🎖️🔰 WHAT YOU GET? 🔰🎖️
- Industry level coding proficieny in Python programming langugage
- Direct interaction with industry experts
- Industry project experience
- Generic programming fundamentals that apply across programming languages
- Soft skill training (basic)
🌐 INTRODUCTION TO PYTHON 🌐
- Why python?
- What is open source?
- Python land-scape in the industry
- Future of Python in industry
📙 Module 1 - Python basics
- Python as general purpose programming language
- Installation
- Python as interpreter
- Hello, World!
- Literals
- Python Comments
- data types
- variables
- Collecting User Input
- Getting Help
- Lab : Exercises in this Lesson
- code 1
- code 2
- code 3
📙 Module 2 - data types
- Text Type: str
- Numeric Types: int, float, complex
- Sequence Types: list, tuple, range
- Mapping Type: dict
- Set Types: set, frozenset
- Boolean Type: bool
- Binary Types: bytes, bytearray, memoryview
- None Type: NoneType
- string formatting, magic strings
- Concept of native data types vs custom data types
- Lab : Exercises in this Lesson
- code 1 (tuple - packing, unpacking)
- code 2
- code 3
📙 Module 3 - Operators and expressions
- Operators
- Integer arithmetic using operators
- Relational operators
- Logical operators
- Shorthand operators
- Expressions
- Type conversions (aka type casting)
- Lab : Exercises in this Lesson
- code 1
- code 2
- code 3
📙 Module 4 - Python source file
- Python source file
- Whitespaces and indentations
- A Simple Python Script
- Evaluation of your code from python file in the interpreter
- Introduction to editors
- Concept of IDE
- Pycharm installation
- Lab : Exercises in this Lesson
- code 1
- code 2
- code 3
📙 Module 5 - Control flow
- If else
- Finally
- Conditional statements
- Truth value testing
- all() & any()
📙 Module 6 - Looping
- While loop
- Lab : Exercises in this Lesson
- Fibonacci series
- Power series
- Multiplication table
- Some printing * examples
- Iterating on list
- For loop
- Range function
- Break & Continue statements
- Else loop
- List comprehensions
- code 1 (list-comprehension)
- code 2 (list as stack)
- code 3 (list as queue)
- code 4 (nested list comprehension)
- Dict comprehensions
- `pass` statement
- Lab : Exercises in this Lesson
- Game of sticks
- Word guessing game
- Find the niddle
- Rolling dice
📙 Module 7 - Functions and Modules
- Defining functions
- Variable scope
- Global variables
- Namespaces
- Function parameters (positional & keyword)
- Default argument value
- Returning value
- *args, **kwargs
- Keyword only arguments
- Docstrings
- Higher order functions
- Map function
📙 Module 8 - Python built-in functions
- hash()
- dir()
- help()
📙 Module 9 - Modules
- module vs packages
- built-in modules
- external modules
- importing modules
- sub-modules
- default modules
- requests module
- os module
- shutil
- datetime
- time
📙 Module 10 - File handling
- File opening
- Closing file
- Reading content from file
- Writing content to a file
- Random seeking in a file
- Lab : Exercises in this Lesson
- copy file
- count spaces, tabs and newline characters in a file
- with statement (context-managers)
- Rolling dice
📙 Module 11 - Exceptions
- how to handle exceptions?
- multiple exceptions
- using finally for cleanup
📙 Module 12 - Collections module
- Counter
- Defaultdict
- Named tuple
📙 Module 13 - using Pycharm as your primary python editor
- Installing pycharm
- Explore pycharm features
📙 Module 14 - creating a command line tool
- what is command line tool?
- Lab : create a CLI tool
📙 Module 15 - Object oriented Python (Classes)
- class definition syntax
- class objects
- instance objects
- method objects
- class and instance variables
- inheritance
- encapsulation
- polymorphism
- abstraction
- multiple inheritance
- factory classes
- mixin classes
- private variables
- property (getter and setter methods)
📙 Module 16 - Iterators, generators and decorators
- Iterators
- Generators
- Generator expressions
- Closures
- Decorators
📙 Module 17 - Regular expressions
📙 Module 18 - Pandas
- Pandas series
- Dataframes
- File handling (read csv, read sql, read json etc)
- Analyzing data using pandas
- Data cleaning in pandas
📙 Module 19 - Numpy
📙 Module 20 - understanding packages
- how to install packages?
- how to manage dependencies?
- how to package and distribute?
📙 Module 21 - Virtualenv
- Installation
- Usage
- Advanced concepts
📙 Module 22 - Python web framework
- Introduction
- Flask
- Django
- FastAPI
📙 Module 23 - Django
- Installation
- Create django project
- Django models
- Django admin console
- Django URLs
- Django views
- Django templates (HTML and CSS)
- Deployment
📙 Module 24 - ORM
- Django ORM
- ORM using sqlalchemy
📙 Module 25 - Flask
- Flask application
- Flask ecosystem
📙 Module 26 - FastAPI
- WSGI vs ASGI
- Python3 features in FastAPI
- Project in FastAPI
- Swagger
📙 Module 27 - Multi-threading and multi-processing
- Programming practices
- Use cases
📙 Module 28 - AsyncIO
- Introduction to asynchronous programming
📙 Module 29 - Coding standards
- PEP-8 standards
- Tabs and spaces
- Code layout
- Minimum line length
- Blank lines
- Order of imports
- Comments (don't do the obvious)
- Naming conventions
- Type hinting and annotations
- Static code analysis
- Black and mypy
📙 Module 30 - debugging python code
- Debugging in IDE
- Pdb and breakpoint
📙 Module 31 - Design patterns
- Introduction
📙 Module 32 - Linux fundamentals
📙 Module 33 - GIT and GITHUB
📙 Module 34 - JIRA
📙 Module 35 - API testing using postman
📙 Module 36 - SQL and Database
📙 Module 37 - Project deployment and cloud platforms
- Gunicorn
- Nginx
- Cloud platforms
📙 Module 38 - Real time projects according to industry requirements
- Command line applications
- Web applications
- Desktop applications
- Lab : Exercises in this Lesson
- Building a command line application with click
- Building a web application using flask, flask, django
- Building a desktop application using Pypercard
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent