slide_deck_template.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# (Investigating Courses Data )\n",
    "## by (Omar Waheed )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Investigation Overview\n",
    "\n",
    "> In this investigation I tried to find all valuable relations between variables and each other to make the best use of the data set  \n",
    "\n",
    "## Dataset Overview\n",
    "\n",
    "> This data set is sample of udemy courses data  gathered by using udemy devolper api and consists of 3682 row and 12 column this data collected at the beginning of 2020 for analysis purpose .\n",
    ">here is the most important data columns .\n",
    "\n",
    "| column name | usage |\n",
    "|--|--|\n",
    "| title | course name on the site  | \n",
    "| avg_rating | course rate  | \n",
    "| current_price | current course price  | \n",
    "| num_subscribers | number of course subscribers | \n",
    "| num_reviews | number of reviews for the course | \n",
    "| num_lectures | number of course lectures  | \n",
    "| primary_category | the main category that this course belong to  | \n",
    "| primary_subcategory | the sub category that this course belong to | \n",
    "| course_creation_date | date of creation  |\n",
    "| course_level | the level of knowledge this course belongs to  |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "base_color = sb.color_palette()[0]\n",
    "%matplotlib inline\n",
    "\n",
    "# suppress warnings from final output\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <td>Deep Learning : Computer Vision Beginner to Ad...</td>\n",
       "      <td>Machine Learning and Data Science Hands-on wit...</td>\n",
       "      <td>Machine Learning (ML) Bootcamp: Python, Tensor...</td>\n",
       "      <td>Learn to Code with Python 3!</td>\n",
       "      <td>Mastering Visual Studio Code</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>url</th>\n",
       "      <td>https://www.udemy.com/course/deep-learning-pyt...</td>\n",
       "      <td>https://www.udemy.com/course/machine-learning-...</td>\n",
       "      <td>https://www.udemy.com/course/ml-bootcamp/</td>\n",
       "      <td>https://www.udemy.com/course/python3-for-begin...</td>\n",
       "      <td>https://www.udemy.com/course/mastering-visual-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>2494264</td>\n",
       "      <td>1978034</td>\n",
       "      <td>2229568</td>\n",
       "      <td>2371066</td>\n",
       "      <td>2664746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_rating</th>\n",
       "      <td>4.39286</td>\n",
       "      <td>3.72727</td>\n",
       "      <td>4.4375</td>\n",
       "      <td>4.11301</td>\n",
       "      <td>4.25806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>199.99</td>\n",
       "      <td>199.99</td>\n",
       "      <td>189.99</td>\n",
       "      <td>199.99</td>\n",
       "      <td>49.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>current_price</th>\n",
       "      <td>11.99</td>\n",
       "      <td>11.99</td>\n",
       "      <td>11.99</td>\n",
       "      <td>11.99</td>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>headline</th>\n",
       "      <td>Go Beginner to Pro in Computer Vision in Pytor...</td>\n",
       "      <td>Machine Learning, Statistics, Python, AI, Tens...</td>\n",
       "      <td>Master the 3 M's of ML: Maths, Methods and Mac...</td>\n",
       "      <td>Go from beginner to advanced with the Python p...</td>\n",
       "      <td>Boost your productivity by using the best IDE ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_subscribers</th>\n",
       "      <td>7758</td>\n",
       "      <td>46560</td>\n",
       "      <td>6860</td>\n",
       "      <td>92051</td>\n",
       "      <td>3119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_reviews</th>\n",
       "      <td>85</td>\n",
       "      <td>1103</td>\n",
       "      <td>8</td>\n",
       "      <td>1191</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_lectures</th>\n",
       "      <td>74</td>\n",
       "      <td>522</td>\n",
       "      <td>48</td>\n",
       "      <td>78</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_category</th>\n",
       "      <td>development</td>\n",
       "      <td>development</td>\n",
       "      <td>development</td>\n",
       "      <td>development</td>\n",
       "      <td>development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_category_link</th>\n",
       "      <td>https://www.udemy.com/courses/development/</td>\n",
       "      <td>https://www.udemy.com/courses/development/</td>\n",
       "      <td>https://www.udemy.com/courses/development/</td>\n",
       "      <td>https://www.udemy.com/courses/development/</td>\n",
       "      <td>https://www.udemy.com/courses/development/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_subcategory</th>\n",
       "      <td>data-science</td>\n",
       "      <td>data-science</td>\n",
       "      <td>data-science</td>\n",
       "      <td>programming-languages</td>\n",
       "      <td>other-it-and-software</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_subcategory_link</th>\n",
       "      <td>https://www.udemy.com/courses/development/data...</td>\n",
       "      <td>https://www.udemy.com/courses/development/data...</td>\n",
