Basic Python Language
with project

1. Introduction to Python

  • Overview of Python
  • Installing Python and setting up the environment
  • Writing and executing your first Python program
  • Python IDEs (e.g., Jupyter Notebook, PyCharm)

2. Basic Syntax and Data Types

  • Python syntax and indentation
  • Variables and data types (int, float, str, bool)
  • Basic operations (arithmetic, comparison, logical)
  • Type conversion and casting

3. Control Structures

  • Conditional statements (if, elif, else)
  • Looping structures (for, while)
  • Break, continue, and pass statements

4. Functions

  • Defining and calling functions
  • Function arguments and return values
  • Default arguments, keyword arguments, and variable-length arguments
  • Lambda functions

5. Data Structures

  • Lists: creation, indexing, slicing, and operations
  • Tuples: properties and operations
  • Dictionaries: key-value pairs, operations, and methods
  • Sets: operations and methods

6. String Manipulation

  • String operations and methods
  • String formatting
  • Regular expressions (basic introduction)

7. Modules and Packages

  • Importing modules
  • Standard Python libraries (math, datetime, os, etc.)
  • Creating and using custom modules
  • Installing and using external packages with pip

8. File Handling

  • Reading from and writing to files
  • Working with file paths
  • Error handling with file operations

9. Exception Handling

  • Introduction to exceptions
  • Try, except, finally blocks
  • Raising exceptions
  • Custom exceptions

10. Object-Oriented Programming (OOP)

  • Classes and objects
  • Constructors and destructors
  • Inheritance, polymorphism, encapsulation, and abstraction
  • Method overloading and overriding

11. Working with Libraries

  • Introduction to popular libraries (NumPy, Pandas, Matplotlib)
  • Basic data manipulation with Pandas
  • Data visualization with Matplotlib

12. Introduction to Databases

  • Connecting to databases using Python
  • Performing CRUD operations
  • Working with SQLite or MySQL databases

13. Basic Web Scraping

  • Introduction to web scraping
  • Using libraries like BeautifulSoup and Requests
  • Parsing HTML and extracting data

14. Basic Python for Data Science

  • Introduction to Data Science concepts
  • Basic data analysis with Pandas
  • Data visualization techniques

15. Final Projects and Practice

  • Mini projects to reinforce learning
  • Practice exercises and problem-solving

Advanced Python Language with
Real time project

1. Advanced Data Structures

  • Custom Data Structures
  • Collections Module: namedtuple, deque, Counter, OrderedDict, defaultdict
  • Sets and Frozensets
  • Heapq and Priority Queues
  • Binary Trees, Graphs, and Tries

2. Object-Oriented Programming (OOP) in Depth

  • Advanced OOP Concepts: Inheritance, Multiple Inheritance, Mixins
  • Abstract Base Classes (ABC)
  • Property Decorators (@property)
  • Magic Methods and Operator Overloading
  • Design Patterns in Python: Singleton, Factory, Observer, etc.
  • Metaclasses and Dynamic Class Creation

3. Functional Programming

  • Lambdas and Higher-Order Functions
  • Map, Filter, Reduce
  • Comprehensions and Generators
  • Decorators
  • Immutability and Pure Functions
  • Functools Module: lru_cache, partial, etc.

4. Iterators and Generators

  • Creating Iterators
  • Custom Generators with yield
  • Generator Expressions
  • Coroutines and yield from
  • Asynchronous Generators

5. Concurrency and Parallelism

  • Threading: threading module, Thread Synchronization, Deadlocks
  • Multiprocessing: multiprocessing module, Process Pooling
  • Asynchronous Programming: asyncio module, Async/Await, Event Loop
  • Concurrent Futures
  • Parallel Computing with Dask
  • GIL (Global Interpreter Lock) and its Implications

6. File Handling and Data Serialization

  • Advanced File Handling
  • Context Managers and the with Statement
  • Data Serialization/Deserialization: JSON, Pickle, YAML, XML
  • Working with CSV, Excel, and HDF5 Files
  • Data Interchange Formats

7. Regular Expressions

  • Introduction to Regular Expressions
  • Advanced Pattern Matching
  • Substitutions and Grouping
  • Working with Complex Text Patterns

8. Networking and Web Scraping

  • Socket Programming
  • HTTP and REST APIs
  • Requests Library for HTTP Requests
  • Web Scraping with BeautifulSoup and Scrapy
  • Automating Browser Tasks with Selenium

9. Testing and Debugging

  • Unit Testing with unittest, pytest
  • Test-Driven Development (TDD)
  • Mocking and Patching
  • Profiling and Optimization
  • Debugging Techniques and Tools (pdb, ipdb, etc.)
  • Static Code Analysis

10. Packaging and Distribution

  • Creating Python Packages
  • Setup Tools and setup.py
  • Publishing to PyPI
  • Versioning and Dependency Management
  • Virtual Environments with venv and virtualenv

11. Advanced Libraries and Frameworks

  • NumPy and Pandas for Data Analysis
  • Matplotlib and Seaborn for Data Visualization
  • Scikit-Learn for Machine Learning
  • TensorFlow/PyTorch for Deep Learning
  • Django/Flask for Web Development
  • SQLAlchemy for Database Interaction

12. Advanced Topics

  • Metaprogramming
  • Reflection and Introspection
  • Building and Interacting with RESTful APIs
  • Security Practices in Python
  • Memory Management and Garbage Collection
  • Advanced Logging Techniques

13. Project Work

  • Capstone Project: Implement a complex project that incorporates multiple advanced Python concepts, such as building a web application, developing a machine learning model, or creating an automation tool.
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