CS-5006/CSC-506

Problem Solving using Python & R

Course Outcomes

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.

Slides / Resources

Introduction 📓❓
Expressions 📓 ❓
Conditionals 📓 ❓
Functions 📓 🎧 ❓
Iterations 📓❓
Strings 📓❓
Files 📓🎦❓

Objects 📓❓
R Programming Notes 📓

Google Colab (Teaching) - Python

Google Colab (Teaching) - R

Python Exercise Programs

Syllabus

UNIT:1

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;

UNIT:2

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.

UNIT:3

Python Modules and Packages: Python Standard Library, Numpy, Pandas, Matplotlib, GUI -Tkinter, wxWidgets; Database- MySQL DB, Scikit-Learn, NL

UNIT:4

R Programming - Control Structures - Functions - Data Manipulation - String Operations- Data Visualization – R for Statistical computing.

TextBook for Python

pythonlearn.pdf