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A practical and beginner-friendly repository for learning Python for Data Science. This collection includes hands-on Jupyter Notebooks covering essential Python concepts used in data analysis, machine learning, and real-world data workflows. Perfect for students, aspiring data scientists, and professionals starting their Python journey.

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📚 DataDrift with Us - Python Learning Journey

Welcome to DataDrift with Us! This repository documents our journey through learning Python from the basics. Below is a summary of what we’ve covered so far.


📘 Day 1 - Python Basics Introduction

  • Introduction to Python
  • Using Google Colab
  • GitHub Basics
  • Python Syntax
  • Comments (Single-line and Multi-line)
  • Variables
  • Data Types
  • F-Strings

📗 Day 2 - User Input, Operators & Conditionals

  • Taking Input using input()
  • Display Output with print()
  • Checking Data Types using type()
  • Typecasting: Converting between str, int, and float
  • Operators: +, -, *, /, %, //
  • Conditional Statements: if, else, elif
  • Logical Operators: and, or, not

📙 Day 3 - String Slicing & Loops

  • String Indexing and Slicing
  • Slice Syntax: [start:end:step]
  • for Loops
  • while Loops
  • Loop Control Statements:
    • break – exit the loop
    • continue – skip to the next iteration
    • pass – do nothing (placeholder)

📕 Day 4 - Functions

  • Definition of a Function
  • Function Syntax
  • Calling a Function
  • Function Parameters / Arguments
  • Return Statement
  • Scope and Lifetime of Variables
  • Local and Global Variables
  • User-defined Functions
  • Default Arguments
  • Keyword Arguments
  • Variable-length Arguments: *args and **kwargs

📙 Day 5 - Intermediate Project: Student Management System

  • Creating and using multiple functions
  • Lists and dictionaries to manage data
  • Building a simple console-based menu
  • User interaction through loops and input
  • Practicing modular and reusable code

📘 Day 6 - Lists and Tuples

  • Introduction to Lists and Tuples
  • List Syntax and Examples
  • Accessing, Modifying, and Deleting Elements
  • Common List Methods: append(), insert(), extend(), remove(), pop(), sort(), reverse(), copy()
  • Looping through Lists
  • List Comprehensions
  • Tuple Syntax and Examples
  • Tuple Immutability
  • Tuple Methods: count(), index()
  • List ↔ Tuple Conversion
  • List vs Tuple Comparison
  • Intermediate Techniques: Slicing, Unpacking, Nested Lists
  • Practice Exercises

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A practical and beginner-friendly repository for learning Python for Data Science. This collection includes hands-on Jupyter Notebooks covering essential Python concepts used in data analysis, machine learning, and real-world data workflows. Perfect for students, aspiring data scientists, and professionals starting their Python journey.

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