notebook_Exploratory_Data_Analysis_with_SQL__eqznon1EA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\"  width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "<h1 align=center><font size = 5>Assignment: SQL Notebook for Peer Assignment</font></h1>\n",
    "\n",
    "Estimated time needed: **60** minutes.\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Using this Python notebook you will:\n",
    "\n",
    "1.  Understand the Spacex DataSet\n",
    "2.  Load the dataset  into the corresponding table in a Db2 database\n",
    "3.  Execute SQL queries to answer assignment questions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview of the DataSet\n",
    "\n",
    "SpaceX has gained worldwide attention for a series of historic milestones.\n",
    "\n",
    "It is the only private company ever to return a spacecraft from low-earth orbit, which it first accomplished in December 2010.\n",
    "SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars wheras other providers cost upward of 165 million dollars each, much of the savings is because Space X can reuse the first stage.\n",
    "\n",
    "Therefore if we can determine if the first stage will land, we can determine the cost of a launch.\n",
    "\n",
    "This information can be used if an alternate company wants to bid against SpaceX for a rocket launch.\n",
    "\n",
    "This dataset includes a record for each payload carried during a SpaceX mission into outer space.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download the datasets\n",
    "\n",
    "This assignment requires you to load the spacex dataset.\n",
    "\n",
    "In many cases the dataset to be analyzed is available as a .CSV (comma separated values) file, perhaps on the internet. Click on the link below to download and save the dataset (.CSV file):\n",
    "\n",
    "<a href=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/labs/module_2/data/Spacex.csv?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\" target=\"_blank\">Spacex DataSet</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store the dataset in database table\n",
    "\n",
    "**it is highly recommended to manually load the table using the database console LOAD tool in DB2**.\n",
    "\n",
    "<img src = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/labs/module_2/images/spacexload.png\">\n",
    "\n",
    "Now open the Db2 console, open the LOAD tool, Select / Drag the .CSV file for the  dataset, Next create a New Table, and then follow the steps on-screen instructions to load the data. Name the new table as follows:\n",
    "\n",
    "**SPACEXDATASET**\n",
    "\n",
    "**Follow these steps while using old DB2 UI which is having Open Console Screen**\n",
    "\n",
    "**Note:While loading Spacex dataset, ensure that detect datatypes is disabled. Later click on the pencil icon(edit option).**\n",
    "\n",
    "1.  Change the Date Format by manually typing DD-MM-YYYY and timestamp format as DD-MM-YYYY HH\\:MM:SS.\n",
    "\n",
    "    Here you should place the cursor at Date field and manually type as DD-MM-YYYY.\n",
    "\n",
    "2.  Change the PAYLOAD_MASS\\_\\_KG\\_  datatype  to INTEGER.\n",
    "\n",
    "<img src = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/labs/module_2/images/spacexload2.png\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Changes to be considered when having DB2 instance with the new UI having Go to UI screen**\n",
    "\n",
    "*   Refer to this insruction in this <a href=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DB0201EN-SkillsNetwork/labs/Labs_Coursera_V5/labs/Lab%20-%20Sign%20up%20for%20IBM%20Cloud%20-%20Create%20Db2%20service%20instance%20-%20Get%20started%20with%20the%20Db2%20console/instructional-labs.md.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">link</a> for viewing  the new  Go to UI screen.\n",
    "\n",
    "*   Later click on **Data link(below SQL)**  in the Go to UI screen  and click on **Load Data** tab.\n",
    "\n",
    "*   Later browse for the downloaded spacex file.\n",
    "\n",
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/labs/module_2/images/browsefile.png\" width=\"800\"/>\n",
    "\n",
    "*   Once done select the schema andload the file.\n",
    "\n",
    " <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/labs/module_2/images/spacexload3.png\" width=\"800\"/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sqlalchemy==1.3.9 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (1.3.9)\n",
      "Requirement already satisfied: ibm_db_sa in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (0.3.7)\n",
      "Requirement already satisfied: ibm-db>=2.0.0 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ibm_db_sa) (3.1.0)\n",
