diff --git a/cohort_data.ipynb b/cohort_data.ipynb
new file mode 100644
index 0000000..d5d1765
--- /dev/null
+++ b/cohort_data.ipynb
@@ -0,0 +1,808 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:9527bd5df2a3419c742c609dff1b891167d5e533dd44cab60cebd298de75b7e1"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as ply\n",
+      "import seaborn as sbn\n",
+      "import numpy as np\n",
+      "\n",
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 40
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "cohort_rails = pd.ExcelFile (\"cohort_3_rails.xlsx\")\n",
+      "ru_lec = cohort_rails.parse('Lecture Score')\n",
+      "ru_hw = cohort_rails.parse('HW Score')\n",
+      "\n",
+      "ru_lec.drop(['Unnamed: 13', 'Unnamed: 14', 'Unnamed: 15', 'Week 5', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19', 'Week 6', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23', 'Week 7', 'Unnamed: 25', 'Unnamed: 26', 'Unnamed: 27', 'Week 8', 'Unnamed: 29', 'Unnamed: 30', 'Unnamed: 31', 'Week 9', 'Unnamed: 33', 'Unnamed: 34', 'Unnamed: 35'],inplace=True,axis=1)\n",
+      "ru_hw.drop(['Unnamed: 13', 'Unnamed: 14', 'Unnamed: 15', 'Week 5', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19', 'Week 6', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23', 'Week 7', 'Unnamed: 25', 'Unnamed: 26', 'Unnamed: 27', 'Week 8', 'Unnamed: 29', 'Unnamed: 30', 'Unnamed: 31', 'Week 9', 'Unnamed: 33', 'Unnamed: 34', 'Unnamed: 35'],inplace=True,axis=1)\n",
+      "ru_lec.columns = ['lec1', 'lec2', 'lec3', 'lec4', 'lec5', 'lec6', 'lec7', 'lec8', 'lec9', 'lec10', 'lec11', 'lec12', 'lec13']\n",
+      "ru_hw.columns = ['hw1', 'hw2', 'hw3', 'hw4', 'hw5', 'hw6', 'hw7', 'hw8', 'hw9', 'hw10', 'hw11', 'hw12', 'hw13']\n",
+      "\n",
+      "ru_combined = ru_lec.join(ru_hw)\n",
+      "ru_combined = ru_combined[pd.notnull(ru_combined['hw9'])]\n",
+      "ru_combined = ru_combined.ix[1:-2]\n",
+      "ru_combined = ru_combined.dropna(axis=1,how='all')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 53
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "cohort_python = pd.read_csv(\"cohort_3_python.csv\")\n",
+      "cohort_python.columns = ['Name', 'lec1', 'hw1', 'lec2', 'hw2', 'lec3', 'hw3', 'lec4', 'hw4', 'lec5', 'hw5', 'lec6', 'hw6', 'lec7', 'lec8', 'hw8', 'lec9', 'hw9', 'lec10', 'hw10', 'lec11', 'hw11', 'lec12', 'hw12', 'lec13']\n",
+      "cohort_python.drop(['lec13'],inplace=True,axis=1)\n",
+      "cohort_python = cohort_python.dropna(axis=1,how='all')\n",
+      "cohort_python = cohort_python.dropna(axis=0,how='all')\n",
+      "cohort_python = cohort_python.set_index('Name')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 107
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Deliverables"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Mean difficulty for lectures per day, per class"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "py_lectures = [col for col in cohort_python.columns if 'lec' in col]\n",
+      "py_homework = [col for col in cohort_python.columns if 'hw' in col]\n",
+      "\n",
+      "ru_lectures = [col for col in ru_combined.columns if 'lec' in col]\n",
+      "ru_homework = [col for col in ru_combined.columns if 'hw' in col]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 86
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "py_lec_mean = cohort_python[py_lectures][:14].mean(axis=0)\n",
+      "ru_lec_mean = ru_combined[ru_lectures][:14].mean(axis=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 85
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ply.plot(py_lec_mean, c='red', label='Python')\n",
+      "ply.plot(ru_lec_mean, c='blue', label='Ruby')\n",
+      "ply.xlabel('day')\n",
+      "ply.ylabel('mean scoring per student')\n",
+      "ply.title('Mean difficulty for lectures per day, per class')\n",
+      "ply.legend(loc=4)\n",
+      "ply.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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FLmrU0Jk61ccjj3iZPNnDVVelMHNmDtWqlaPNRMyJpOukvPgcYCi1yuS55zys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\nrdP6AAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x111c5b080>"
+       ]
+      }
+     ],
+     "prompt_number": 87
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Mean difficulty for lectures per week, per class"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "py_lec_week1_mean = py_lec_mean[0:4].mean(axis=0)\n",
+      "py_lec_week2_mean = py_lec_mean[4:8].mean(axis=0)\n",
+      "py_lec_week3_mean = py_lec_mean[8:12].mean(axis=0)\n",
+      "py_lec_week4_mean = py_lec_mean[12:].mean(axis=0)\n",
+      "py_lec_weekly_mean = [py_lec_week1_mean, py_lec_week2_mean, py_lec_week3_mean, py_lec_week4_mean]\n",
+      "\n",
+      "ru_lec_week1_mean = ru_lec_mean[0:4].mean(axis=0)\n",
+      "ru_lec_week2_mean = ru_lec_mean[4:8].mean(axis=0)\n",
+      "ru_lec_week3_mean = ru_lec_mean[8:12].mean(axis=0)\n",
+      "ru_lec_week4_mean = ru_lec_mean[12:].mean(axis=0)\n",
+      "ru_lec_weekly_mean = [ru_lec_week1_mean, ru_lec_week2_mean, ru_lec_week3_mean, ru_lec_week4_mean]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 88
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ply.plot(py_lec_weekly_mean, c='red', label='Python')\n",
+      "ply.plot(ru_lec_weekly_mean, c='blue', label='Ruby')\n",
+      "ply.xlabel('Week')\n",
+      "ply.ylabel('mean scoring per student')\n",
+      "ply.title('Mean difficulty for lectures per week, per class')\n",
+      "ply.legend(loc=4)\n",
+      "ply.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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P9zrK5iOW3krLROR9bJtDAjDCGLMp7pGpBsGXm0vmVX0JfryOkp59yH9mAiQm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\n59t8ouYHhlwAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x111c71eb8>"
+       ]
+      }
+     ],
+     "prompt_number": 89
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Mean difficulty for homework per day, per class"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "py_hw_mean = cohort_python[py_homework][:14].mean(axis=0)\n",
+      "ru_hw_mean = ru_combined[ru_homework][:14].mean(axis=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 91
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ply.plot(py_hw_mean, c='red', label='Python')\n",
+      "ply.plot(ru_hw_mean, c='blue', label='Ruby')\n",
+      "ply.xlabel('day')\n",
+      "ply.ylabel('mean scoring per student')\n",
+      "ply.title('Mean difficulty for homework per day, per class')\n",
+      "ply.legend(loc=4)\n",
+      "ply.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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ct9Xj7NbdJPTsQuRHRgHF5DkLgl6bWHFpePNo1wRjxBGe/6cxXEiKYorLBaNH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\nRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x111adc470>"
+       ]
+      }
+     ],
+     "prompt_number": 92
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Mean difficulty for homework per week, per class"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "py_hw_week1_mean = py_hw_mean[0:4].mean(axis=0)\n",
+      "py_hw_week2_mean = py_hw_mean[4:8].mean(axis=0)\n",
+      "py_hw_week3_mean = py_hw_mean[8:12].mean(axis=0)\n",
+      "py_hw_week4_mean = py_hw_mean[12:].mean(axis=0)\n",
+      "py_hw_weekly_mean = [py_hw_week1_mean, py_hw_week2_mean, py_hw_week3_mean, py_hw_week4_mean]\n",
+      "\n",
+      "ru_hw_week1_mean = ru_hw_mean[0:4].mean(axis=0)\n",
+      "ru_hw_week2_mean = ru_hw_mean[4:8].mean(axis=0)\n",
+      "ru_hw_week3_mean = ru_hw_mean[8:12].mean(axis=0)\n",
+      "ru_hw_week4_mean = ru_hw_mean[12:].mean(axis=0)\n",
+      "ru_hw_weekly_mean = [ru_hw_week1_mean, ru_hw_week2_mean, ru_hw_week3_mean, ru_hw_week4_mean]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 93
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ply.plot(py_hw_weekly_mean, c='red', label='Python')\n",
+      "ply.plot(ru_hw_weekly_mean, c='blue', label='Ruby')\n",
+      "ply.xlabel('Week')\n",
+      "ply.ylabel('mean scoring per student')\n",
+      "ply.title('Mean difficulty for lectures per week, per class')\n",
+      "ply.legend(loc=4)\n",
+      "ply.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
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vJfuRCQQiEcoPOIiiG28l2qVrskMzpsmI5WqlySIyF3c5awgYpqpLEx6ZMfUV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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x111702c50>"
+       ]
+      }
+     ],
+     "prompt_number": 94
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Mean difficulty for lectures and homework per week day. Is there a trend, like Mondays are easier?"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ru_combined.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>lec1</th>\n",
+        "      <th>lec2</th>\n",
+        "      <th>lec3</th>\n",
+        "      <th>lec4</th>\n",
+        "      <th>lec6</th>\n",
+        "      <th>lec7</th>\n",
+        "      <th>lec8</th>\n",
+        "      <th>lec9</th>\n",
+        "      <th>lec10</th>\n",
+        "      <th>lec11</th>\n",
