diff --git a/1-Introduction/01-defining-data-science/notebook.ipynb b/1-Introduction/01-defining-data-science/notebook.ipynb
index 0e9199fc..9713cb3c 100644
--- a/1-Introduction/01-defining-data-science/notebook.ipynb
+++ b/1-Introduction/01-defining-data-science/notebook.ipynb
@@ -2,412 +2,314 @@
"cells": [
{
"cell_type": "markdown",
+ "metadata": {},
"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",
+ "# Challenge: Analyzing Text about Data Science\n",
+ "\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. \n",
+ "\n",
+ "## Goal\n",
+ "\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.\n",
+ "\n",
"As a text, I will use the page on Data Science from Wikipedia:"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
- "source": [],
- "metadata": {}
+ "metadata": {},
+ "source": []
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"url = 'https://en.wikipedia.org/wiki/Data_science'"
- ],
- "outputs": [],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
- "## Step 1: Getting the Data\r\n",
- "\r\n",
+ "## Step 1: Getting the Data\n",
+ "\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,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"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",
- "