BootcampStats.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at the second question of interest.  That is - What does the data suggest of Bootcamp grads in terms of job placement and salary?\n",
    "\n",
    "Again, let's read in the data and necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Respondent</th>\n",
       "      <th>Professional</th>\n",
       "      <th>ProgramHobby</th>\n",
       "      <th>Country</th>\n",
       "      <th>University</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>FormalEducation</th>\n",
       "      <th>MajorUndergrad</th>\n",
       "      <th>HomeRemote</th>\n",
       "      <th>CompanySize</th>\n",
       "      <th>...</th>\n",
       "      <th>StackOverflowMakeMoney</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HighestEducationParents</th>\n",
       "      <th>Race</th>\n",
       "      <th>SurveyLong</th>\n",
       "      <th>QuestionsInteresting</th>\n",
       "      <th>QuestionsConfusing</th>\n",
       "      <th>InterestedAnswers</th>\n",
       "      <th>Salary</th>\n",
       "      <th>ExpectedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Student</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Not employed, and not looking for work</td>\n",
       "      <td>Secondary school</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>High school</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Student</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Yes, full-time</td>\n",
       "      <td>Employed part-time</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Computer science or software engineering</td>\n",
       "      <td>More than half, but not all, the time</td>\n",
       "      <td>20 to 99 employees</td>\n",
       "      <td>...</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>A master's degree</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Professional developer</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor's degree</td>\n",
       "      <td>Computer science or software engineering</td>\n",
       "      <td>Less than half the time, but at least one day ...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>...</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>A professional degree</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>113750.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Professional non-developer who sometimes write...</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Doctoral degree</td>\n",
       "      <td>A non-computer-focused engineering discipline</td>\n",
       "      <td>Less than half the time, but at least one day ...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>...</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>A doctoral degree</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Professional developer</td>\n",
       "      <td>Yes, I program as a hobby</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Master's degree</td>\n",
       "      <td>Computer science or software engineering</td>\n",
       "      <td>Never</td>\n",
       "      <td>10 to 19 employees</td>\n",
       "      <td>...</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",
       "      <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 × 154 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Respondent                                       Professional  \\\n",
       "0           1                                            Student   \n",
       "1           2                                            Student   \n",
       "2           3                             Professional developer   \n",
       "3           4  Professional non-developer who sometimes write...   \n",
       "4           5                             Professional developer   \n",
       "\n",
       "                ProgramHobby         Country      University  \\\n",
       "0                  Yes, both   United States              No   \n",
       "1                  Yes, both  United Kingdom  Yes, full-time   \n",
       "2                  Yes, both  United Kingdom              No   \n",
       "3                  Yes, both   United States              No   \n",
       "4  Yes, I program as a hobby     Switzerland              No   \n",
       "\n",
       "                         EmploymentStatus  \\\n",
       "0  Not employed, and not looking for work   \n",
       "1                      Employed part-time   \n",
       "2                      Employed full-time   \n",
       "3                      Employed full-time   \n",
       "4                      Employed full-time   \n",
       "\n",
       "                                     FormalEducation  \\\n",
       "0                                   Secondary school   \n",
       "1  Some college/university study without earning ...   \n",
       "2                                  Bachelor's degree   \n",
       "3                                    Doctoral degree   \n",
       "4                                    Master's degree   \n",
       "\n",
       "                                  MajorUndergrad  \\\n",
       "0                                            NaN   \n",
       "1       Computer science or software engineering   \n",
       "2       Computer science or software engineering   \n",
       "3  A non-computer-focused engineering discipline   \n",
       "4       Computer science or software engineering   \n",
       "\n",
       "                                          HomeRemote  \\\n",
       "0                                                NaN   \n",
       "1              More than half, but not all, the time   \n",
       "2  Less than half the time, but at least one day ...   \n",
       "3  Less than half the time, but at least one day ...   \n",
       "4                                              Never   \n",
       "\n",
       "                CompanySize       ...       StackOverflowMakeMoney Gender  \\\n",
       "0                       NaN       ...            Strongly disagree   Male   \n",
       "1        20 to 99 employees       ...            Strongly disagree   Male   \n",
       "2  10,000 or more employees       ...                     Disagree   Male   \n",
       "3  10,000 or more employees       ...                     Disagree   Male   \n",
       "4        10 to 19 employees       ...                          NaN    NaN   \n",
       "\n",
       "  HighestEducationParents                          Race         SurveyLong  \\\n",
       "0             High school  White or of European descent  Strongly disagree   \n",
       "1       A master's degree  White or of European descent     Somewhat agree   \n",
       "2   A professional degree  White or of European descent     Somewhat agree   \n",
       "3       A doctoral degree  White or of European descent              Agree   \n",
       "4                     NaN                           NaN                NaN   \n",
       "\n",
       "  QuestionsInteresting QuestionsConfusing InterestedAnswers    Salary  \\\n",
