Python Higher Ed Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question:\n",
    "\n",
    "> Of the top 100 world ranked universities (as of 2015), does ranked quality of education at the top 25 result in higher alumni employment than the the bottom 25 universities?\n",
    "\n",
    "## Data Definitions:\n",
    "\n",
    ">__World Rank:__ 7 indicators make up a university's rank: quality of education, alumni employment, quality of faculty, research output, quality publications, influence, citations\n",
    "\n",
    "The two indicators compared in this project are:\n",
    "\n",
    ">__Quality of Education:__ measured by the number of a university's alumni who have won major international awards, prizes, and medals relative to the university's size (15%) \n",
    "\n",
    ">__Alumni Employment:__ measured by the number of a university's alumni who have held CEO positions at the world's top companies relative to the university's size (15%) \n",
    "\n",
    "## References:\n",
    "*  https://www.kaggle.com/mylesoneill/world-university-rankings <br>\n",
    "*  https://cwur.org/methodology/world-university-rankings.php"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial Thoughts/Brainstorming\n",
    "The definition of both indicators is fairly narrow. Quality of Education is based on external recognitions, and Alumni Employment looks at *very* specific types of employment -- CEOs at \"top\" companies; top companies is also not defined in the data set. Quality of Education could be defined by numerous other parameters not used here, like faculty involvement, extracurriculars, etc. Alumni employment is very specific and doesn't include more \"standard\" post-college employment in the ranking. Given that, the results of this project can't really be applied to universities beyond this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this is a variable to hold the csv data after it's read\n",
    "university_db = pd.read_csv('UniversityRanking.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this creates the SQL database\n",
    "university_db.to_sql('university_table', sqlite3.connect('university.db'), if_exists = 'replace')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic World Rankings List\n",
    "\n",
    "First, we'll start with a basic listing of the Top 25 universities and the Bottom 25 universities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>World Rank</th>\n",
       "      <th>Institution</th>\n",
       "      <th>Alumni Employment</th>\n",
       "      <th>Quality of Education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Harvard University</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Stanford University</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Massachusetts Institute of Technology</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>University of Cambridge</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>University of Oxford</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Columbia University</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>University of California, Berkeley</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>University of Chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Princeton University</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Cornell University</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Yale University</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>California Institute of Technology</td>\n",
       "      <td>328</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>University of Tokyo</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>University of Pennsylvania</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>University of California, Los Angeles</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Johns Hopkins University</td>\n",
       "      <td>84</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Kyoto University</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>New York University</td>\n",
