{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Challenge: Analyzing Text about Data Science\n",
"\n",
"In this example, let's do a simple exercise that includes all the steps of a typical data science process. You don't need to write any code; you can simply click on the cells below to run them and observe the results. As a challenge, you're encouraged to test this code with different data.\n",
"\n",
"## Goal\n",
"\n",
"In this lesson, we've been discussing various concepts related to Data Science. Let's explore more related concepts by performing **text mining**. We'll start with a text about Data Science, extract keywords from it, and then attempt to visualize the results.\n",
"\n",
"For the text, we'll use the Wikipedia page on Data Science:\n"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 62,
"source": [
"url = 'https://en.wikipedia.org/wiki/Data_science'"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Step 1: Obtaining the Data\n",
"\n",
"The first step in any data science process is obtaining the data. We'll use the `requests` library for this:\n"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 63,
"source": [
"import requests\r\n",
"\r\n",
"text = requests.get(url).content.decode('utf-8')\r\n",
"print(text[:1000])"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"\n",
"\n",
"\n",
"Data science - Wikipedia\n",
"