       "      <td>https://www.udemy.com/courses/development/data...</td>\n",
       "      <td>https://www.udemy.com/courses/development/prog...</td>\n",
       "      <td>https://www.udemy.com/courses/development/othe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>course_creation_date</th>\n",
       "      <td>2019-08-05T03:56:57Z</td>\n",
       "      <td>2018-10-20T07:06:41Z</td>\n",
       "      <td>2019-02-19T19:41:12Z</td>\n",
       "      <td>2019-05-16T20:52:02Z</td>\n",
       "      <td>2019-11-20T14:57:47Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instructor_name</th>\n",
       "      <td>Manifold AI Learning ®</td>\n",
       "      <td>EDU CBA</td>\n",
       "      <td>Samuel Reischl</td>\n",
       "      <td>Joseph Delgadillo</td>\n",
       "      <td>Alex Horea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instructor_Link</th>\n",
       "      <td>https://www.udemy.com/user/manifold-ai-learning/</td>\n",
       "      <td>https://www.udemy.com/user/premananda-suna/</td>\n",
       "      <td>https://www.udemy.com/user/samuel-reischl/</td>\n",
       "      <td>https://www.udemy.com/user/josephdelgadillo2/</td>\n",
       "      <td>https://www.udemy.com/user/alexhorea/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>what_you_will_learn</th>\n",
       "      <td>['Master how to Perform Computer Vision Task w...</td>\n",
       "      <td>['Learn the use of Python for Data Science and...</td>\n",
       "      <td>['The three building blocks of Machine Learnin...</td>\n",
       "      <td>['Introduction to Python and Setup', 'Python P...</td>\n",
       "      <td>['Install and configure VSCode', 'Handle Exten...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>description_non_formatted</th>\n",
       "      <td>&lt;p&gt;With the &lt;strong&gt;Deep learning&lt;/strong&gt; mak...</td>\n",
       "      <td>&lt;p&gt;Learn from well designed, well-crafted stud...</td>\n",
       "      <td>&lt;p&gt;Do you want to &lt;strong&gt;master Machine Learn...</td>\n",
       "      <td>&lt;p&gt;If you would like to learn &lt;strong&gt;Python 3...</td>\n",
       "      <td>&lt;p&gt;&lt;strong&gt;Visual Studio Code&lt;/strong&gt; is the ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reviews</th>\n",
       "      <td>[[5.0, 'good course with neat explanations'], ...</td>\n",
       "      <td>[[5.0, 'the teaching is very good and they giv...</td>\n",
       "      <td>[[4.0, 'El contenido ha sido claro en el proce...</td>\n",
       "      <td>[[4.0, 'yes'], [3.5, 'the recording is kind of...</td>\n",
       "      <td>[[5.0, 'Vscode has so many features and it’s e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>course_level</th>\n",
       "      <td>All Levels</td>\n",
       "      <td>All Levels</td>\n",
       "      <td>All Levels</td>\n",
       "      <td>All Levels</td>\n",
       "      <td>Intermediate Level</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>small_image_link</th>\n",
       "      <td>https://i.udemycdn.com/course/96x54/2494264_9a...</td>\n",
       "      <td>https://i.udemycdn.com/course/96x54/1978034_1c...</td>\n",
       "      <td>https://i.udemycdn.com/course/96x54/2229568_3a...</td>\n",
       "      <td>https://i.udemycdn.com/course/96x54/2371066_74...</td>\n",
       "      <td>https://i.udemycdn.com/course/96x54/2664746_b9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium_image</th>\n",
       "      <td>https://i.udemycdn.com/course/125_H/2494264_9a...</td>\n",
       "      <td>https://i.udemycdn.com/course/125_H/1978034_1c...</td>\n",
       "      <td>https://i.udemycdn.com/course/125_H/2229568_3a...</td>\n",
       "      <td>https://i.udemycdn.com/course/125_H/2371066_74...</td>\n",
       "      <td>https://i.udemycdn.com/course/125_H/2664746_b9...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                           0  \\\n",
       "title                      Deep Learning : Computer Vision Beginner to Ad...   \n",
       "url                        https://www.udemy.com/course/deep-learning-pyt...   \n",
       "id                                                                   2494264   \n",
       "avg_rating                                                           4.39286   \n",
       "price                                                                 199.99   \n",
       "current_price                                                          11.99   \n",
       "headline                   Go Beginner to Pro in Computer Vision in Pytor...   \n",
       "num_subscribers                                                         7758   \n",
       "num_reviews                                                               85   \n",
       "num_lectures                                                              74   \n",
       "primary_category                                                 development   \n",
       "primary_category_link             https://www.udemy.com/courses/development/   \n",
       "primary_subcategory                                             data-science   \n",
       "primary_subcategory_link   https://www.udemy.com/courses/development/data...   \n",
       "course_creation_date                                    2019-08-05T03:56:57Z   \n",
       "instructor_name                                       Manifold AI Learning ®   \n",