       "0       Strongly agree           Disagree    Strongly agree       NaN   \n",
       "1       Somewhat agree           Disagree    Strongly agree       NaN   \n",
       "2                Agree           Disagree             Agree  113750.0   \n",
       "3                Agree     Somewhat agree    Strongly agree       NaN   \n",
       "4                  NaN                NaN               NaN       NaN   \n",
       "\n",
       "   ExpectedSalary  \n",
       "0             NaN  \n",
       "1         37500.0  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "\n",
       "[5 rows x 154 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv('./survey_results_public.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2602, 154)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#In this case, we want to look at bootcamp data\n",
    "#First - let's just look at how many people took a bootcamp in the dataset\n",
    "\n",
    "bootcamp_df = df[df['TimeAfterBootcamp'].isnull()==False]\n",
    "not_bootcamp_df = df[df['TimeAfterBootcamp'].isnull()==True] \n",
    "bootcamp_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Looks like a reasonable sample of ~2600 people\n",
    "\n",
    "#Additional questions about bootcamps - they suggest high salaries, placement, \n",
    "#helping those with non-traditional backgrounds and diversity break into tech... let's see what\n",
    "#the data suggests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male                                                       0.863071\n",
       "Female                                                     0.112494\n",
       "Other                                                      0.006455\n",
       "Gender non-conforming                                      0.005533\n",
       "Male; Other                                                0.002766\n",
       "Female; Gender non-conforming                              0.001383\n",
       "Male; Gender non-conforming                                0.001383\n",
       "Male; Female; Transgender; Gender non-conforming; Other    0.000922\n",
       "Male; Female                                               0.000922\n",
       "Transgender; Gender non-conforming                         0.000922\n",
       "Transgender                                                0.000922\n",
       "Female; Transgender; Gender non-conforming                 0.000922\n",
       "Male; Transgender                                          0.000922\n",
       "Male; Female; Transgender; Gender non-conforming           0.000461\n",
       "Male; Gender non-conforming; Other                         0.000461\n",
       "Female; Gender non-conforming; Other                       0.000461\n",
       "Name: Gender, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bootcamp_df['Gender'].value_counts()/(bootcamp_df.shape[0] - sum(bootcamp_df['Gender'].isnull()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male                                                       0.903857\n",
       "Female                                                     0.071659\n",
       "Other                                                      0.006418\n",
       "Male; Other                                                0.005019\n",
       "Gender non-conforming                                      0.004501\n",
       "Male; Gender non-conforming                                0.001886\n",
       "Female; Transgender                                        0.001703\n",
       "Transgender                                                0.001612\n",
       "Female; Gender non-conforming                              0.000791\n",
       "Male; Female; Transgender; Gender non-conforming; Other    0.000395\n",
       "Transgender; Gender non-conforming                         0.000395\n",
       "Male; Female                                               0.000395\n",
       "Male; Transgender                                          0.000274\n",
       "Male; Female; Transgender; Gender non-conforming           0.000182\n",
       "Female; Transgender; Gender non-conforming                 0.000182\n",
       "Gender non-conforming; Other                               0.000122\n",
       "Male; Female; Transgender                                  0.000122\n",
       "Male; Transgender; Gender non-conforming                   0.000122\n",
       "Male; Female; Other                                        0.000061\n",
       "Male; Gender non-conforming; Other                         0.000061\n",
       "Female; Other                                              0.000030\n",
       "Male; Female; Transgender; Other                           0.000030\n",
       "Male; Transgender; Other                                   0.000030\n",
       "Female; Transgender; Gender non-conforming; Other          0.000030\n",
       "Female; Transgender; Other                                 0.000030\n",
       "Transgender; Other                                         0.000030\n",
       "Male; Female; Gender non-conforming; Other                 0.000030\n",
       "Male; Female; Gender non-conforming                        0.000030\n",
       "Name: Gender, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_bootcamp_df['Gender'].value_counts()/(not_bootcamp_df.shape[0] - sum(not_bootcamp_df['Gender'].isnull()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#It does appear there is a small push for diversity overall by bootcamps, but not huge..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bachelor's degree                                                    0.475019\n",
       "Master's degree                                                      0.225211\n",
       "Some college/university study without earning a bachelor's degree    0.162567\n",
       "Secondary school                                                     0.069562\n",
       "Doctoral degree                                                      0.022291\n",
       "Professional degree                                                  0.016141\n",
       "I prefer not to answer                                               0.011914\n",
       "Primary/elementary school                                            0.009224\n",
       "I never completed any formal education                               0.008071\n",
       "Name: FormalEducation, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bootcamp_df['FormalEducation'].value_counts()/(bootcamp_df.shape[0] - sum(bootcamp_df['FormalEducation'].isnull()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bachelor's degree                                                    0.417565\n",
       "Master's degree                                                      0.216335\n",
       "Some college/university study without earning a bachelor's degree    0.157942\n",
       "Secondary school                                                     0.117381\n",
       "Doctoral degree                                                      0.025620\n",
       "I prefer not to answer                                               0.022095\n",