       "      <td>22</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>University of Michigan, Ann Arbor</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Swiss Federal Institute of Technology in Zurich</td>\n",
       "      <td>64</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>University of California, San Diego</td>\n",
       "      <td>567</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Northwestern University</td>\n",
       "      <td>12</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Hebrew University of Jerusalem</td>\n",
       "      <td>164</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Seoul National University</td>\n",
       "      <td>9</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>University of Wisconsin–Madison</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    World Rank                                      Institution  \\\n",
       "0            1                               Harvard University   \n",
       "1            2                              Stanford University   \n",
       "2            3            Massachusetts Institute of Technology   \n",
       "3            4                          University of Cambridge   \n",
       "4            5                             University of Oxford   \n",
       "5            6                              Columbia University   \n",
       "6            7               University of California, Berkeley   \n",
       "7            8                            University of Chicago   \n",
       "8            9                             Princeton University   \n",
       "9           10                               Cornell University   \n",
       "10          11                                  Yale University   \n",
       "11          12               California Institute of Technology   \n",
       "12          13                              University of Tokyo   \n",
       "13          14                       University of Pennsylvania   \n",
       "14          15            University of California, Los Angeles   \n",
       "15          16                         Johns Hopkins University   \n",
       "16          17                                 Kyoto University   \n",
       "17          18                              New York University   \n",
       "18          19                University of Michigan, Ann Arbor   \n",
       "19          20  Swiss Federal Institute of Technology in Zurich   \n",
       "20          21              University of California, San Diego   \n",
       "21          22                          Northwestern University   \n",
       "22          23                   Hebrew University of Jerusalem   \n",
       "23          24                        Seoul National University   \n",
       "24          25                  University of Wisconsin–Madison   \n",
       "\n",
       "    Alumni Employment  Quality of Education  \n",
       "0                   1                     1  \n",
       "1                   2                     9  \n",
       "2                  11                     3  \n",
       "3                  10                     2  \n",
       "4                  13                     7  \n",
       "5                   6                    13  \n",
       "6                  21                     5  \n",
       "7                  14                    11  \n",
       "8                  15                     4  \n",
       "9                  18                    12  \n",
       "10                 26                    10  \n",
       "11                328                     6  \n",
       "12                  3                    16  \n",
       "13                  4                    20  \n",
       "14                 27                    28  \n",
       "15                 84                    18  \n",
       "16                 16                    22  \n",
       "17                 22                    32  \n",
       "18                 17                    24  \n",
       "19                 64                    17  \n",
       "20                567                    36  \n",
       "21                 12                   163  \n",