       "instructor_Link             https://www.udemy.com/user/manifold-ai-learning/   \n",
       "what_you_will_learn        ['Master how to Perform Computer Vision Task w...   \n",
       "description_non_formatted  <p>With the <strong>Deep learning</strong> mak...   \n",
       "reviews                    [[5.0, 'good course with neat explanations'], ...   \n",
       "course_level                                                      All Levels   \n",
       "small_image_link           https://i.udemycdn.com/course/96x54/2494264_9a...   \n",
       "medium_image               https://i.udemycdn.com/course/125_H/2494264_9a...   \n",
       "\n",
       "                                                                           1  \\\n",
       "title                      Machine Learning and Data Science Hands-on wit...   \n",
       "url                        https://www.udemy.com/course/machine-learning-...   \n",
       "id                                                                   1978034   \n",
       "avg_rating                                                           3.72727   \n",
       "price                                                                 199.99   \n",
       "current_price                                                          11.99   \n",
       "headline                   Machine Learning, Statistics, Python, AI, Tens...   \n",
       "num_subscribers                                                        46560   \n",
       "num_reviews                                                             1103   \n",
       "num_lectures                                                             522   \n",
       "primary_category                                                 development   \n",
       "primary_category_link             https://www.udemy.com/courses/development/   \n",
       "primary_subcategory                                             data-science   \n",
       "primary_subcategory_link   https://www.udemy.com/courses/development/data...   \n",
       "course_creation_date                                    2018-10-20T07:06:41Z   \n",
       "instructor_name                                                      EDU CBA   \n",
       "instructor_Link                  https://www.udemy.com/user/premananda-suna/   \n",
       "what_you_will_learn        ['Learn the use of Python for Data Science and...   \n",
       "description_non_formatted  <p>Learn from well designed, well-crafted stud...   \n",
       "reviews                    [[5.0, 'the teaching is very good and they giv...   \n",
       "course_level                                                      All Levels   \n",
       "small_image_link           https://i.udemycdn.com/course/96x54/1978034_1c...   \n",
       "medium_image               https://i.udemycdn.com/course/125_H/1978034_1c...   \n",
       "\n",
       "                                                                           2  \\\n",
       "title                      Machine Learning (ML) Bootcamp: Python, Tensor...   \n",
       "url                                https://www.udemy.com/course/ml-bootcamp/   \n",
       "id                                                                   2229568   \n",
       "avg_rating                                                            4.4375   \n",
       "price                                                                 189.99   \n",
       "current_price                                                          11.99   \n",
       "headline                   Master the 3 M's of ML: Maths, Methods and Mac...   \n",
       "num_subscribers                                                         6860   \n",
       "num_reviews                                                                8   \n",
       "num_lectures                                                              48   \n",
       "primary_category                                                 development   \n",
       "primary_category_link             https://www.udemy.com/courses/development/   \n",
       "primary_subcategory                                             data-science   \n",
       "primary_subcategory_link   https://www.udemy.com/courses/development/data...   \n",
       "course_creation_date                                    2019-02-19T19:41:12Z   \n",
       "instructor_name                                               Samuel Reischl   \n",
       "instructor_Link                   https://www.udemy.com/user/samuel-reischl/   \n",
       "what_you_will_learn        ['The three building blocks of Machine Learnin...   \n",
       "description_non_formatted  <p>Do you want to <strong>master Machine Learn...   \n",
       "reviews                    [[4.0, 'El contenido ha sido claro en el proce...   \n",
       "course_level                                                      All Levels   \n",
       "small_image_link           https://i.udemycdn.com/course/96x54/2229568_3a...   \n",
       "medium_image               https://i.udemycdn.com/course/125_H/2229568_3a...   \n",
       "\n",
       "                                                                           3  \\\n",
       "title                                           Learn to Code with Python 3!   \n",
       "url                        https://www.udemy.com/course/python3-for-begin...   \n",
