{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Challenge: Analyzing Text about Data Science\n",
"\n",
"For dis example, make we do one simple exercise wey go cover all di steps wey dey for traditional data science process. You no need write any code, you fit just click di cells wey dey below to run dem and see wetin e go show. As challenge, e good make you try dis code with different data.\n",
"\n",
"## Goal\n",
"\n",
"For dis lesson, we don dey talk about different concepts wey relate to Data Science. Make we try find more related concepts by doing **text mining**. We go start with one text about Data Science, extract keywords from am, and then try show di result.\n",
"\n",
"Di text wey we go use na di page about Data Science wey dey Wikipedia:\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: How to Collect Data\n",
"\n",
"Di first step for any data science process na to collect di data. We go use `requests` library do am:\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",
"