       "22                164                    15  \n",
       "23                  9                   367  \n",
       "24                 29                    29  "
      ]
     },
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is a variable for the SQL connection\n",
    "connection = sqlite3.connect('university.db')\n",
    "\n",
    "# selecting top 25 universities and saving them to a variable\n",
    "unitop25_df = pd.read_sql_query('SELECT world_rank AS \"World Rank\", institution AS \"Institution\", alumni_employment AS \"Alumni Employment\", quality_of_education AS \"Quality of Education\" FROM university_table WHERE world_rank < 26 AND year = 2015 ORDER BY world_rank', connection)\n",
    "unitop25_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>World Rank</th>\n",
       "      <th>Institution</th>\n",
       "      <th>Alumni Employment</th>\n",
       "      <th>Quality of Education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75</td>\n",
       "      <td>University of Texas Southwestern Medical Center</td>\n",
       "      <td>567</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76</td>\n",
       "      <td>Ruprecht Karl University of Heidelberg</td>\n",
       "      <td>402</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77</td>\n",
       "      <td>University of Notre Dame</td>\n",
       "      <td>24</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78</td>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>73</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79</td>\n",
       "      <td>Ludwig Maximilian University of Munich</td>\n",
       "      <td>129</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>80</td>\n",
       "      <td>Georgia Institute of Technology</td>\n",
       "      <td>60</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>81</td>\n",
       "      <td>University of Rochester</td>\n",
       "      <td>53</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>82</td>\n",
       "      <td>Katholieke Universiteit Leuven</td>\n",
       "      <td>202</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>83</td>\n",
       "      <td>Brown University</td>\n",
       "      <td>43</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>84</td>\n",
       "      <td>Emory University</td>\n",
       "      <td>146</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>85</td>\n",
       "      <td>Erasmus University Rotterdam</td>\n",
       "      <td>46</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>86</td>\n",
       "      <td>Tel Aviv University</td>\n",
       "      <td>107</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>87</td>\n",
       "      <td>Utrecht University</td>\n",
       "      <td>199</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>88</td>\n",
       "      <td>University of Sydney</td>\n",
       "      <td>198</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>89</td>\n",
       "      <td>University of California, Irvine</td>\n",
       "      <td>567</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>90</td>\n",
       "      <td>University of Texas MD Anderson Cancer Center</td>\n",
       "      <td>567</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>91</td>\n",
       "      <td>Swiss Federal Institute of Technology in Lausanne</td>\n",
       "      <td>74</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>92</td>\n",
       "      <td>University of Geneva</td>\n",
       "      <td>191</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>93</td>\n",
       "      <td>University of Melbourne</td>\n",
       "      <td>113</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>94</td>\n",
       "      <td>King's College London</td>\n",
       "      <td>190</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>95</td>\n",
       "      <td>Tufts University</td>\n",
       "      <td>48</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>96</td>\n",
       "      <td>University of Zurich</td>\n",
       "      <td>389</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>97</td>\n",
