From ddf53c54c3166aa39ee2e0d22d16092b30d0c342 Mon Sep 17 00:00:00 2001
From: ezzeldean <franko43rb@gmail.com>
Date: Fri, 20 Sep 2024 08:32:41 +0300
Subject: [PATCH] Add files via upload

---
 DataScienceEcosystem.ipynb | 157 +++++++++++++++++++++++++++++++++++++
 1 file changed, 157 insertions(+)
 create mode 100644 DataScienceEcosystem.ipynb

diff --git a/DataScienceEcosystem.ipynb b/DataScienceEcosystem.ipynb
new file mode 100644
index 0000000..cd810cd
--- /dev/null
+++ b/DataScienceEcosystem.ipynb
@@ -0,0 +1,157 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "91ac5847-9baa-4e25-99ca-5881fc5f2958",
+   "metadata": {},
+   "source": [
+    "# Data Science Tools and Ecosystem"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7655284-0775-4f91-8008-19df2a8e8eb7",
+   "metadata": {},
+   "source": [
+    "In this notebook, Data Science Tools and Ecosystem are summarized."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4b1dee5-dd59-4fc5-b750-d1359c0bc894",
+   "metadata": {},
+   "source": [
+    "**Objectives**\n",
+    "  \n",
+    "- List popular languages for Data Science.\n",
+    "- Identify key libraries used in Data Science.\n",
+    "- Explain the conversion of time units (e.g., minutes to hours).\n",
+    "- Describe open-source tools for data science development.\n",
+    "- Understand basic arithmetic operations in Python."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f74728fd-0f3d-4379-a7d5-b4fd38eefebf",
+   "metadata": {},
+   "source": [
+    "# Some of the popular languages that Data Scientists use are:\n",
+    "\n",
+    "01. Python\n",
+    "02. R\n",
+    "03. SQL"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b7e4bd2-fb5e-4a14-88ef-f9da420ac312",
+   "metadata": {},
+   "source": [
+    "# Some of the commonly used libraries used by Data Scientists include:\n",
+    "\n",
+    "01. Pandas\n",
+    "02. NumPy\n",
+    "03. scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ffc6231-a09b-48d9-84d0-bf27456ecacf",
+   "metadata": {},
+   "source": [
+    "# Data Science Tools\n",
+    "\n",
+    "|Data Science Tools|\n",
+    "|------------------|\n",
+    "| Hadoop |\n",
+    "| RStudio |\n",
+    "| Apache Spark |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae24edad-f46a-4e08-8751-75470ca5253b",
+   "metadata": {},
+   "source": [
+    "### Below are a few examples of evaluating arithmetic expressions in Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d4f48efb-1443-46fa-ba5d-2f02b0101196",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This a simple arithmetic expression to mutiply then add integers\n",
+    "\n",
+    "(3*4)+5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "de583323-f377-4dbd-b6f8-61ce8f8a976a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.3333333333333335"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This will convert 200 minutes to hours by diving by 60\n",
+    "\n",
+    "200/60"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "988c13be-2271-4dda-a270-adf0649afa5b",
+   "metadata": {},
+   "source": [
+    "## Author\n",
+    "\n",
+    "Ezz El Dean Hashish"
+   ]
+  }
+ ],
+ "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}