Introduction-partB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div class=\"alert alert-block alert-success\"><h3>IFN619 - Data Analytics for Strategic Decision Makers</h4></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
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   "source": [
    "## Introduction :: Asking the right questions\n",
    "\n",
    "1. Introductions\n",
    "2. Unit Structure and Assessment\n",
    "3. Communication and Slack\n",
    "**4. Data Analytics Cycle**\n",
    "**5. Getting started with Jupyter**\n",
    "**6. Some Exercises to Consolidate Your Knowledge**\n",
    "**7. Asking the right questions**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
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   "source": [
    "### [4] Data Analytics Cycle\n",
    "\n",
    "In our approach to data analytics, we will follow a process that requires that we address 5 questions:\n",
    "1. Which is the right question?\n",
    "2. Which is the right data?\n",
    "3. Which is the right analysis?\n",
    "4. Which is the right visualisation?\n",
    "5. Which is the right insight?\n",
    "\n",
    "For this unit, we are concerned with more than just data analytics, we are interested in what is *appropriate, efficous, ethical ...* what is ***right!***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### 1. A starting question\n",
    "\n",
    "Students choose to study for many different reasons. Usually, these reasons are associated with both (a) their history and (b) their imagined future. \n",
    "\n",
    "*Which questions might give us insight into students of IFN619 and their reasons for studying?* \n",
    "\n",
    "- **One example:** ***Which fields are IFN619 students most interested in?***\n",
    "\n",
    "**DISCUSSION:** What are some other possible questions?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### 2. Some relevant data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importa Python libraries\n",
    "import pandas as pd               # used for tabular datasets\n",
    "import matplotlib.pyplot as plt   # used for visualisation purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# read our data into a dataframe variable (df)\n",
    "data = pd.read_csv('data/ifn619_2020.csv') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "student_id\n",
      "course_code\n",
      "major_code\n"
     ]
    }
   ],
   "source": [
    "# what is this data about?\n",
    "# let's check if there is any information in the columns\n",
    "\n",
    "for heading in data.columns:   # for each heading in the dataframe\n",
    "    print(heading)           # print the heading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>student_id</th>\n",
       "      <th>course_code</th>\n",
       "      <th>major_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>IN27</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>IN23</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>IN20</td>\n",
       "      <td>IN20MJR-DATASC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>IN26</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>IN20</td>\n",
       "      <td>IN20MJR-DATASC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>IN21</td>\n",
       "      <td>IN20MJR-SECUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>IN20</td>\n",
       "      <td>IN20MJR-BUSPMGT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>IN20</td>\n",
       "      <td>IN20MJR-COMPSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>IN20</td>\n",
       "      <td>IN20MJR-DATASC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>IN26</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student_id course_code       major_code\n",
       "0           1        IN27                 \n",
       "1           2        IN23                 \n",
       "2           3        IN20   IN20MJR-DATASC\n",
       "3           4        IN26                 \n",
       "4           5        IN20   IN20MJR-DATASC\n",
       "5           6        IN21    IN20MJR-SECUR\n",
       "6           7        IN20  IN20MJR-BUSPMGT\n",
       "7           8        IN20   IN20MJR-COMPSC\n",
       "8           9        IN20   IN20MJR-DATASC\n",
       "9          10        IN26                 "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's take a quick look at our data\n",
    "N = 10          # number of rows to display\n",
    "data.head(N)    # print the first N rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**DISCUSSION:** How relevant is the data, what are some potential issues?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### 3. Analysing the data\n",
    "\n",
    "We can explore the data, asking questions like *how many students are there?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(218, 3)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many students are in this dataset?\n",
    "# We can answer this questions by determining the total number of \n",
    "# instances (rows) in our dataset\n",
    "\n",
    "# The dimensional shape of the dataframe\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "218"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We only want the number of rows\n",
    "data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 218 students represented in the data\n"
     ]
    }
   ],
   "source": [
    "print(\"There are {} students represented in the data\".format(df.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**DISCUSSION:** Why might this question be important?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "How can we address our original question: Which fields are IFN619 students most interested in?\n",