+        "      <th>...</th>\n",
+        "      <th>hw2</th>\n",
+        "      <th>hw3</th>\n",
+        "      <th>hw6</th>\n",
+        "      <th>hw7</th>\n",
+        "      <th>hw8</th>\n",
+        "      <th>hw9</th>\n",
+        "      <th>hw10</th>\n",
+        "      <th>hw11</th>\n",
+        "      <th>hw12</th>\n",
+        "      <th>hw13</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>R01</th>\n",
+        "      <td> 2</td>\n",
+        "      <td>   2</td>\n",
+        "      <td>   4</td>\n",
+        "      <td>   3</td>\n",
+        "      <td>   3</td>\n",
+        "      <td>   5</td>\n",
+        "      <td>   2</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>   4</td>\n",
+        "      <td>...</td>\n",
+        "      <td>   3</td>\n",
+        "      <td>   3</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3</td>\n",
+        "      <td>   3</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>R02</th>\n",
+        "      <td> 3</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 6</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>   5</td>\n",
+        "      <td>...</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 2.5</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>   4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>R03</th>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td>   6</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>...</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 5.5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>   3</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>R04</th>\n",
+        "      <td> 2</td>\n",
+        "      <td>   4</td>\n",
+        "      <td>   4</td>\n",
+        "      <td>   4</td>\n",
+        "      <td>   5</td>\n",
+        "      <td>   5</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>   6</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>   3</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>   4</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>   6</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>   4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>R05</th>\n",
+        "      <td> 2</td>\n",
+        "      <td>   3</td>\n",
+        "      <td>   5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>   3</td>\n",
+        "      <td>   4</td>\n",
+        "      <td>   3</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 3</td>\n",
+        "      <td>   3</td>\n",
+        "      <td>...</td>\n",
+        "      <td>   5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 3</td>\n",
+        "      <td>   3</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3</td>\n",
+        "      <td>   5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.5</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "<p>5 rows \u00d7 23 columns</p>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 101,
+       "text": [
+        "    lec1 lec2 lec3 lec4 lec6 lec7 lec8 lec9 lec10 lec11 ...   hw2  hw3 hw6  \\\n",
+        "R01    2    2    4    3    3    5    2    3     4     4 ...     3    3   4   \n",
+        "R02    3  3.5  4.5    4  4.5  4.5  3.5    6     4     5 ...     4  4.5   4   \n",
+        "R03    3  4.5    4  3.5    6  4.5    4    5     5   4.5 ...     4  5.5   5   \n",
+        "R04    2    4    4    4    5    5    4    5     4     6 ...   4.5    3   4   \n",
+        "R05    2    3    5  4.5    3    4    3    5     3     3 ...     5  4.5   5   \n",
+        "\n",
+        "    hw7  hw8  hw9 hw10 hw11 hw12 hw13  \n",
+        "R01   3    3    4    3  4.5    4  4.5  \n",
+        "R02   4    4  2.5    3  4.5    4    4  \n",
+        "R03   4  4.5    4    4    3    4  3.5  \n",
+        "R04   4    4    4    5    6    5    4  \n",
+        "R05   3    3  4.5    3    5    4  3.5  \n",
+        "\n",
+        "[5 rows x 23 columns]"
+       ]
+      }
+     ],
+     "prompt_number": 101
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "cohort_python.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>lec1</th>\n",
+        "      <th>hw1</th>\n",
+        "      <th>lec2</th>\n",
+        "      <th>hw2</th>\n",
+        "      <th>lec3</th>\n",
+        "      <th>hw3</th>\n",
+        "      <th>lec4</th>\n",
+        "      <th>hw4</th>\n",
+        "      <th>lec5</th>\n",
+        "      <th>hw5</th>\n",
+        "      <th>...</th>\n",
+        "      <th>lec8</th>\n",
+        "      <th>hw8</th>\n",
+        "      <th>lec9</th>\n",
+        "      <th>hw9</th>\n",
