Python Programming Course

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Python Programming Course Outline

Module 1: Introduction to Python

Objective: Understand the fundamentals of Python, its setup, and how to start writing and running Python code.

  1. What is Python?

    • Overview of Python: History, Features, and Benefits
    • Python’s popularity and use cases: Web Development, Data Science, Automation, etc.
    • Python as an interpreted, high-level, and general-purpose language

  2. Setting Up Python Development Environment

    • Installing Python (Python 3.x version)
    • Setting up IDEs: PyCharm, VS Code, Jupyter Notebook, and using the built-in IDLE
    • Running Python scripts in command-line and IDEs
    • Introduction to Python’s REPL (Read-Eval-Print Loop)

  3. Basic Syntax and Structure

    • Python’s structure: Indentation, Comments, and Docstrings
    • Writing your first Python program: print("Hello, World!")
    • Understanding basic syntax and whitespace significance in Python

  4. Basic Input/Output

    • Reading user input using input() function
    • Output formatting using print() and string interpolation (f-strings)
    • Handling data types in input/output operations

Module 2: Variables, Data Types, and Operators

Objective: Learn how to work with variables, data types, and perform basic operations in Python.

  1. Python Variables and Constants

    • Declaring variables and assigning values
    • Variable naming conventions and rules
    • Constants in Python (using uppercase and convention)

  2. Basic Data Types in Python

    • Primitive data types: int, float, bool, str
    • Type conversion and type casting: int(), float(), str(), bool()
    • Dynamic typing in Python (no need to declare data type explicitly)

  3. Operators in Python

    • Arithmetic operators: +, -, *, /, //, %, **
    • Comparison operators: ==, !=, <, >, <=, >=
    • Logical operators: and, or, not
    • Assignment operators: =, +=, -=, *=, /=
    • Identity and membership operators: is, is not, in, not in

  4. Expressions and Statements

    • Understanding expressions and statements in Python
    • Order of operations and operator precedence
    • Using parentheses for grouping expressions

Module 3: Control Flow Statements

Objective: Learn how to control the flow of your Python programs with conditions and loops.

  1. Conditional Statements

    • The if, else, and elif structure
    • Nested conditions and logical expressions
    • Using if statements with multiple conditions

  2. Loops in Python

    • The for loop: Iterating over ranges, lists, tuples, and dictionaries
    • The while loop: Creating loops with conditions
    • break, continue, and pass statements
    • Infinite loops and how to avoid them

  3. List Comprehensions

    • Introduction to list comprehensions for concise code
    • Syntax of list comprehension
    • Using list comprehensions with conditional statements

Module 4: Functions and Modules

Objective: Understand how to organize and reuse code using functions and modules.

  1. Defining Functions

    • Syntax for defining functions: def function_name():
    • Function arguments: positional, default, and keyword arguments
    • Return statements: returning values from functions
    • Function scope: local and global variables

  2. Lambda Functions

    • Introduction to anonymous (lambda) functions in Python
    • Syntax and use cases for lambda functions
    • Using map(), filter(), and reduce() with lambda

  3. Modules and Packages

    • What are Python modules and how to create them
    • Importing modules: import module_name and from module import
    • Using built-in Python modules: math, random, datetime, os, and more
    • Creating and organizing packages

  4. Recursion

    • Introduction to recursion: when a function calls itself
    • Base cases and recursive cases
    • Common recursive problems: factorial, Fibonacci series, etc.

Module 5: Data Structures in Python

Objective: Learn about Python’s built-in data structures and how to manipulate them.

  1. Lists in Python

    • Declaring and initializing lists
    • Indexing, slicing, and updating list elements
    • List methods: append(), insert(), remove(), pop(), sort(), reverse()
    • Nested lists and list comprehensions

  2. Tuples

    • Declaring and using tuples in Python
    • Tuple immutability and use cases
    • Accessing tuple elements and slicing
    • Comparing tuples to lists

  3. Dictionaries

    • Creating and using dictionaries (key-value pairs)
    • Accessing, adding, and removing elements from dictionaries
    • Iterating through dictionaries using loops
    • Dictionary methods: keys(), values(), items(), get(), update()

  4. Sets

    • Understanding sets: unordered collections of unique elements
    • Set operations: union, intersection, difference, and symmetric difference
    • Adding, removing, and checking membership in sets

  5. Stacks and Queues

    • Implementing stacks using lists and collections.deque
    • Implementing queues using lists and collections.deque
    • Stack and queue operations

Module 6: File Handling

Objective: Learn how to work with files (text, CSV, and more) in Python.