    "\n",
    "Perhaps, the course code?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      IN27\n",
       "1      IN23\n",
       "2      IN20\n",
       "3      IN26\n",
       "4      IN20\n",
       "       ... \n",
       "213    IN23\n",
       "214    IN27\n",
       "215    IN20\n",
       "216    IN20\n",
       "217    IN20\n",
       "Name: course_code, Length: 218, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select column \"course_code\" from our dataset\\\n",
    "\n",
    "data.course_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['IN27', 'IN23', 'IN20', 'IN26', 'IN21', 'U083'], dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can remove all repeated elements of the columns by \n",
    "# calling the unique() method\n",
    "\n",
    "data.course_code.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "But perhaps what is more useful is to know how many students are from each course..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "course_code\n",
       "IN20    90\n",
       "IN21    12\n",
       "IN23    14\n",
       "IN26    19\n",
       "IN27    82\n",
       "U083     1\n",
       "Name: student_id, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "course_counts = data.groupby('course_code')['student_id'].count()\n",
    "course_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "What about the major code?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' ', 'IN20MJR-DATASC', 'IN20MJR-SECUR', 'IN20MJR-BUSPMGT',\n",
       "       'IN20MJR-COMPSC', 'IN20MJR-ENTSYS', 'IN20MJR-INFOMGT',\n",
       "       'IN20MJR-BUSANAL'], dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.major_code.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "major_code\n",
       "                   117\n",
       "IN20MJR-BUSANAL      1\n",
       "IN20MJR-BUSPMGT     15\n",
       "IN20MJR-COMPSC      15\n",
       "IN20MJR-DATASC      59\n",
       "IN20MJR-ENTSYS       4\n",
       "IN20MJR-INFOMGT      6\n",
       "IN20MJR-SECUR        1\n",
       "Name: student_id, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "major_counts = data.groupby('major_code')['student_id'].count()\n",
    "major_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**DISCUSSION:** What is the problem with our analysis?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### 4. Visualising the results\n",
    "\n",
    "Visualising our results can help us see features that are difficult to see in raw numbers. Visualisation can also help others understand our results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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": [
    "\n",
    "course_counts.plot(kind=\"line\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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": [
    "major_counts.plot(kind=\"bar\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**DISCUSSION:** What are the problem with these visualisations? How can these problems be fixed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>student_id</th>\n",
       "      <th>course_code</th>\n",
       "      <th>major_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>IN20</td>\n",
       "      <td>IN20MJR-DATASC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>IN20</td>\n",
       "      <td>IN20MJR-DATASC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>IN21</td>\n",
       "      <td>IN20MJR-SECUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>IN20</td>\n",
       "      <td>IN20MJR-BUSPMGT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>IN20</td>\n",
       "      <td>IN20MJR-COMPSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>212</td>\n",
       "      <td>IN20</td>\n",
       "      <td>IN20MJR-DATASC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>213</td>\n",
       "      <td>IN21</td>\n",
       "      <td>IN20MJR-DATASC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>216</td>\n",
       "      <td>IN20</td>\n",
       "      <td>IN20MJR-BUSANAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>217</td>\n",
       "      <td>IN20</td>\n",
       "      <td>IN20MJR-DATASC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>218</td>\n",
       "      <td>IN20</td>\n",
       "      <td>IN20MJR-COMPSC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     student_id course_code       major_code\n",
       "2             3        IN20   IN20MJR-DATASC\n",
       "4             5        IN20   IN20MJR-DATASC\n",
       "5             6        IN21    IN20MJR-SECUR\n",
       "6             7        IN20  IN20MJR-BUSPMGT\n",
       "7             8        IN20   IN20MJR-COMPSC\n",
       "..          ...         ...              ...\n",
       "211         212        IN20   IN20MJR-DATASC\n",
       "212         213        IN21   IN20MJR-DATASC\n",
       "215         216        IN20  IN20MJR-BUSANAL\n",
       "216         217        IN20   IN20MJR-DATASC\n",
       "217         218        IN20   IN20MJR-COMPSC\n",
       "\n",
       "[101 rows x 3 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = data[data['major_code'].str.contains('\\S', regex=True)]\n",
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "major_code\n",
       "IN20MJR-BUSANAL     1\n",
       "IN20MJR-BUSPMGT    15\n",
       "IN20MJR-COMPSC     15\n",
       "IN20MJR-DATASC     59\n",
       "IN20MJR-ENTSYS      4\n",
       "IN20MJR-INFOMGT     6\n",
