{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b0d4b792-4839-4617-adbb-26ac104c9b0a",
   "metadata": {},
   "source": [
    "#  Notebook Ninja – Getting Started with Jupyter\n",
    "\n",
    "##  Introduction\n",
    "Jupyter Notebook is an interactive tool used for writing and executing code while also documenting the process using text, images, and visualizations. It is widely used in data science, machine learning, and research because it allows users to combine code, output, and explanations in a single place.\n",
    "\n",
    "In this notebook, I will demonstrate my understanding of Markdown formatting and Python programming, along with basic data visualization using Matplotlib."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e7743fb-0fa8-407b-b0bf-d6e8e03b19fd",
   "metadata": {},
   "source": [
    "## Quest 1: Markdown & Presentation Skills\n",
    "\n",
    "###  Learning Goals\n",
    "Through this notebook, I aim to learn:\n",
    "- How to structure a notebook using Markdown  \n",
    "- How to format text for better readability  \n",
    "- How to present code and explanations clearly\n",
    "\n",
    "### Markdown Formatting Examples\n",
    "\n",
    "- This is **bold text** used to highlight important points  \n",
    "- This is *italic text* used for emphasis  \n",
    "- This is **_bold and italic_** combined  \n",
    "\n",
    "Below is a horizontal line used to separate sections:\n",
    "\n",
    "----------\n",
    "\n",
    "###  Embedded Image\n",
    "\n",
    "Below is the logo of Jupyter Notebook:\n",
    "\n",
    "![Jupyter Logo](https://upload.wikimedia.org/wikipedia/commons/3/38/Jupyter_logo.svg)\n",
    "\n",
    "Images help make notebooks more visually appealing and easier to understand.\n",
    "\n",
    "---\n",
    "\n",
    "### Code Snippet Example\n",
    "\n",
    "Below is a simple Python function shown inside Markdown:\n",
    "\n",
    "```  python\n",
    "def greet(name):\n",
    "    return f\"Hello, {name}! Welcome to Jupyter Notebook.\"\n",
    "print(greet(\"Apeksha\")) \n",
    "     ```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346b7aa4-cb20-4749-b2b0-eae56511781c",
   "metadata": {},
   "source": [
    "\n",
    "## Quest 2: Python Coding & Visualization\n",
    "\n",
    "###  Section Overview\n",
    "\n",
    "In this section, I will perform basic Python operations such as variable declaration and arithmetic calculations. I will also create a simple line graph using Matplotlib to visualize data.\n",
    "\n",
    "This demonstrates how Jupyter Notebook allows users to combine programming with data visualization effectively.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f8cbb798-296a-4ab5-ad9d-1694679e35a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum: 25\n",
      "Difference: 5\n",
      "Product: 150\n"
     ]
    }
   ],
   "source": [
    "# Declaring variables\n",
    "a = 15\n",
    "b = 10\n",
    "\n",
    "# Performing operations\n",
    "sum_result = a + b\n",
    "difference = a - b\n",
    "product = a * b\n",
    "\n",
    "# Displaying results\n",
    "print(\"Sum:\", sum_result)\n",
    "print(\"Difference:\", difference)\n",
    "print(\"Product:\", product)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b48ebb19-9b57-4d74-b01b-23c26a1789d4",
   "metadata": {},
   "source": [
    "###  Explanation of Code\n",
    "\n",
    "In the above code:\n",
    "- Two variables `a` and `b` are declared  \n",
    "- Basic arithmetic operations like addition, subtraction, and multiplication are performed  \n",
    "- The results are displayed using print statements  \n",
    "\n",
    "This shows how Python can be used for simple calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dbdcd722-2ce5-4528-a41f-f08515006600",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data for plotting\n",
    "x = [1, 2, 3, 4, 5]\n",
    "y = [3, 6, 9, 12, 15]\n",
    "\n",
    "# Creating the plot\n",
    "plt.plot(x, y, marker='o')\n",
    "\n",
    "# Adding details\n",
    "plt.title(\"Linear Growth Example\")\n",
    "plt.xlabel(\"X Values\")\n",
    "plt.ylabel(\"Y Values\")\n",
    "\n",
    "# Displaying grid\n",
    "plt.grid()\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fab1b6ad-4fb9-4775-ab31-289488fdaf40",
   "metadata": {},
   "source": [
    "### Graph Explanation\n",
    "\n",
    "The above graph represents a linear relationship between X and Y values. As X increases, Y increases proportionally.\n",
    "\n",
    "This type of visualization helps in understanding patterns and trends in data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ca0226-6cd0-42aa-8867-680eda20fd84",
   "metadata": {},
   "source": [
    "##  Conclusion\n",
    "\n",
    "In this notebook, I have successfully:\n",
    "\n",
    "- Learned how to use Markdown for structuring and formatting content  \n",
    "- Understood how to combine text, images, and code effectively  \n",
    "- Performed basic Python calculations  \n",
    "- Created a simple data visualization using Matplotlib  \n",
    "\n",
    "Jupyter Notebook is a powerful tool that enhances both coding and communication skills. It helps in presenting ideas clearly, which is essential in data science and technical fields.\n",
    "\n",
    "Overall, this exercise improved my confidence in writing clean, organized, and interactive notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b736b7d-2b0c-41be-a380-54e5b04e6aff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}