MAT 210 Programming

Syllabus for MAT 210 – Programming

This is a compulsory course for BS Mathematics students in their 3^rd semester.

From XKCD. Thanks to Aman Agarwal for the reference!

Credits (Lec:Tut:Lab)= 1:0:1 (One lecture hour and three lab hours weekly)

Prerequisites: None

Overview: This course provides an introduction to formal programming languages via the medium of Python 3.0. The programming activities will be centered around mathematical models involving differential equations, algebraic systems, iterative processes, linear transformations, random processes etc. The course begins with Python language constructs and moves to an in-depth exploration of the SCIPY and NUMPY packages that hold the key to the desired mathematical simulations.

Detailed Syllabus:

1. Basics of the PYTHON programming language:

• Input and output statements, formatting output, copy and assignment, arithmetic operations, string operations, lists and tuples, control statements
• User defined functions, call by reference, variable number of arguments
• One dimensional arrays, two dimensional arrays, random number generation
• Classes, static data, private data, inheritance, scope of variables, nested functions

2. The NUMPY and SCIPY packages:

• Numpy numerical types, data type objects, character codes, dtype constructors.
• Mathematical libraries, plotting 2D and 3D functions, ODE integrators, charts and histograms, image processing functions.
• File I/O, loading data from CSV files
• Using SCIPY/NUMPY to solve models involving difference equations, differential equations, finding limit at a point, approximation using Taylor series, interpolation, definite integrals.

Main References:

1. John Zelle, Python Programming: An Introduction to Computer Science. Franklin, Beedle & Associates Inc., Second Edition, 2010.
2. Ivan Idris, Numpy 1.5 Beginner’s Guide. Packt Publishing, 2011.
3. Hans Petter Langtangen, A Primer on Scientific Programming on Python. Springer, Second Edition, 2011.

Other References:

1. Hans Petter Langtangen, Python Scripting for Computational Science. Springer, 2010.
2. David M. Beazley, Python Essential Reference, 3^rd Edition. Pearson, 2009.