diff --git a/1-Introduction/01-defining-data-science/notebook.ipynb b/1-Introduction/01-defining-data-science/notebook.ipynb
index cf3988e8..5edafb9b 100644
--- a/1-Introduction/01-defining-data-science/notebook.ipynb
+++ b/1-Introduction/01-defining-data-science/notebook.ipynb
@@ -1,419 +1,736 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "source": [
- "# Challenge: Analyzing Text about Data Science\r\n",
- "\r\n",
- "In this example, let's do a simple exercise that covers all steps of a traditional data science process. You do not have to write any code, you can just click on the cells below to execute them and observe the result. As a challenge, you are encouraged to try this code out with different data. \r\n",
- "\r\n",
- "## Goal\r\n",
- "\r\n",
- "In this lesson, we have been discussing different concepts related to Data Science. Let's try to discover more related concepts by doing some **text mining**. We will start with a text about Data Science, extract keywords from it, and then try to visualize the result.\r\n",
- "\r\n",
- "As a text, I will use the page on Data Science from Wikipedia:"
- ],
- "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: Getting the Data\r\n",
- "\r\n",
- "First step in every data science process is getting the data. We will use `requests` library to do that:"
- ],
- "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",
- "