       "IN20MJR-SECUR       1\n",
       "Name: student_id, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_major_counts = new_data.groupby('major_code')['student_id'].count()\n",
    "only_major_counts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "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": [
    "only_major_counts.plot(kind=\"bar\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe we can add different colors to the different majors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "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": [
    "# we have 7 different majors, so we can. use the system's colors from the default color map\n",
    "colors = tuple([\"C0\", \"C1\",\"C2\",\"C3\",\"C4\",\"C5\", \"C6\"])\n",
    "\n",
    "only_major_counts.plot(kind=\"bar\", color=colors)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### 5. Insight\n",
    "\n",
    "To actually make our analytics insightful, we need to present it in a way that makes the analysis meaningful to people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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": [
    "chartTitle=\"Number of IFN619 students per major\"\n",
    "yaxisLabel=\"number of students\"\n",
    "xaxisLabel=\"major\"\n",
    "\n",
    "only_major_counts.plot.bar(color=colors, ax=plt.axes(title=chartTitle,ylabel = yaxisLabel ,\n",
    "                                                     xlabel = xaxisLabel) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**DISCUSSION:** Is this sufficient for insight? What else is needed for insight?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Only 101 of a total 218 students have a major. This represents only 46.33% of the population.\n"
     ]
    }
   ],
   "source": [
    "studs_with_major = only_major_counts.sum()\n",
    "total_studs = data.shape[0]\n",
    "ratio = studs_with_major/total_studs\n",
    "sent = \"Only {} of a total {} students have a major. This represents only {:.2%} of the population.\"\n",
    "print(sent.format(studs_with_major,total_studs,ratio))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**DISCUSSION:** \n",
    "- Is this enough?\n",
    "- What can we say about the original question?\n",
    "- Think back to the issues raised in the discussion - How do they compound over the course of the analytics?\n",
    "- What is the end result for the insight?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### [5] Getting started with Jupyter\n",
    "- Create a GitHub account (if you don't have one)\n",
    "- Login to Jupyter - [http://jupyter.infosci-apps.qut.edu.au](http://jupyter.infosci-apps.qut.edu.au)\n",
    "- Click the link in Blackboard to sync the workshop materials to your Jupyter accounts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Notebooks have 2 main types of cells - `Markdown` cells for displaying content like a web page, and `Code` cells for running code (in our case Python code) and displaying the results. In this practical session we will explore both of these types of cells and how they work together for a complete analytics process.\n",
    "\n",
    "Before moving on to the tasks, familiarise yourself with the menus above, and in particular the keyboard short cuts (click the keyboard icon) for `run cell and insert below`, `run cell and select next`, changing between `command` and `edit` modes, and changing between `Code` and `Markdown` cell types.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### [6] Some Exercises to Consolidate Your Knowledge\n",
    "\n",
    "You are part of a data analytics company that evaluates data from sport competitions. Your boss needs to get some information about the olympic games and wants you to answer the following questions:\n",
    "\n",
    "-> answer each question with the appropriate visualisations and insights\n",
    "\n",
    "-> (dataset file can be found in *data/olympics.csv*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### **Question 1:** \n",
    "Which are the best performing countries in the olympics (most medals won in summer games, winter games, or both)?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset to be used throughout this exercise\n",
    "dataset_path = \"data/olympics.csv\"\n",
    "\n",
    "# YOUR ANALYSIS HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Question 2:**\n",
    "Which country had the biggest difference between their summer and winter gold medal counts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOUR ANALYSIS HERE\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Question 3:**\n",
    "Make an exploratory analysis of the entire dataset, find a relevant business concern that would provide new insights about the performance of each country, and provide an answer to it.\n",
    "\n",
    "-> there is no right / wrong answer here. Your creatility and ability to find a relevant business concern are the most important things here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOUR ANALYSIS HERE\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### [7] Asking the right questions\n",
    "\n",
    "- Not just about the analytics, more about asking the right questions\n",
    "- Try the business analytics excercise at home"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [8] What's next\n",
    "- Studios - help with getting started in Jupyter\n",
    "- Tutorials - work on a basic task to help you get oriented to the technologies\n",
    "- Assessment - Assignments 1 & 3 will be introduced next week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}