       "      <td>Arizona State University</td>\n",
       "      <td>455</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>98</td>\n",
       "      <td>Yonsei University</td>\n",
       "      <td>45</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>99</td>\n",
       "      <td>University of Oslo</td>\n",
       "      <td>286</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>100</td>\n",
       "      <td>Kyushu University</td>\n",
       "      <td>49</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    World Rank                                        Institution  \\\n",
       "0           75    University of Texas Southwestern Medical Center   \n",
       "1           76             Ruprecht Karl University of Heidelberg   \n",
       "2           77                           University of Notre Dame   \n",
       "3           78                                Tsinghua University   \n",
       "4           79             Ludwig Maximilian University of Munich   \n",
       "5           80                    Georgia Institute of Technology   \n",
       "6           81                            University of Rochester   \n",
       "7           82                     Katholieke Universiteit Leuven   \n",
       "8           83                                   Brown University   \n",
       "9           84                                   Emory University   \n",
       "10          85                       Erasmus University Rotterdam   \n",
       "11          86                                Tel Aviv University   \n",
       "12          87                                 Utrecht University   \n",
       "13          88                               University of Sydney   \n",
       "14          89                   University of California, Irvine   \n",
       "15          90      University of Texas MD Anderson Cancer Center   \n",
       "16          91  Swiss Federal Institute of Technology in Lausanne   \n",
       "17          92                               University of Geneva   \n",
       "18          93                            University of Melbourne   \n",
       "19          94                              King's College London   \n",
       "20          95                                   Tufts University   \n",
       "21          96                               University of Zurich   \n",
       "22          97                           Arizona State University   \n",
       "23          98                                  Yonsei University   \n",
       "24          99                                 University of Oslo   \n",
       "25         100                                  Kyushu University   \n",
       "\n",
       "    Alumni Employment  Quality of Education  \n",
       "0                 567                    59  \n",
       "1                 402                    38  \n",
       "2                  24                   221  \n",
       "3                  73                   309  \n",
       "4                 129                    42  \n",
       "5                  60                   135  \n",
       "6                  53                    72  \n",
       "7                 202                   367  \n",
       "8                  43                    64  \n",
       "9                 146                   367  \n",
       "10                 46                   367  \n",
       "11                107                    77  \n",
       "12                199                    84  \n",
       "13                198                   105  \n",
       "14                567                   227  \n",
       "15                567                   367  \n",
       "16                 74                   367  \n",
       "17                191                    35  \n",
       "18                113                   141  \n",
       "19                190                    27  \n",
       "20                 48                    60  \n",
       "21                389                   115  \n",
       "22                455                   367  \n",
