DataScience Topics Survey/Visualization-of-DataScience-topics-survey.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of DataScience Topics Survey"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A survey was conducted to gauge an audience interest in different data science topics, namely:\n",
    "\n",
    "- Big Data (Spark / Hadoop)\n",
    "- Data Analysis / Statistics\n",
    "- Data Journalism\n",
    "- Data Visualization\n",
    "- Deep Learning\n",
    "- Machine Learning\n",
    "\n",
    "The participants had three options for each topic: Very Interested, Somewhat interested, and Not interested. 2,233 respondents completed the survey.\n",
    "\n",
    "The survey results have been saved in a csv file and can be accessed through this link: https://cocl.us/datascience_survey_data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Very interested</th>\n",
       "      <th>Somewhat interested</th>\n",
       "      <th>Not interested</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Big Data (Spark / Hadoop)</th>\n",
       "      <td>1332</td>\n",
       "      <td>729</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Analysis / Statistics</th>\n",
       "      <td>1688</td>\n",
       "      <td>444</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Journalism</th>\n",
       "      <td>429</td>\n",
       "      <td>1081</td>\n",
       "      <td>610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Visualization</th>\n",
       "      <td>1340</td>\n",
       "      <td>734</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deep Learning</th>\n",
       "      <td>1263</td>\n",
       "      <td>770</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machine Learning</th>\n",
       "      <td>1629</td>\n",
       "      <td>477</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Very interested  Somewhat interested  \\\n",
       "Big Data (Spark / Hadoop)              1332                  729   \n",
       "Data Analysis / Statistics             1688                  444   \n",
       "Data Journalism                         429                 1081   \n",
       "Data Visualization                     1340                  734   \n",
       "Deep Learning                          1263                  770   \n",
       "Machine Learning                       1629                  477   \n",
       "\n",
       "                            Not interested  \n",
       "Big Data (Spark / Hadoop)              127  \n",
       "Data Analysis / Statistics              60  \n",
       "Data Journalism                        610  \n",
       "Data Visualization                     102  \n",
       "Deep Learning                          136  \n",
       "Machine Learning                        74  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import libraries and read data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "df = pd.read_csv('Topic_Survey.csv',index_col=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Very interested</th>\n",
       "      <th>Somewhat interested</th>\n",
       "      <th>Not interested</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Data Analysis / Statistics</th>\n",
       "      <td>75.59</td>\n",
       "      <td>19.88</td>\n",
       "      <td>2.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machine Learning</th>\n",
       "      <td>72.95</td>\n",
       "      <td>21.36</td>\n",
       "      <td>3.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Visualization</th>\n",
       "      <td>60.01</td>\n",
       "      <td>32.87</td>\n",
       "      <td>4.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Big Data (Spark / Hadoop)</th>\n",
       "      <td>59.65</td>\n",
       "      <td>32.65</td>\n",
       "      <td>5.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deep Learning</th>\n",
       "      <td>56.56</td>\n",
       "      <td>34.48</td>\n",
       "      <td>6.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Journalism</th>\n",
       "      <td>19.21</td>\n",
       "      <td>48.41</td>\n",
       "      <td>27.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Very interested  Somewhat interested  \\\n",
       "Data Analysis / Statistics            75.59                19.88   \n",
       "Machine Learning                      72.95                21.36   \n",
       "Data Visualization                    60.01                32.87   \n",
       "Big Data (Spark / Hadoop)             59.65                32.65   \n",
       "Deep Learning                         56.56                34.48   \n",
       "Data Journalism                       19.21                48.41   \n",
       "\n",
       "                            Not interested  \n",
       "Data Analysis / Statistics            2.69  \n",
       "Machine Learning                      3.31  \n",
       "Data Visualization                    4.57  \n",
       "Big Data (Spark / Hadoop)             5.69  \n",
       "Deep Learning                         6.09  \n",
       "Data Journalism                      27.32  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prep the data\n",
    "df = df.sort_values('Very interested', ascending=False)\n",
    "respondents = 2233\n",
    "df = round((df / respondents) * 100,2)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create the plot\n",
    "per = df.plot(kind='bar', \n",
    "            figsize=(20, 8), \n",
    "            width=0.8, \n",
    "            color=['#5cb85c', '#5bc0de', '#d9534f'],\n",
    "           )\n",
    "\n",
    "per.set_title(\"Percentage of Respondents' Interest in Data Science Area\", fontsize=16)\n",
    "per.spines['left'].set_visible(False)\n",
    "per.spines['top'].set_visible(False)\n",
    "per.spines['right'].set_visible(False)\n",
    "per.axes.get_yaxis().set_visible(False)\n",
    "per.tick_params(labelsize=14)\n",
    "per.legend(fontsize=14)\n",
    "for p in per.patches:\n",
    "    per.annotate(np.round(p.get_height(),decimals=2), \n",
    "                (p.get_x()+p.get_width()/2., p.get_height()), \n",
    "                ha='center', \n",
    "                va='center', \n",
    "                xytext=(0, 10), \n",
    "                textcoords='offset points',\n",
    "                fontsize = 14\n",
    "               )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this bar plot we can infer that :\n",
    "\n",
    "- Most of the students are \"very interested\" in the field of Data Analytics/Statistics and Machine Learning\n",
    "- Only very few students are \"not interested\" in almost all the data science topics\n",
    "- In the field of **Data Journalism** more number of students are \"somewhat interested\" than \"very interested\""
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}