Python Syllabus
- 1.
Introduction to Python Programming
- 2.
Variables, Data Types, and Operators
- 3.
Control Flow Statements
- 4.
Functions and Modules
- 5.
Python Data Structures
- 6.
File Handling
- 7.
Exception Handling
- 8.
Object-Oriented Programming (OOP) in Python
- 9.
Advanced Python Concepts
- 10.
Working with Libraries
- 11.
File Formats and Data Serialization
- 12.
Working with Databases
- 13.
Python for Web Development (Optional)
- 14.
Unit Testing and Debugging
- 15.
Python and APIs
- 16.
Python for Data Science (Optional)
- 17.
Python for Machine Learning (Optional)
- 18.
Python for Automation and Scripting (Optional)
- 19.
Final Projects and Case Studies
Python Course Syllabus
Introduction to Python Programming
- History and Features of Python
- Installing Python and Setting Up the Environment
- Installing Python (Windows, Mac, Linux)
- Introduction to Python IDEs (PyCharm, VS Code, Jupyter Notebook)
- Running Python Programs
- Interactive Mode vs Script Mode
- Basic Syntax
- Python Keywords and Identifiers
- Writing Comments
Variables, Data Types, and Operators
- Variables and Assignments
- Dynamic Typing, Multiple Assignments
- Python Data Types
- Numbers (int, float, complex)
- Strings, Lists, Tuples, Sets, and Dictionaries
- Type Conversion
- Basic I/O in Python
- input() and print()
- Operators
- Arithmetic, Relational, Logical, Bitwise, Assignment, and Identity Operators
- Operator Precedence
Control Flow Statements
- Conditional Statements
- if, if-else, if-elif-else
- Looping Constructs
- for Loop, while Loop
- Loop Control Statements (break, continue, pass)
- List Comprehensions
Functions and Modules
- Defining and Calling Functions
- Function Parameters and Arguments
- Default Arguments, Keyword Arguments
- Variable-length Arguments (*args, **kwargs)
- return Statement
- Lambda Functions
- Recursion
- Creating and Importing Modules
- Using Built-in Modules (math, random, datetime)
- Installing and Using Third-Party Packages (e.g., pip)
Python Data Structures
- Lists
- Creating, Accessing, Slicing, Modifying Lists
- List Methods and Comprehension
- Tuples
- Creating, Accessing, and Unpacking Tuples
- Sets
- Set Operations (Union, Intersection, Difference)
- Frozen Sets
- Dictionaries
- Creating, Accessing, and Modifying Dictionaries
- Dictionary Methods
- String Manipulation
- String Operations, Methods, and Formatting
- String Slicing
File Handling
- Reading and Writing Files (open(), read(), write(), close())
- Reading and Writing in Text and Binary Mode
- Using with Statement for File Handling
- Exception Handling in File I/O
Exception Handling
- Types of Errors (Syntax Errors, Logical Errors)
- try, except, else, finally Blocks
- Raising Exceptions (raise)
- Creating Custom Exceptions
Object-Oriented Programming (OOP) in Python
- Classes and Objects
- Defining Classes and Creating Objects
- __init__() Method (Constructor)
- Self Parameter
- Class Variables and Instance Variables
- Inheritance
- Single Inheritance, Multiple Inheritance, Multilevel Inheritance
- Method Overriding
- Encapsulation and Abstraction
- Private and Protected Members
- Polymorphism
- Method Overloading, Method Overriding
- super() function
- Magic/Dunder Methods
- __str__(), __repr__(), __len__(), etc.
Advanced Python Concepts
- Iterators and Generators
- Creating Iterators using iter() and next()
- Generators (yield Statement)
- Decorators
- Function and Class Decorators
- Closures
- Context Managers (with Statement)
Working with Libraries
- NumPy (for numerical computations)
- Creating Arrays, Array Operations
- Slicing and Indexing
- Pandas (for data manipulation and analysis)
- Series and DataFrame
- Importing and Exporting Data (CSV, Excel)
- Handling Missing Data
- DataFrame Operations
- Matplotlib (for data visualization)
- Plotting Graphs (Line, Bar, Histogram, Scatter)
- Customizing Plots (Labels, Titles, Legends)
File Formats and Data Serialization
- Working with CSV Files
- Reading and Writing CSV Files
- Working with JSON
- JSON Operations: json.dumps(), json.loads()
- Pickle Module
- Serializing and Deserializing Python Objects
Working with Databases
- SQL vs NoSQL Databases
- SQLite Database Operations
- Connecting to Database, Creating Tables
- CRUD Operations (Create, Read, Update, Delete)
- Executing SQL Queries
- Introduction to ORM (Object-Relational Mapping)
- Using SQLAlchemy
Python for Web Development (Optional)
- Overview of Django and Flask
- Creating a Simple Web Application
- Setting up Flask/Django, Routing, and Templates
- Handling Forms and Requests
- GET and POST Requests
- Working with Databases in Web Apps
Unit Testing and Debugging
- Introduction to Unit Testing
- Using unittest module
- Writing and Running Test Cases
- Debugging in Python
- Using pdb Module
- Common Debugging Techniques
Python and APIs
- Introduction to APIs
- REST APIs and HTTP Requests
- Making API Calls using requests library
- Handling JSON Responses
- Building Simple APIs using Flask
Python for Data Science (Optional)
- Introduction to Data Science Libraries
- NumPy for numerical operations
- Pandas for data manipulation
- Matplotlib and Seaborn for visualization
- SciPy for scientific computing
- Exploratory Data Analysis (EDA)
Python for Machine Learning (Optional)
- Overview of Machine Learning Libraries (Scikit-learn)
- Supervised Learning
- Linear and Logistic Regression
- Decision Trees, Random Forests
- Unsupervised Learning
- Clustering (K-means)
- Model Evaluation
- Train/Test Split, Cross-Validation
- Performance Metrics (Accuracy, Precision, Recall)
Python for Automation and Scripting (Optional)
- Automating Tasks with Python
- File and Folder Automation
- Web Scraping using BeautifulSoup and Scrapy
- Working with OS Module
- File/Directory Operations
- Working with Email and FTP Automation
Final Projects and Case Studies
- Capstone Project
- Building a Python Project combining all learned concepts (choose from web development, data science, automation, etc.)
- Real-World Case Studies
- Implementing case studies related to Python in domains like finance, healthcare, or social media analytics