       "23                 45                   367  \n",
       "24                286                    53  \n",
       "25                 49                   367  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting bottom 25 universities and saving them to a variable\n",
    "unibottom25_df = pd.read_sql_query('SELECT world_rank AS \"World Rank\", institution AS \"Institution\", alumni_employment AS \"Alumni Employment\", quality_of_education AS \"Quality of Education\" FROM university_table WHERE world_rank BETWEEN 75 AND 100 AND year = 2015 ORDER BY world_rank', connection)\n",
    "unibottom25_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Individual Comparison\n",
    "\n",
    "Note: For both indicators, the closer to 0 is a \"better\" score.\n",
    "\n",
    "Initially I compared the top and bottom universities via line chart. The first thing to notice is roughly the top 15 universities run one-to-one in terms of highest employment and highest rank (minus one outlier in California Institute of Technology).\n",
    "\n",
    "After the 20th ranked university, the comparison diverges regularly.\n",
    "\n",
    "For the bottom 25, employment and rank don't seem related at all and variable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "plt.xticks(np.arange(1, 26, step=1))\n",
    "plt.yticks(np.arange(0, 600, step=50))\n",
    "\n",
    "unitop25_df.plot(x='World Rank', y='Quality of Education', color='red', linestyle='solid', ax=ax)\n",
    "unitop25_df.plot(x='World Rank', y='Alumni Employment', color='blue', linestyle='solid', ax=ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "\n",
    "plt.xticks(np.arange(75, 101, step=1))\n",
    "plt.yticks(np.arange(0, 600, step=50))\n",
    "\n",
    "unibottom25_df.plot(x='World Rank', y='Quality of Education', color='red', linestyle='solid', ax=ax)\n",
    "unibottom25_df.plot(x='World Rank', y='Alumni Employment', color='blue', linestyle='solid', ax=ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the same data is re-plotted as a scatter plot, the lack of correlation for that bottom half becomes even more obvious."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quality of Education (blue)/Alumni Employment (orange)')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jejCNYrqOdLU/k6p4EI3kifb+mleH5lfRFB1HAz/I290uaaSkbSNiUdl9mpl1u2P22b5rK4TeylQQYyLiba18uKQhpIrl5cDXIuIOSe8HPi/pTFKciTMi4nmqAgZllWBCriDMzDqgzDDX30vaq5UPj4iVEbE3Ke7D/pL2BCYB/wDsB2xJmt0VHDDIzKyrlKkgDgJmSponabakOZJmN7OTiFhCms31bRGxKJLnge+xembXSsCgiupgQtWfdWlEjI+I8aNHj26mGGZm1oQyTUyHt/LBkkYDyyNiiaRhwJuBCyr9CnnU0jHA3LzJ9cCHJF1J6pxe6v4HM7POKRNy9FFJW5Cu7qvzP9pg022By3M/xAbAlIj4haRpufIQcDfw7zn/DaQhrg+Shrl6skAzsw4qM8z1s8C7gT+xuk8ggEOKtouI2cA+NdJrbpdHL32wUXnM+tXsKTD1XFg6H0aMgUPPhHETOl0qs35RpolpArBLRLzQ7sKYdZXZU+DnH4bl+cnXpY+ndXAlYeuFMp3Uc4GR7S6IWdeZeu7qyqFieU9KN1sPlLmDOA+YJWkuVQ/KRcRR9TcxGwSWzm8u3WyQKVNBXA5cAMwBVrW3OGZdZMSY1KxUK91sPVCmgng6Ir7c9pKYdZtDz1yzDwJg6LCUbrYeKFNBzJR0Huk5heomJs+0aoNbpSPao5hsPVWmgqgMVT2gKq3hMFezQWHcBFcItt4q86Dcwf1REDMz6y5lHpSr2eBaIh6EmZkNYGWeg/hb1WslaW6msY02krSJpDsl3SPpXknn5PSdJN0h6QFJP5G0UU7fOK8/mH/ecB9mZtY+ZZqYLqxel/RFUod1I/Uiyn0cuDgirpT0TeAUUvS4U4C/RMTLJZ1IGlr7zua+jpmZ9ZUydxC9bQrs3ChTntK7VkS5Q4BKuNHLSTO6Qoood3levgo4NM/4amZmHVCmD2IOqyfpGwKMBkr1P/SOKEea8G9JRKzIWSpR46AqolxErJC0FHgp8HSvzzwVOBVgxx13LFMMMzNrQZlhrm+vWl4BPFl1gi8UESuBvSWNBK4BXlErW34vFVEuIi4FLgUYP358UYxrMzNbB3UrCElb5sVne/1ouCQi4pmyO8lBg24lPUsxUtKGuZKpjhpXiSg3X9KGwAig9D7MzKxvFd1BzCRdwde7si/sh6gXUQ64BTgeuBI4Gbgub3J9Xv9D/vm0HCPCzMw6oG4FERE7reNn14sodx9wpaTPAbOAy3L+y4AfSnqQdOdw4jru38zM1kGZPogXSdqFdOL+p4jYsyhvQUS5h4D9a6T/HTihmfKYmVn7NBzmKmlbSR+VdCdwL6lS+ae2l8zMzDqqbgUh6d8kTQN+A4wC3gcsiohzImJOfxXQzMw6o6iJ6WukDuN3RcQMAEnuNDYzW08UVRDbkfoELpK0NTCF9DS0mZmtB+o2MUXE0xHxjYh4A3AosBRYLOl+Sf/VbyU0M7OOKOqD2LayHBHzI+KLEfFq0txJz9fbzszMBoeiJqbvStoCuBW4EbgtIlZExDzgnP4onJmZdU7Rg3KHS9oEeBNwLPBFSY+RKosbI+Kx/imimZl1QuFzEBHx94i4MSI+EhHjgU+QKpWv5uci6pK0g6Rbcp/FvZI+ktPPlrRA0t35dUTVNpNywKB5kg7rg+9nZmYtKv0ktaTNgMci4uvA1yuR4AqsAD4REXdJ2hyYKenm/LOLI+KLvT5/D9JT2q8kjaD6taTd8oywZmbWz4o6qTeQ9C5Jv5S0GJgHLMp3A5OBlxV9cEQsioi78vKzwP2sjv1Qy9HAlRHxfEQ8DDxIjSk5zMysfxQ1Md0C7AJMAraJiDERsRXweuB24HxJ/1JmJzm+9D7AHTnpQ5JmS6p0hENVwKCsOphQ9WedKmmGpBlPPfVUmd2bmVkLiiqIN0fEZyNidkSsqiRGxDMR8bOIeAfwk0Y7kPQS4GfARyNiGSn+9C7A3sAioBLzunTAoIgYHxHjR48e3Wj3ZmbWoqIH5ZZXliUdJOk9eXm0pJ1656lF0lBS5fDfEXF13ubJiFiZK51vs7oZqRIwqKI6mJCZmfWzMrO5ngV8itTUBGm6jR+V2E6kGA/3R8RFVenbVmU7Fpibl68HTpS0ca6AdgUKR0qZmVn7lBnFdCyp/6DS4bwwj0pq5EDgX4E5ku7Oaf8J/JOkvUnNR48Ap+XPvVfSFOA+0gioD3oEk5lZ55SpIF6IiKjM5JqHuzYUEbdRu1/hhoJtPg98vsznm5lZezVsYgKmSPoWMFLSvwG/JvUdmJnZINbwDiIivijpLcAyYHfgzIi4ucFmZmY2wJV6kjpXCK4UzMzWIw0rCEnPsvp5hI1Io5j+FhHD21kwMzPrrDJNTGuMWJJ0DJ4Cw8xs0CvTSb2GiLgWOKQNZTEzsy5SponpuKrVDYDx1JgCw8zMBpcyndRHVi2vID3cdnRbSmNmZl2jTB/Ee1r5YEk7AD8AtgFWAZdGxCWStiRN8jeWVNlMiIi/5Kk5LgGOAJ4D3l2ZLtzMzPpf3QpC0lcoaEqKiA83+Ox6AYPeDUyNiPMlnQGcQZrr6XDS/Eu7Aq8hzfr6mia+i5mZ9aGiO4gZ6/LBEbGINJ03EfGspErAoKNJca4BLgduJVUQRwM/iIgAbpc0UtK2+XPMzKyf1a0gIuLy6nVJw1NyPNvsTnoFDNq6ctKPiEWStsrZ6gUMWqOCkHQqcCrAjjvu2GxRzMyspDLTfY+XNAeYDcyVdI+kV5fdQY2AQXWz1khzwCAzsw4p8xzEd4EPRMTYiHgZ8EHge2U+vFbAIODJSkyI/L44pztgkJlZFylTQTwbEf9bWcnTeDdsZqoXMIgUGOjkvHwycF1V+klKDgCWuv/BzKxzikYx7ZsX78zTff+Y1OTzTlLHciP1AgadT5pC/BTgMeCE/LMbSENcHyQNc21peK2ZmfWNolFMF/ZaP6tqueGT1AUBgwAOrZE/SM1XZmbWBYpGMR3cnwUxM7PuUrcPQtK/5H6Eej/fRdJB7SmWmZl1WlET00uBuyXNBGYCTwGbAC8H3gg8TXoK2szMBqGiJqZLJH2VNLX3gcA4oAe4H/jXiHisf4poZtbFZk+BqefC0vkwYgwceiaMm9DpUvWJwsn6ImIlKdSow42a2aB37awFTL5pHguX9LDdyGFMPGx3jtln+/obzJ4CP/8wLO9J60sfT+swKCqJpgMGmZkNRtfOWsCkq+ewYEkPASxY0sOkq+dw7awF9Teaeu7qyqFieU9KHwRcQZiZAZNvmkfP8pVrpPUsX8nkm+bV32jp/ObSBxhXEGZmwMIlPU2lA6nPoZn0AabMZH0bS3qXpP+UdGblVWK770paLGluVdrZkhZIuju/jqj62SRJD0qaJ+mw1r+SmVnzths5rKl0IHVID+3186HDUvogUOYO4jpSrIYVwN+qXo18H3hbjfSLI2Lv/LoBQNIewInAK/M2X5c0pMQ+zMz6xMTDdmfY0DVPO8OGDmHiYbvX32jcBDjyyzBiB0Dp/cgvD4oOaigXk3pMRNQ60ReKiN/mOBBlHA1cGRHPAw9LehDYH/hDs/s1M2tFZbRSU6OYIFUGg6RC6K1MBfF7SXtFxJw+2ueHJJ1Eilj3iYj4Cykw0O1VeSrBgtbigEFm1i7H7LN94wphPVKmiekgUjzpeZJmS5ojaXaL+/sGsAuwNylSXGVCwFLBgsABg8zM+kuZO4jD+2pnEfFkZVnSt4Ff5FUHCzIz6zIN7yAi4lFgJHBkfo3MaU2rRJLLjgUqI5yuB07MI6Z2AnYF7mxlH2Zm1jca3kFI+gjwb0AlZOiPJF0aEV9psN2PgTcBoyTNJ8WTeJOkvUnNR48ApwFExL2SpgD3kUZLfTBP82FmZh2iFKenIEPqb3htRPwtr28G/CEixvVD+QqNHz8+ZsyY0elimJkNKJJmRsT4RvnKdFILqL6aX0n9SHFmZjZIlOmk/h5wh6Rr8voxwGXtK5KZmXWDhhVERFwk6VbScFcB74mIWe0umJmZdVbdCkLS8IhYJmlLUofyI1U/2zIinml/8czMrFOK7iCuAN5OCjda3ZOtvL5zG8tlZmYdVhRy9O35faf+K46ZmXWLMtN9Ty2TZmZmg0tRH8QmwKakB922YPXQ1uHAdv1QNjMz66CiO4jTSP0P/5DfK6/rgK81+uA6AYO2lHSzpAfy+xY5XZK+nAMGzZa077p8KTMzW3d1K4iIuCT3P3wyInaOiJ3y61UR8dUSn/191g4YdAYwNSJ2BabmdUgTAu6aX6eSZn01M7MOKvMcxFck7QnsAWxSlf6DBtvVChh0NGl+JoDLgVuBT+X0H0Sa9+N2SSMlbRsRi8p9DTMz62tlJus7i3RS3wO4gXS1fxtQWEHUsXXlpB8RiyRtldO3Bx6vylcJGLRWBeGAQWZm/aPMXEzHA4cCT0TEe4BXARv3cTkcMMjMrMuUqSB6ImIVsELScGAxrT8k92QlJkR+X5zTHTDIzKzLlKkgZkgaCXybNIrpLloP5nM9cHJePpk0IqqSflIezXQAsNT9D2Z9aPYUuHhPOHtkep89pdMlsgGgTCf1B/LiNyXdCAyPiIYxqesEDDofmCLpFOAx4ISc/QbgCOBB4DngPU1+DzOrZ/YU+PmHYXlPWl/6eFoHGDehc+WyrlcmYNCxwLSIWJrXRwJviohr+6F8hRwwyKyEi/dMlUJvI3aAj81dO90Gvb4MGHRWpXIAiIglpLsBMxsIls5vLt0sK1NB1MpTJtCQmXWDEWOaSzfLynZSXyRpF0k7S7qY1FltZgPBoWfC0GFrpg0dltLNCpSpIP4DeAH4CfBT4O/AB9tZKDPrQ+MmwJFfTn0OKL0f+eWB2UHt0Vj9qmEndTdzJ7XZeqT3aCxId0IDtbLroLKd1GWm2riFGk81R8QhLZbNzKx5U89ds3KAtD71XFcQbVKms/mTVcubAO8AVrSnOGZmdXg0Vr8r86Bc7w7p30n6TZvKY2ZW24gxdZ7n8GisdikTcnTLqtcoSYcB26zLTiU9ImmOpLslzajaz1rBhMzMAI/G6oAyTUwzSX0QIjUtPQyc0gf7Pjginq5arwQTOl/SGXn9U32wHzMbDCr9DFPPTc1KI8akysH9D21Tpolpp/4oCPWDCZmZJeMmuELoR3UrCEnHFW0YEVevw34D+JWkAL4VEZdSP5hQ73I5YJCZWT8ouoM4Mr9vBbwOmJbXDyZd3a9LBXFgRCzMlcDNkv5YdsNcmVwK6TmIdSiDmZkVqFtB5OhxSPoFsEfl6j4H+vnauuw0Ihbm98WSrgH2JwcTyncP1cGEzMxacu2sBUy+aR4Ll/Sw3chhTDxsd47ZZ/tOF2vAKDPVxthewXueBHZrdYeSNpO0eWUZeCswl/rBhMxsALh21gIOPH8aO53xSw48fxrXzlrQ8fJMunoOC5b0EMCCJT1MunpOx8s1kJQZxXSrpJuAH5P6Dk4EblmHfW4NXCOpsv8rIuJGSdOpHUzIzLpc5WTcs3wlsPpkDHTsin3yTfNeLE9Fz/KVTL5p3oC7i5h+/bfY4a7JbBVPsVijeXzfiex31Glt32+ZUUwfykGD3pCTLo2Ia1rdYUQ8BLyqRvqfgUNb/VyzUmZPGfDDJDt1sijSjSfjhUt6mkrvVtOv/xZ7zvwMw/QCCLbhKUbM/AzToe3HvWgU0z9ERKXz+IbqSkHSARFxe1tLZtbXBkHozU6eLIq0cjJud//AdiOHsaDG/rcbOaxG7u61w12T0/GuMkwvsMNdk6HNx7yoD+KKquU/9PrZ19tQFutmg2Ga5aLJ3jqombb7wpNFh8oE6aR71Aa3cdtGH+ahjd/FbRt9mKM2uK3uybg/+gcmHrY7w4YOWSNt2NAhTDxs9z7bR3/YKp6qk/50zfS+VNTEpDrLtdZtMOvWK+9mm4u6cLK3Ztvut4qnav73NTpZNHO13