A day wise course curriculum to start python from scratch.

Day-by-day curriculum for learning Python from scratch. Each day builds upon the previous one, gradually introducing new concepts and reinforcing earlier ones.

Day 1: Introduction to Python

• 1.Overview of Python: history, features, and applications.
• 2.Setting up Python environment (installing Python, IDEs like PyCharm or Jupyter Notebook).
• 3.Writing your first Python program (Hello World!).
• 4.Basic data types: integers, floats, strings, booleans.
• 5.Basic operations: arithmetic, string concatenation, comparison.
• 6.Using print() function to output text.

Day 2: Variables and Data Structures

• 1.Declaring variables and variable naming conventions.
• 2.Dynamic typing in Python.
• 3.Lists: creating, accessing elements, list methods.
• 4.Tuples: creating, accessing elements (immutable sequences).
• 5.Dictionaries: creating, accessing elements by key, dictionary methods.

Day 3: Control Flow

• 1.Conditional statements (if, elif, else).
• 2.Using logical operators (and, or, not).
• 3.Loops: for loops, while loops.
• 4.Loop control statements: break, continue.
• 5.Iterating through lists and dictionaries.

Day 4: Functions

• 1.Defining and calling functions.
• 2.Function parameters and arguments.
• 3.Return statement and returning values from functions.
• 4.Scope of variables: local vs global.
• 5.Built-in functions vs user-defined functions.

Day 5: More on Functions and Modules

• 1.Default arguments and keyword arguments.
• 2.Lambda functions.
• 3.Importing modules and using functions from them.
• 4.Creating your own modules.
• 5.Documenting functions using docstrings.

Day 6: File Handling

• 1.Opening and closing files.
• 2.Reading from and writing to files.
• 3.File modes: read, write, append.
• 4.Working with text files.
• 5.Handling exceptions using try-except blocks.

Day 7: Object-Oriented Programming Basics

• 1.Introduction to object-oriented programming (OOP).
• 2.Classes and objects.
• 3.Attributes and methods.
• 4.Constructors and destructors.
• 5.Inheritance and polymorphism.

Day 8: More on OOP

• 1.Encapsulation and access modifiers.
• 2.Class and static methods.
• 3.Method overriding and method overloading.
• 4.Special methods (dunder/magic methods).
• 5.Implementing OOP concepts in Python.

Day 9: Error Handling and Debugging

• 1.Understanding exceptions and error types.
• 2.Using try-except blocks for error handling.
• 3.Raising exceptions with raise.
• 4.Debugging techniques: print debugging, using debuggers.
• 5.Handling errors gracefully in your programs.

Day 10: Final Projects and Review

• 1.Work on a small project incorporating concepts learned.
• 2.Review all concepts covered in the past days.
• 3.Practice problem-solving on platforms like LeetCode, HackerRank, or Codecademy.
• 4.Seek help or clarification on any challenging topics.
• 5.Plan your next steps in Python learning.

We can adjust the pace based on our learning speed and schedule. It is not mandatory and it is not suggested to read the above all in 10 days . we may take 100 days to learn the basic python before stepping into the more advanced topics such as web development, data science, machine learning, or automation based on our interests and career goals.

 

Perquisites that are needed to start with Python

While there aren't any strict prerequisites for starting with Python, here are some things that will be helpful:

• Basic computer literacy: Familiarity with using a computer, navigating files and folders, and basic understanding of how software works will be beneficial.

• Logical thinking: Being able to break down problems into smaller steps and think logically is crucial for programming.

• Problem-solving skills: Programming involves solving problems and finding solutions, so good problem-solving skills are essential.

• Interest and motivation: Having a genuine interest in learning and a desire to code will keep you motivated throughout the learning process.

Optional, but helpful:

Basic understanding of mathematics: Familiarity with basic mathematical concepts like algebra and logic can be helpful for certain aspects of Python, especially when working with data or scientific computing.

Prior programming experience: While not necessary, having some experience with another programming language can give you a head start in understanding the fundamental concepts of programming that apply to Python as well.

Beyond these, the key things you'll need to get started with Python are:

A computer: Any modern computer will suffice.

Python interpreter: Download and install the latest version of Python from the official website: https://www.python.org/downloads/

Text editor or IDE: While you can use a simple text editor like Notepad, using a code editor or Integrated Development Environment (IDE) specifically designed for Python will provide features like syntax highlighting, code completion, and debugging, making your development experience more efficient and enjoyable. Popular options include: 

Text editors: Sublime Text, Visual Studio Code, Atom
IDEs: PyCharm, Spyder, Thonny

How are Python applications installed on a computer, and what program writing applications do we need to install?

Installing Python Applications:

• Use 'pip': This is the most common method. pip comes with Python and lets you install applications directly from the command line. Just type pip install <application_name> (e.g., pip install django).
• Manual installation: Some applications might have specific instructions. Follow them carefully for successful installation.

Programs for Writing Python Applications:

Text editor/IDE: Choose one that suits your needs. Popular options include:

Text editors: 

• You don't need a specific program to write Python code, but a good text editor or Integrated Development Environment (IDE) can significantly enhance your experience.
• These tools offer features like syntax highlighting, code completion, debugging, and project management, making Python development more efficient and enjoyable.
• Sublime Text, Visual Studio Code, Atom.

IDEs: PyCharm, Spyder, Thonny

Python interpreter: This comes bundled with the official Python download (https://www.python.org/downloads/).

Bonus Tip: Use virtual environments to isolate project dependencies and avoid conflicts. Popular tools include venv and virtualenv.

Remember: This is a general guide. Specific requirements might vary depending on the application and your project.

 What exactly we can do with Python?

Python's versatility makes it a powerful tool across various domains. Here are some of the things you can achieve with Python:

Web Development:

• Back-end development: Python excels at building the server-side logic of websites and applications. Frameworks like Django and Flask streamline creating complex web functionalities.
• Data handling: Python efficiently processes and manages data within web applications, interacting with databases and ensuring smooth data flow.

Data Science and Machine Learning:

• Data analysis and manipulation: Python offers powerful libraries like NumPy, Pandas, and Matplotlib for data cleaning, exploration, and visualization.
• Machine learning: Python is a popular choice for building and deploying machine learning models due to its extensive libraries like TensorFlow, PyTorch, and scikit-learn.

Automation and Scripting:

• Automating tasks: Python scripts can automate repetitive tasks, saving time and effort.expand_more This can involve file management, data processing, web scraping, and more.expand_more
• System administration: Python can manage system tasks like network configuration, server administration, and deployment automation.

Other Applications:

• Game development: Libraries like Pygame enable creating various game genres, from simple 2D games to more complex simulations.
• Desktop applications: Python can build user-friendly desktop applications with graphical interfaces using frameworks like Tkinter and PyQt.
• Scientific computing: Python's numerical computing capabilities make it suitable for scientific research, simulations, and data analysis in various fields.

Beyond these, Python can be used for: -

• Web scraping: Extracting data from websites.
• Software testing: Automating software testing processes.
• Network programming: Building network applications and tools.
• Blockchain development: Creating blockchain applications and smart contracts.

Applications of python