Syllabus (COMPUTER APPLICATION)

Course Type: SEC-1

Semester: 1

Course Code: BBCASEC01C

Course Title: Object Oriented Programming using Python

(L-P-Tu): 2-1-0

Credit: 3

Practical/Theory: Combined

Course Objective: Course Objectives: • To understand why Python is useful for data analysis. • To learn Python programme and its application. • To learn how to build packages in Python. • To learn how to use list, tuples, dictionaries in Python Programmes.

Learning Outcome: Course Outcomes: After the completion of this course, students will be able to: • Implement object-oriented concept in Python programming. • Write Python codes for data analysis. • Create an application in Python.

Syllabus:

Unit I: Theory Credit: 2 (L 30)

Introduction to Object Oriented Programming: Object-Oriented Programming Basics, Characteristics of Object-Oriented Programming, Object-Oriented Programming Paradigm, Benefits, Applications and properties of Object-Oriented Programing. [L 6]

Basics of Python: Python Installation, Python Variables and Datatypes, Identifiers and Keywords, Literals, Strings, Python Basic Operators Understandings (Arithmetic operator, Relational operator, Logical and Boolean operator, Assignment Operator, Bit wise operator). [L 6]

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. [L 6]

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. [L 6]

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. [L 6]

Unit II: Practical Credit:1 (L 30)

Practical part will be based upon the modules, covered in the theory part.

Reading References:

1. David Ascher, Mark Lutz, Learning Python, Publisher O’Reilly Media.
2. Reema Thareja, Python Programming using Problem Solving approach, Oxford University press.
3. Wes Mckinney, Python for Data Analysis, Publisher O’Reilly Media.
4. Wes McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy and IPython, O’Reilly Media.
5. C. O’Neil and R. Schutt, Doing Data Science: Straight Talk from the Frontline, O’Reilly Media.
6. Allen Downey, Jeffrey Elkner, Chris Meyers, Learning with Python, Dreamtech Press.
7. David Taieb, Data Analysis with Python: A Modern Approach, Packt Publishing.