Syllabus (COMPUTER SCIENCE)

Course Type: ME-3

Semester: 3

Course Code: BCOSMEB23C

Course Title: Programming Using Python

(L-P-Tu): 2-2-0

Credit: 4

Practical/Theory: Combined

Course Objective: • Understand Object Oriented Programming Concept. • Familiar with Python IDLE. • Understand Loops, Decision Statements in Python. • Understand the concept of File Handling and Exception Handling.

Learning Outcome: • Ability to write database applications in Python. • Understand the basic concept of Class, Object, Function, List, Tuple, Set, Dictionary etc. • Design Graphical-user Interfaces in Python. • Understand Python applications for File Handling.

Theory

Introduction to Object Oriented Programming

Object-Oriented Programming, Characteristics, Object-Oriented Programming Paradigm, Benefits and Application, Properties. (5 Lectures)

Basics of Python

Python Installation, Python Variables and Data types, Identifiers and Keywords, Literals, Strings, Python Basic Operators Understandings (Arithmetic operator, Relational operator, Logical and Boolean operator, Assignment Operator, Bit wise operator). (5 Lectures)

Building Blocks and Control Statements

Standard Libraries in Python, Decision Control Flow Statement (if, if…else, if…elif…else), Nested if, While Loop, For Loop, Continue and Break Statements. (5 Lectures)

Arrays and Functions

Creating Arrays, Indexing and Slicing of Arrays, Array Operations, Syntax and Basics of Functions, Use of Functions, Function Arguments, Built-In Functions, Function Definition and Calling Functions, The return Statement. (5 Lectures)

Strings, Sets, Lists, Tuples and Dictionaries Creating Lists, List slicing, List Methods, Passing list to a function, Creating tuples, Tuple Operations, Creating Sets, Set Operations, Basic String Operations, Basics of Dictionaries, Creating a Dictionary, Formatting Dictionaries. (5 Lectures)

Files and Exception Handlings

Basic File Operations, Errors and Exceptions. (5 Lectures)

Programming Lab using Pythion

Practical part based upon the topics covered in the theory part.

Reference Books:

1. David Ascher and Mark Lutz, Learning Python, O’Reilly Media.

2. ReemaThareja, “Python Programming using Problem Solving approach”, Oxford University press.

3. Wes Mckinney, Python for Data Analysis, First edition, O’Reilly Media.

4. W. McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy and IPython, O’Reilly Media.

5. C. O’Neil & R. Schutt, Doing Data Science: Straight Talk from the Frontline, O’Reilly Media.