       "Primary/elementary school                                            0.020967\n",
       "Professional degree                                                  0.013794\n",
       "I never completed any formal education                               0.008301\n",
       "Name: FormalEducation, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_bootcamp_df['FormalEducation'].value_counts()/(not_bootcamp_df.shape[0] - sum(not_bootcamp_df['FormalEducation'].isnull()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#In terms of formal education it looks basically the same - more bachelors degree holders do \n",
    "#bootcamps, but fewer phds do bootcamps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "I already had a job as a developer when I started the program    0.458493\n",
       "Immediately upon graduating                                      0.112990\n",
       "I got a job as a developer before completing the program         0.096849\n",
       "One to three months                                              0.087625\n",
       "I haven't gotten a job as a developer yet                        0.081091\n",
       "Less than a month                                                0.059954\n",
       "Four to six months                                               0.040354\n",
       "Longer than a year                                               0.032667\n",
       "Six months to a year                                             0.029977\n",
       "Name: TimeAfterBootcamp, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bootcamp_df['TimeAfterBootcamp'].value_counts()/bootcamp_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#So interestingly this data makes it more difficult to analyze the impact of bootcamps,\n",
    "# as many of the students already had developer jobs before starting the program\n",
    "# we could remove them?\n",
    "\n",
    "#If you are truly new to the space, we can rule out that you already have a job as a developer\n",
    "# then we can look at the other individuals and see which are still not \n",
    "\n",
    "not_devs = bootcamp_df[bootcamp_df['TimeAfterBootcamp']!=\"I already had a job as a developer when I started the program\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Immediately upon graduating                                 0.208659\n",
       "I got a job as a developer before completing the program    0.178850\n",
       "One to three months                                         0.161817\n",
       "I haven't gotten a job as a developer yet                   0.149752\n",
       "Less than a month                                           0.110717\n",
       "Four to six months                                          0.074521\n",
       "Longer than a year                                          0.060326\n",
       "Six months to a year                                        0.055358\n",
       "Name: TimeAfterBootcamp, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_devs['TimeAfterBootcamp'].value_counts()/not_devs.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Respondent</th>\n",
       "      <th>Professional</th>\n",
       "      <th>ProgramHobby</th>\n",
       "      <th>Country</th>\n",
       "      <th>University</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>FormalEducation</th>\n",
       "      <th>MajorUndergrad</th>\n",
       "      <th>HomeRemote</th>\n",
       "      <th>CompanySize</th>\n",
       "      <th>...</th>\n",
       "      <th>StackOverflowMakeMoney</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HighestEducationParents</th>\n",
       "      <th>Race</th>\n",
       "      <th>SurveyLong</th>\n",
       "      <th>QuestionsInteresting</th>\n",
       "      <th>QuestionsConfusing</th>\n",
       "      <th>InterestedAnswers</th>\n",
       "      <th>Salary</th>\n",
       "      <th>ExpectedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1696</th>\n",
       "      <td>1697</td>\n",
       "      <td>Professional developer</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor's degree</td>\n",
       "      <td>Computer science or software engineering</td>\n",
       "      <td>All or almost all the time (I'm full-time remote)</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>...</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>High school</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 154 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Respondent            Professional ProgramHobby        Country  \\\n",
       "1696        1697  Professional developer    Yes, both  United States   \n",
       "\n",
       "     University    EmploymentStatus    FormalEducation  \\\n",
       "1696         No  Employed full-time  Bachelor's degree   \n",
       "\n",
       "                                MajorUndergrad  \\\n",
       "1696  Computer science or software engineering   \n",
       "\n",
       "                                             HomeRemote  \\\n",
       "1696  All or almost all the time (I'm full-time remote)   \n",
       "\n",
       "                   CompanySize       ...       StackOverflowMakeMoney Gender  \\\n",
       "1696  10,000 or more employees       ...                     Disagree   Male   \n",
       "\n",
       "     HighestEducationParents                          Race SurveyLong  \\\n",
       "1696             High school  White or of European descent   Disagree   \n",
       "\n",
       "     QuestionsInteresting QuestionsConfusing InterestedAnswers    Salary  \\\n",
       "1696                Agree           Disagree             Agree  195000.0   \n",
       "\n",
       "      ExpectedSalary  \n",
       "1696             NaN  \n",
       "\n",
       "[1 rows x 154 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bootcamp_df[bootcamp_df['Salary']==195000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l1sKGc3uwmVk1AyYRSe8B3gvsK+kTpUX7ALvUMzAzM2t9g7VEdgM60np7l8pfAE6tV1Bm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d0ac518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bootcamp_df['Salary'].hist(bins=20);\n",
    "plt.title('Salary for Bootcamp Grads');\n",
    "plt.xlabel('Salary');\n",
    "plt.ylabel('Count');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       765.000000\n",
       "mean      61775.438188\n",
       "std       41797.379964\n",
       "min           0.029364\n",
       "25%       29520.430108\n",
       "50%       56980.056980\n",
       "75%       91397.849462\n",
       "max      195000.000000\n",
       "Name: Salary, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bootcamp_df['Salary'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Here we can get some idea of how bootcamp grades fair, but this isn't straightforward.\n",
    "#Many of these individuals are not new to the field, and the salaries are all over the place\n",
    "#But the descriptive statistics here give us some ideas... just nothing really concrete"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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