Upon successful completion of this course, students should be able to:
CO1. Write programs using Python data structures.
CO2. Develop solutions to real-world problems using object-oriented concepts
CO3. Read and write data from/to files using Python.
CO4. Make use of Python Modules and Packages to solve complex problems
CO5. Write simple R programs for statistical computing.
Problems solving fundamentals, Python: variables, expressions, statements, precedence of operators; Data structures: list, Dictionary, tuples; Lists: list slices, list methods, mutability, cloning lists, List comprehension; Tuples: tuple assignment, tuple as return value; Dictionaries: operations and methods; Conditional constructs; Iterative constructs. Strings: string slices, immutability, string functions and methods;
Functions: parameters, return values, local and global scope, function composition, recursion, and lambda functions.
Object orientation – Classes, Objects, methods, Operator overloading, and Inheritance. Files and exceptions: text files, reading and writing files, format operator; errors and exceptions, handling exceptions; creating modules and packages.
Python Modules and Packages: Python Standard Library, Numpy, Pandas, Matplotlib, GUI -Tkinter, wxWidgets; Database- MySQL DB, Scikit-Learn, NL
R Programming - Control Structures - Functions - Data Manipulation - String Operations- Data Visualization – R for Statistical computing.