kp/wpf2eIA9Z37nxcprjJ7mgqHfYe4eY4G1J36efNM83rLyN5y+0RS209MsjFF8YcUEJt+0UeFdRDPfo5I+0EcxLdZotmHtSmKxRq3bnEclFFUQUWe51roNZq1Ms9xKW38z27RSafXTZG/NnMSabbtv5WTR7Am/lZP3fn/6CtS4s9nvT18B1m5xS7DYAAASs0lEQVQGGb/sZs4b+h02rapQzh/6HSYtg1oVSivfo5Le7gqh3U1lj+87kRGVZsWsJzbi8VdPbHsFUdTENEbSlyV9pWq5sj6wquDBroXmn6aaEJq98q6cvJc+DsTqk3dRuZrdppXmon6Y7K3ZppNm2+4f33ciPbHRGmk9sRGP7zuxbpmKKqFaxi+7mfOHfocxGzzNBoIxG6ST9/hlN9fdR7N/I5M2+umLlUPFpnqBSRv9tM++R0ua/F/qj6ay/Y46jbmv/hxPMJpVIZ5gNHNf/bmOj2Kq/ovrHbZtvQ3j1vTVQhdeSTd9JdbslXcrdxxNbhNL59ds56yXDsC4CUx/5C95BNDTLNYoHt9rIvv1YTNZs3cEzXak7nfUaUyHNb/Dq4tHMTVbCU3a6KdsSr2T93m1d9Lk38jW1G4Sq5deVN4+G5XUwv9SS6O3Wjgn7HfUaS92SG/DOk6n3YSiJ6kv76cyDBhNn1hbaQZpdpsWTsZNNyEcembtUI91rrxbOnk3eQX6JKNqNrWk9NqunbWASdNfRs/yS15MGzZ9COftsKCwPb6ZC4JmT2ITD9t9jb8paNyR2uzJotlKqJWTd7N/I6pToaigua/to5Ja+F9qutLq1v68Oso8SW1Z07e4rTSDNLlN1DmB1kuHFpoQxk1g+l7nrHGLO32vc+r+QT/JqKbSgfp3I3XSz3vhBJ7r1dTyXGzEeS/Uf76y2ePXSvNBs6N5jtlne36w36PcvslHeGjjd3H7Jh/hB/s92qdt2M2O5ql3ki46eTNuQooNPWIHQOm9KFZ0C819bR+V1MIghnrHtW6l1aUj6erpugpC0tskzZP0YI4L0TbTr/8WT5z9cladNYInzn4506//VmH+hUt6av7z17taaOXk3cqVdDPp0Hz777WzFnDS9JdxwN8vYefn/5sD/n4JJ01/Wd0TZSsn7+m7/EfNtvXpu/xHzfwzhr+FM5a/j/mrRrEqxPxVozhj+fuYMfwtdffR7NVeK23eX9rjAS7oVfleMPQ7fGmPB2pvMHsK+805i214ig3ycw37zTmrT4cRH7PP9px33F5sP3IYArYfOYzzjturfiXUal/NuAnwsblw9pL0XnRF3GyF0sr3aFaTFynQQqXVhSPpihQ9KHdBRHxK0gkRUb/nqA9JGkKaCPAtwHxguqTrI+K+vt5XKw8cnfySOzl9+dojL7YcuhHwj2vlb6UZ5Llh27Bpz6La6TXyn/fCCWuMBoF8Ml5+ApfUyA/NNyE02846Y/hbOGMZnL7hFLbTn1kYL+ULKyYws+Dk/dH7duXVy9+39jb37crvjlo7f2qaeYHrXzjoxbRhQ4dwXsHVZLNNFK20eTc7mqel/poWNDWap78C87QQ26Gto5KabCarlAeaGEo7wMKmFnVSHyHpM8AkoF8qCNKsrg/m6TiQdCUpkFCfVxCtPJ14+tCfsOmKta+8Tx/6E+CctfK3cvL+wvJ3cnp8fa1tvrD8nZxdI38rJ+Nm239ba1dv7uS9cEkPCzhojW0AVGcfrYxxb7a9v6U272avELv1inJ9DMzTYsXYVKXVQiXUSUUVxI3A08BmkpaRHs+phB6NiBjehvJsD1SfueYDr6nO0FcBg1p54GjTnieaSm/l5H35X/fnmQ1eWGubnz+/f80KopWTcbN/pM2eKFs5ebdyMm72arLZcrXSgdz0FeIAu6Ic9NpdMQ6wsKlFo5gmAhMlXRcRR/dTeWoOflljpY8CBrX0dGKT/8ytNoNcv2TtK+nt+/Bk3OwfaSsnymZP3i2djFvQTLla+t02e4U4wK4orQ8MoLuzMrO5Hi1pa2C/nHRHRJ3JQdbdfGCHqvUxwMJ27KilpxOb/Gfuj2aQyn6abpdt4o+0P6Ys6NZpEZr+3TZ7hTjAriht/aKI4otwSScAXySFGRXwemBiRFzV54WRNgT+jzTt9wJgOvCuiLi3Vv7x48fHjBmtP7O3etrk/MBRmWmT+2G6aEfBMrN2kjQzIsY3zFeigrgHeEtELM7ro4FfR8RaMR36gqQjgC8BQ4DvRsTn6+Vd1wrCzGx9VLaCKBNRboNK5ZD9mTY+PxERNwA3tOvzzcysnDIVxI1VIUcB3olP4GZmg16ZTuqJko4DDiL1QaxTyFEzMxsYytxBEBFXA1e3uSxmZtZFum4uJjMz6w6uIMzMrKaGFYSkt0tyRWJmtp4p8xzEj4DXAj8DvhcR9/dHwcqQ9BTwaB981CgoiobSJ9usr/voxjINln10Y5kGyz66sUytblPLyyJidMNcEdHwBQwnzVV8O/AH0mR5m5fZdiC8gBnt3mZ93Uc3lmmw7KMbyzRY9tGNZWp1m3V5lWo6iohlpDuIK4FtgWOBuyTVjuZiZmYDXpk+iKMkXQNMA4YC+0fE4cCrgE+2uXxmZtYhZZ6DOB64OCJ+W50YEc9Jem97itXvLu2HbdbXfXRjmQbLPrqxTINlH91Ypla3aVmZTuoLIuJTjdLMzGxwKdMHUSv82eF9XRAzM+sudZuYJL0f+ACwi6TZVT/aHPhduwtmZmYdVjCcagQwljSL68uqXlv25zCrdr6A7wKLgbkl8+8A3ALcD9wLfKTENpsAdwL35G3OKbmvIcAs4Bcl8z8CzAHupsRQOGAkcBXwx/x9Xtsg/+75syuvZcBHG2zzsfyd5+a/o00a5P9Izntvvc+udcyALYGbgQfy+xYN8p+Q97EKGF9yH5Pz72o2cA0wskH+z+a8dwO/ArYr+7dHGvwRwKgG+zibFFirckyOKLMP4D+Aefl38IUG+/hJ1ec/Atxd4ne1N2lI/N3ADNLAlqL8ryINn58D/BwY3uh/rt4xL8hf95gXbFPzmBfkr3vM621T75gX7KPwmPf1q+ifdXjVgVjr1c5C9dcLeAOwb+9/oIL82wL75uXNSdHv9miwjYCX5OWhwB3AASX29XHgCpqrIEaVyZvzXw68Ly9vRNUJr8S2Q4AnSA/b1MuzPfAwMCyvTwHeXZB/T1LlsCnpzvbXwK5ljhnwBeCMvHwGcEGD/K8gVXi39j5ZFGzzVmDDvHxBiX1Un+Q+DHyzzN9ePjHcRHoAdFSDfZwNfLKZv2/g4Py73Tivb1X2/wG4EDizxD5+BRyel48Abm2Qfzrwxrz8XuCzjf7n6h3zgvx1j3nBNjWPeUH+use83jb1jnnBPgqPeV+/ivogrsjvM0lXATOrXoMijFukkVnPNJF/UUTclZefJdXuhbFAI/lrXh2aX4UjAySNAf4R+E7ZsjVD0nDSP+pluYwvRMSSJj7iUOBPEdHoKfYNgWE5lOymFMcXfwVwe0Q8FxErgN+QnrdZQ51jdjSpwiO/H1OUPyLuj4h59QpSZ5tf5XJBujoe0yD/sqrVzeh1zAv+9i4GTm8if111tnk/cH5EPJ/zLG6QHwBJAiawOi5M0TZBergWUkvEwgb5dwcqoyRvBt5Rlb/e/1zNY14vf9ExL9im5jEvyF/3mDc4d6x1zFs517RD3QoiIt6e33eKiJ3ze+W1c/8VsTtJGgvsQ7ojaJR3iKS7SbfWN0dEo22+RPqDWdVEkQL4laSZkk5tkHdn4Cnge5JmSfqOpM2a2NeJ9DpRrFWYiAWkWOaPAYuApRHxq4JN5gJvkPRSSZuSrjx3KFmerSNiUd7vImCrktu16r3A/zTKJOnzkh4H/hk4s0T+o4AFEXFPE2X5kKTZkr4raYsS+XcDXi/pDkm/kbRfyf28HngyIh4okfejwOT83b8ITGqQfy5wVF4+gTrHvdf/XMNj3sz/aIltah7z3vnLHPPqbcoc8xplavaYt6xuBSFp36JXOwvV7SS9hPRk+Ud7XTXUFBErI2Jv0hXI/pL2LPjstwOLI2Jmk8U6MCL2JY0w+6CkNxTk3ZB0m/+NiNgH+BvpNr0hSRuR/pl/2iDfFqSrvJ2A7YDNJP1LvfyR5vi6gHQFeSOpz2ZFvfydIunTpHL9d6O8EfHpiNgh5/1Qg8/dFPg0JSqSKt8AdiG1+S8iNQE1siGwBXAAMBGYku8OGvknGlwUVHk/8LH83T9GvlMt8F7S3+xMUnPKC70zNPs/12z+om3qHfNa+Rsd8+pt8mcWHvMa+2jlmLeuXtsTqYOk3mtaf7WBtftF6ogv1QeR8w8ltRd+vMX9nUVxu/F5wHxSn8ITwHPAj5rcx9kN9rEN8EjV+uuBX5b87KOBX5XIdwJwWdX6ScDXm/gO/wV8oMwxI3W4bpuXtwXmlTnG1OmDqLcNcDKpM3XTZv6OSIM7au3/xW2AvUh3mI/k1wrS3dc2JfdR7zv2/l3dCLypav1PwOgG33tD4ElgTMnjsZTVz1gJWNbE72o34M5eaWv9zxUd81r5Gx3zetvUO+ZF+6h3zHtv0+iYl9hH3d9jX72KmpgOLngdUm+7wSxfaV0G3B8RF5XcZrSkkXl5GPBm0siImiJiUkSMiYixpKacaRFR98o7f+5mkjavLJM61+YW7OMJ4HFJu+ekQ4H7ynwfyl9JPgYcIGnT/Hs7lNSOWpekrfL7jsBxJfcDcD3pH5n8fl3J7UqT9DbgU8BREfFcify7Vq0eRcExB4iIORGxVUSMzcd+PqmT8omCfWxbtXosBce8yrXAIXn73UgDFBrNDvpm4I8RMb/E50Pqc3hjXj6ENNKorqrjvgHwGeCbVT+r9z9X85i3+D9ac5t6x7wgf91jXmubomNesI9WjnnrytQipBEmE0hXgScBJ7Wz1uqvF+kEtAhYng/OKQ3yH0Rq668MZWs4zAwYRxquOjsfzDObKN+bKDGKidSncA+rh9J+usQ2e5MGG8wmnTS2KLHNpsCfgREly38O6Z9kLvBD8siZgvz/S6qo7gEOLXvMgJcCU0knoqlUjbKrk//YvPw86cr4phL7eBB4vOq4f7NB/p/l7z2bNHRz+2b+9ug1Kq3OPn5IGho6m3TC3LbE99gI+FEu213AIY3KBHwf+PcmjsdBpMEs95DazV/dIP9HSKN0/g84n3z3UfQ/V++YF+Sve8wLtql5zAvy1z3m9bapd8wL9lF4zPv6VWaqjbNIJ6o9gBtIbdy3RcTxhRuamdmAVmaqjeNJzQNPRMR7SA+1bNzWUpmZWceVqSB6ImIVsCKPn19MatIwM7NBrMx03zNyJ+u3Se2KfyVNHWFmZoNYwz6INTKnBzaGR8TsBlnNzGyAK9NJXfOBq+gVQMjMzAaXMhXEz6tWNwH2B2bGevoshA1Mki4GHo2IL+X1m4DHI+J9ef1C0pQHpcbO523+GhEvqZH+fdLw5KtqpL+R/CAZ6QGoqS1+n3eTHvgqfELbbF007KSOiCOrXm8hPRPxZPuLZtanfg+8Dl58IGsU8Mqqn7+OknFOJA1Zh3JMjDTtykepeiDMrBuVGcXU23xSJWE2kPyOXEGQKoa5wLOStpC0MWk22VlKJkuaK2mOpHcCSHqTpFskXUF6UOlFeZuvSrpP0i8pN1ngH6ianVPSmZKm5/1eWpkfSdKtki6QdKek/5P0+t4fJOkfJf1B0qjmfy1m9TUcxSTpK6yehnYD0hO4zcw2adZxEbFQ0oo8jcfrWH2Cfi2pyWd2RLwg6R2kv/FXke4ypkuq9LftD+wZEQ/3+vhjSVNW7wVsTXoa/LsNivQ20hPsFV+NiHMBJP0QeDvpaVxIMQn2l3QEaS6vN1c2knQsKXbIERHxl3K/DbNySg1zrVpeAfw4Ihxy1Aaiyl3E64CLSBXE60gVxO9znoNIf+MrgScl/QbYjxRB784alQOk2BqVbRZKmlZQhsmSvkC6yzigKv1gSaeTpjPZkjRlSqWCuDq/zyRN0PbiNsB44K1RcsZSs2aUaWL6KWkuoVnAVa4cbACr9EPsRWpiup10B1Hd/1A09fXfCn5Wdrz4RODlpEnpLgeQtAnwdeD4iNiL9MzRJlXbPJ/fV7LmRd1DpOmxdyu5b7OmFMWDGCrpS6TJqr5H+mN+SNIZ+ef79E8RzfrM70hNN89EitHxDCk292tJTU6QIpu9UynI02jS3UGjB0N/C5yYt9mWdGVfV56Z4BJgA0mHsboyeDrP/192nrNHSbPe/kDSKxtlNmtW0R3EhcBLgLER8epIgWVeAews6Rusvu01GyjmkPoVbu+VtjQiKlNeX0OaKfMeYBpwehRMuV21zQP5s75BCpdaKNL48s/lz19CumuYQ+qXmF72C0UKo/nPwE8l7VJ2O7My6j4HIelBUtD46JU+hDR//OERcXvNjc3MbMAruoNY1btygBQ+E3jKlYOZ2eBWVEHcJ+mk3ok5rnBhZDAzMxv4ipqYtif1M/SQhtcFabjfMODYiFjQX4U0M7P+V2YupkNIT54KuLfVuWPMzGxgaWq6bzMzW3+0MheTmZmtB1xBmJlZTa4gzMysJlcQZmZWkysIMzOr6f8Dw+Vfv1zxtIIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(np.arange(1, 26, step=1))\n",
    "plt.yticks(np.arange(0, 600, step=50))\n",
    "\n",
    "plt.scatter(unitop25_df['World Rank'], unitop25_df['Quality of Education'])\n",
    "plt.scatter(unitop25_df['World Rank'], unitop25_df['Alumni Employment'])\n",
    "\n",
    "plt.xlabel(\"World Rank\", fontsize=10)\n",
    "plt.ylabel(\"Quality of Education (blue)/Alumni Employment (orange)\", fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Quality of Education (blue)/Alumni Employment (orange)')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(np.arange(75, 101, step=1))\n",
    "plt.yticks(np.arange(0, 600, step=50))\n",
    "\n",
    "plt.scatter(unibottom25_df['World Rank'], unibottom25_df['Quality of Education'])\n",
    "plt.scatter(unibottom25_df['World Rank'], unibottom25_df['Alumni Employment'])\n",
    "\n",
    "plt.xlabel(\"World Rank\", fontsize=10)\n",
    "plt.ylabel(\"Quality of Education (blue)/Alumni Employment (orange)\", fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Quality of Education and Alumi Employment are indeed higher for the top ranked university, but this seems to only apply for roughly the top 15 or so universities. Beyond those, the two indicators don't look obviously correlated and could be influenced by other outside factors. Is Alumni Employment also influenced by location, general university name recognition, or other factors? While the hypothesis is interesting, I think the question needs more outside data to compare to and to account for other variables influencing post-grad employment.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}