       "id                                                                   2371066   \n",
       "avg_rating                                                           4.11301   \n",
       "price                                                                 199.99   \n",
       "current_price                                                          11.99   \n",
       "headline                   Go from beginner to advanced with the Python p...   \n",
       "num_subscribers                                                        92051   \n",
       "num_reviews                                                             1191   \n",
       "num_lectures                                                              78   \n",
       "primary_category                                                 development   \n",
       "primary_category_link             https://www.udemy.com/courses/development/   \n",
       "primary_subcategory                                    programming-languages   \n",
       "primary_subcategory_link   https://www.udemy.com/courses/development/prog...   \n",
       "course_creation_date                                    2019-05-16T20:52:02Z   \n",
       "instructor_name                                            Joseph Delgadillo   \n",
       "instructor_Link                https://www.udemy.com/user/josephdelgadillo2/   \n",
       "what_you_will_learn        ['Introduction to Python and Setup', 'Python P...   \n",
       "description_non_formatted  <p>If you would like to learn <strong>Python 3...   \n",
       "reviews                    [[4.0, 'yes'], [3.5, 'the recording is kind of...   \n",
       "course_level                                                      All Levels   \n",
       "small_image_link           https://i.udemycdn.com/course/96x54/2371066_74...   \n",
       "medium_image               https://i.udemycdn.com/course/125_H/2371066_74...   \n",
       "\n",
       "                                                                           4  \n",
       "title                                           Mastering Visual Studio Code  \n",
       "url                        https://www.udemy.com/course/mastering-visual-...  \n",
       "id                                                                   2664746  \n",
       "avg_rating                                                           4.25806  \n",
       "price                                                                  49.99  \n",
       "current_price                                                          11.99  \n",
       "headline                   Boost your productivity by using the best IDE ...  \n",
       "num_subscribers                                                         3119  \n",
       "num_reviews                                                               31  \n",
       "num_lectures                                                              66  \n",
       "primary_category                                                 development  \n",
       "primary_category_link             https://www.udemy.com/courses/development/  \n",
       "primary_subcategory                                    other-it-and-software  \n",
       "primary_subcategory_link   https://www.udemy.com/courses/development/othe...  \n",
       "course_creation_date                                    2019-11-20T14:57:47Z  \n",
       "instructor_name                                                   Alex Horea  \n",
       "instructor_Link                        https://www.udemy.com/user/alexhorea/  \n",
       "what_you_will_learn        ['Install and configure VSCode', 'Handle Exten...  \n",
       "description_non_formatted  <p><strong>Visual Studio Code</strong> is the ...  \n",
       "reviews                    [[5.0, 'Vscode has so many features and it’s e...  \n",
       "course_level                                              Intermediate Level  \n",
       "small_image_link           https://i.udemycdn.com/course/96x54/2664746_b9...  \n",
       "medium_image               https://i.udemycdn.com/course/125_H/2664746_b9...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load in the dataset into a pandas dataframe\n",
    "courses_data = pd.read_csv('MyUdemyCoursesData.csv')\n",
    "courses_data.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "courses_data.drop(columns=['url', 'primary_category_link' , 'primary_subcategory_link' , 'instructor_name' , 'instructor_Link' ,'what_you_will_learn' ,'description_non_formatted' , 'reviews' ,'small_image_link' , 'medium_image'],inplace=True)\n",
    "courses_data.dropna(inplace=True)\n",
    "courses_data[\"paid\"] = 'paid'\n",
    "# assign false to courses with price 0 \n",
    "courses_data.loc[(courses_data.current_price == 0.0),'paid']=\"free\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "time_series = courses_data.copy()\n",
    "time_series[\"course_creation_date\"]=  pd.to_datetime(time_series[\"course_creation_date\"])\n",
    "time_series['year']= time_series.course_creation_date.dt.year\n",
    "time_series['month'] = time_series.course_creation_date.dt.month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## (Courses Creation Year Distribution)\n",
    "\n",
    "Udemy was founded at 2010 so we can see it was growing rapidly over years and its courses become famous fast "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(data = time_series[time_series['year']<2019], x = 'year', color = base_color)\n",
