs, Box plot, Histogram, Pie graph, Line chart, Scatterplot, Spatial Attribute Analysis
OUTCOMES:
On completion of the course, the student is expected to be able to
CO1 Summarise the data types, variable, expressions and control statements used in python
CO2 Write simple programs in python for visualization and analysis of vector & image data
CO3 Analyse the object orientation capabilities of python and its applications in spatial analysis
CO4 Describe the data, variables, operators and functions available in R
CO5 Apply the R programming for analysis of spatial and non-spatial data and for visualisation
REFERENCES
1. Larry Pace, Joshua Wiley, Beginning R -An Introduction to Statistical Programming, 2nd Edition, Apress, ISBN: 9781484203743, 2015
2. David I. Schneider, Introduction to Programming Using Python, 1st Edition, Pearson, ISBN: 9780134058221, 2015
3. Y. Daniel Liang, Introduction to Programming Using Python, 1st edition, Pearson, ISBN: 9780132747189, 2017
4. Lawhead Joel, QGIS Python Programming Cookbook,2nd Revised Edition, Packt Publishing, ISBN: 9781783984985, 2017.
5. Chaowei Yang, Introduction to GIS Programming and Fundamentals with Python and ArcGIS, 1st Edition,2017, CRC Press, ISBN: 9781466510081
6. Chris Brunsdon, Lex Comber, An Introduction to R for Spatial Analysis and Mapping, 1st Edition, Sage Publications Ltd (UK), ISBN: 9781446272954,2nd Edition, 2019.
7. Hamid Reza Pourghasemi, Spatial Modeling in GIS and R for Earth and Environmental Sciences, Elsevier (S&T), ISBN: 9780128152263, 2019