  1. Reading and Writing Text Files

    • Opening files with open(): file modes (r, w, a, rb, wb)
    • Reading from files: read(), readline(), readlines()
    • Writing to files: write(), writelines()
    • Using with statement for automatic file handling

  2. CSV File Handling

    • Reading and writing CSV files using csv module
    • Handling CSV data with csv.reader(), csv.writer(), and csv.DictReader()
    • Converting data to/from CSV format

  3. Exception Handling with Files

    • Handling file errors with try, except, finally
    • Using os and shutil for file and directory operations
    • Checking file existence and file manipulations

Module 7: Object-Oriented Programming (OOP) in Python

Objective: Master the principles of object-oriented programming in Python.

  1. Classes and Objects

    • Creating classes and objects in Python
    • Defining methods and attributes in a class
    • Initializing objects using the __init__() constructor

  2. Inheritance

    • Creating subclasses and inheriting from a superclass
    • Using super() to call parent class methods
    • Method overriding in subclasses

  3. Encapsulation

    • Understanding public and private attributes
    • Using getter and setter methods
    • The @property decorator for controlling access to attributes

  4. Polymorphism

    • Achieving polymorphism through method overriding
    • Dynamic method dispatch in Python
    • Using duck typing to achieve polymorphism

  5. Abstraction

    • Introduction to abstract classes using abc module
    • Creating abstract methods and enforcing implementation in subclasses

Module 8: Advanced Python Topics

Objective: Dive into more advanced Python concepts and libraries.

  1. Decorators

    • Understanding decorators and their syntax
    • Creating simple decorators
    • Using decorators for logging, authentication, and more

  2. Generators

    • Introduction to generators using yield
    • Benefits of using generators over regular functions
    • Implementing custom generators for lazy evaluation

  3. Regular Expressions

    • Introduction to regular expressions (regex) in Python
    • Using re module for pattern matching
    • Common regex operations: search(), match(), findall(), sub()

  4. Multithreading and Concurrency

    • Introduction to the threading module for parallel execution
    • Creating and managing threads in Python
    • Synchronizing threads using Lock, Event, Semaphore

  5. Working with APIs

    • Introduction to REST APIs and web scraping
    • Using requests module to make HTTP requests
    • Parsing JSON responses and integrating with external services

Module 9: Final Project and Best Practices

Objective: Apply Python skills to a real-world project and learn best practices.

  1. Capstone Project

    • Plan and design a final project that incorporates all the topics covered in the course (web app, data analysis, etc.)
    • Implementing the project using best practices
    • Debugging and testing the project

  2. Best Practices and Pythonic Code

    • Writing clean, readable, and maintainable code
    • Pythonic conventions: using list comprehensions, unpacking, etc.
    • Code styling with PEP 8

  3. Testing and Debugging

    • Writing unit tests using unittest or pytest
    • Debugging techniques and using IDE debuggers
    • Handling errors and exceptions in a clean way

Course Outcome

By the end of this course, you will:

  • Have a thorough understanding of Python programming concepts from basic syntax to advanced topics
  • Be able to write, debug, and optimize Python code
  • Be proficient in object-oriented programming (OOP) and handle file I/O operations
  • Be able to work with Python’s built-in data structures, and libraries for tasks such as web scraping, APIs, and concurrency
  • Have hands-on experience with real-world Python projects and best coding practices

Target Audience:
This course is ideal for beginners to intermediate learners who want to learn Python and apply it to a variety of domains such as data science, web development, automation, and more. It is suitable for anyone looking to start a career in programming or enhance their existing skills.