+        "      <th>lec10</th>\n",
+        "      <th>hw10</th>\n",
+        "      <th>lec11</th>\n",
+        "      <th>hw11</th>\n",
+        "      <th>lec12</th>\n",
+        "      <th>hw12</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Name</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>P01</th>\n",
+        "      <td>  3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 5.5</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>P02</th>\n",
+        "      <td>  4</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>...</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>P03</th>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  6</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>P04</th>\n",
+        "      <td>  3</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  1</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  1</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>P05</th>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  6</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "<p>5 rows \u00d7 23 columns</p>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 106,
+       "text": [
+        "      lec1  hw1  lec2  hw2  lec3  hw3  lec4  hw4  lec5  hw5  ...   lec8  hw8  \\\n",
+        "Name                                                         ...               \n",
+        "P01      3  4.0     3    4     4  5.0   5.0    5     4    4  ...    5.5  NaN   \n",
+        "P02      4  3.5     3    5     4  4.5   4.5    5     5    5  ...    NaN    5   \n",
+        "P03    NaN  5.0     3    4     5  5.0   5.0    5     5    5  ...    5.0    6   \n",
+        "P04      3  3.0     2    3     4  4.0   4.0    4     5  NaN  ...    4.0  NaN   \n",
+        "P05    NaN  3.0     3    3     3  4.0   4.0    4     5    4  ...    4.0    4   \n",
+        "\n",
+        "      lec9  hw9  lec10  hw10  lec11  hw11  lec12  hw12  \n",
+        "Name                                                    \n",
+        "P01      4    5    NaN   NaN    NaN   NaN    NaN   NaN  \n",
+        "P02      5    5    NaN   NaN      5     5      5   NaN  \n",
+        "P03    NaN  NaN      5     5      5   NaN    NaN   NaN  \n",
+        "P04      1    3      1     3      5     5      5     5  \n",
+        "P05      3    6    NaN   NaN    NaN   NaN    NaN   NaN  \n",
+        "\n",
+        "[5 rows x 23 columns]"
+       ]
+      }
+     ],
+     "prompt_number": 106
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 94
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 94
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 94
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 94
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 94
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Choose a sample student from each class and graph their data. What's their story? Are they getting better over time?"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_student = ru_lec[6:7]\n",
+      "python_student = cohort_python[7:8]\n",
+      "\n",
+      "py_lectures = [col for col in cohort_python.columns if 'lec' in col]\n",
+      "rub_lect_col = [col for col in ru_lec.columns if 'lec' in col]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 95
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "python_student[py_lectures].max().plot(c='red', label='Python Student')\n",
+      "ruby_student[rub_lect_col].max().plot(c='blue', label='Ruby Student')\n",
+      "ply.xlabel('Day')\n",
+      "ply.ylabel('mean lecture scoring per student')\n",
+      "ply.title('Difficulty for 1 student per class')\n",
+      "ply.legend(loc=4)\n",
+      "ply.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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RduE7TP3CSxBaBrffnrroAlpHOqo+a5wGbRnCv5h9ybOmZfjEzxKtxwKjgIk4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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x11191ada0>"
+       ]
+      }
+     ],
+     "prompt_number": 96
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The ruby student is maintaining an average pace in regards to how they view lectures. The python student on the other hand is progressively getting more and more challenged in lectures every day. They have a steady growth into pain. As seen in week1/week2, the Ruby had one rough day, but otherwise has hovered between 4 and 4.5."
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 1ac83fd..98261ae 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,2 +1,4 @@
 pandas
 xlrd
+matplotlib
+ipython[all]