    "plt.title('Courses Creation Distribution Yearly')\n",
    "plt.xlabel('year (year)')\n",
    "plt.ylabel('courses Count (course)')\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(15,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ( Distribution Of paid vs free Courses)\n",
    "\n",
    "> Udemy paid courses have more features than free courses as course duration is not limited with 2 hours  also students and instructors could connect together and paid courses provide a valuable certificate at the end of the course so both instructors and students prefer paid courses over free one  ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_counts = courses_data['paid'].value_counts()\n",
    "plt.pie(sorted_counts, labels = sorted_counts.index, startangle = 90, counterclock = False,autopct='%1.2f');\n",
    "plt.title('Courses Types with percentages ')\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(20, 10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## (Courses Price Distribution )\n",
    "\n",
    "> As we mentioned udemy paid courses have more advantages and on the same times udemy prices is quite good that may explain why it had high demand courses range is very good  ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "courses_data= courses_data[courses_data[\"current_price\"]<=25]\n",
    "time_series = time_series[time_series[\"current_price\"]<=25]\n",
    "sb.countplot( data=courses_data ,x='current_price',color = base_color)\n",
    "plt.title('Courses Price Distribution ')\n",
    "plt.xlabel('price ($)')\n",
    "plt.ylabel('courses Count (course)')\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(15, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## (Course Level Distribution )\n",
    "\n",
    "> If you are an instructor you are seeking the biggest part of customer so we see that courses that for all levels come in the first place while courses that is specific to some levels comes second "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Levels_count = courses_data['course_level'].value_counts()\n",
    "Levels_order = Levels_count.index\n",
    "\n",
    "sb.countplot(data=courses_data, x='course_level', color=base_color, order=Levels_order);\n",
    "df_count = courses_data.shape[0]\n",
    "# Recalculating the type_counts just to have clarity.\n",
    "type_counts = courses_data['course_level'].value_counts()\n",
    "\n",
    "# get the current tick locations and labels\n",
    "locs, labels = plt.xticks(rotation=90) \n",
    "\n",
    "# loop through each pair of locations and labels\n",
    "for loc, label in zip(locs, labels):\n",
    "\n",
    "    # get the text property for the label to get the correct count\n",
    "    count = type_counts[label.get_text()]\n",
    "    pct_string = '{:0.1f}%'.format(100*count/df_count)\n",
    "\n",
    "    # print the annotation just below the top of the bar\n",
    "    plt.text(loc, count+2, pct_string, ha = 'center', color = 'black')\n",
    "\n",
    "plt.title('Courses Level Distribution')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('Course Level (Level)')\n",
    "plt.ylabel('courses Count (course)')\n",
    "fig = plt.gcf()\n",
    "# Change seaborn plot size\n",
    "fig.set_size_inches(20, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## (Courses average rating by color and Number of Subscribers )\n",
    "\n",
    "> Both types of udemy courses free and paid get high ratings from subscribers as they listen for customers opinions and do the best for them "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sb.FacetGrid(data = courses_data, hue = 'paid',size=10 )\n",
    "g.map(plt.scatter, 'num_subscribers', 'avg_rating')\n",
    "g.add_legend()\n",
    "plt.title('Courses average rating by color and Number of Subscribers')\n",
    "plt.xlabel('Number of Subscribers (Subscriber)')\n",
    "plt.ylabel('Course Average rate ')\n",
    "fig = plt.gcf()\n",
    "# Change seaborn plot size\n",
    "fig.set_size_inches(15, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "> Once you're ready to finish your presentation, check your output by using\n",
    "nbconvert to export the notebook and set up a server for the slides. From the\n",
    "terminal or command line, use the following expression:\n",
    "> > `jupyter nbconvert slide_deck_template.ipynb --to slides --post serve --template output_toggle`\n",
    "\n",
    "> This should open a tab in your web browser where you can scroll through your\n",
    "presentation. Sub-slides can be accessed by pressing 'down' when viewing its parent\n",
    "slide. Make sure you remove all of the quote-formatted guide notes like this one\n",
    "before you finish your presentation!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
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   "display_name": "Python 3",
   "language": "python",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
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  "latex_envs": {
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   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
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   "types_to_exclude": [
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