      "Requirement already satisfied: sqlalchemy>=0.7.3 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ibm_db_sa) (1.3.9)\n",
      "Requirement already satisfied: ipython-sql in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (0.4.0)\n",
      "Requirement already satisfied: sqlalchemy>=0.6.7 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython-sql) (1.3.9)\n",
      "Requirement already satisfied: six in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython-sql) (1.15.0)\n",
      "Requirement already satisfied: ipython-genutils>=0.1.0 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython-sql) (0.2.0)\n",
      "Requirement already satisfied: prettytable<1 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython-sql) (0.7.2)\n",
      "Requirement already satisfied: ipython>=1.0 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython-sql) (7.29.0)\n",
      "Requirement already satisfied: sqlparse in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython-sql) (0.4.2)\n",
      "Requirement already satisfied: jedi>=0.16 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (0.18.0)\n",
      "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (3.0.20)\n",
      "Requirement already satisfied: decorator in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (5.1.0)\n",
      "Requirement already satisfied: setuptools>=18.5 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (58.0.4)\n",
      "Requirement already satisfied: backcall in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (0.2.0)\n",
      "Requirement already satisfied: pickleshare in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (0.7.5)\n",
      "Requirement already satisfied: pexpect>4.3 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (4.8.0)\n",
      "Requirement already satisfied: traitlets>=4.2 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (5.1.1)\n",
      "Requirement already satisfied: matplotlib-inline in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (0.1.2)\n",
      "Requirement already satisfied: pygments in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (2.10.0)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from jedi>=0.16->ipython>=1.0->ipython-sql) (0.8.3)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from pexpect>4.3->ipython>=1.0->ipython-sql) (0.7.0)\n",
      "Requirement already satisfied: wcwidth in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=1.0->ipython-sql) (0.2.5)\n"
     ]
    }
   ],
   "source": [
    "!pip install sqlalchemy==1.3.9\n",
    "!pip install ibm_db_sa\n",
    "!pip install ipython-sql"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to the database\n",
    "\n",
    "Let us first load the SQL extension and establish a connection with the database\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sql extension is already loaded. To reload it, use:\n",
      "  %reload_ext sql\n"
     ]
    }
   ],
   "source": [
    "%load_ext sql"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DB2 magic in case of old UI service credentials.**\n",
    "\n",
    "In the next cell enter your db2 connection string. Recall you created Service Credentials for your Db2 instance before. From the **uri** field of your Db2 service credentials copy everything after db2:// (except the double quote at the end) and paste it in the cell below after ibm_db_sa://\n",
    "\n",
    "<img src =\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DB0201EN-SkillsNetwork/labs/FinalModule_edX/images/URI.jpg\">\n",
    "\n",
    "in the following format\n",
    "\n",
    "**%sql ibm_db_sa://my-username:my-password\\@my-hostname:my-port/my-db-name**\n",
    "\n",
    "**DB2 magic in case of new UI service credentials.**\n",
    "\n",
    "<img src =\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/labs/module_2/images/servicecredentials.png\" width=600>  \n",
    "\n",
    "*   Use the following format.\n",
    "\n",
    "*   Add security=SSL at the end\n",
    "\n",
    "**%sql ibm_db_sa://my-username:my-password\\@my-hostname:my-port/my-db-name?security=SSL**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(ibm_db_dbi.OperationalError) ibm_db_dbi::OperationalError: [IBM][CLI Driver] SQL30081N  A communication error has been detected. Communication protocol being used: \"TCP/IP\".  Communication API being used: \"SOCKETS\".  Location where the error was detected: \"169.60.146.125\".  Communication function detecting the error: \"recv\".  Protocol specific error code(s): \"104\", \"*\", \"0\".  SQLSTATE=08001 SQLCODE=-30081\n",
      "(Background on this error at: http://sqlalche.me/e/e3q8)\n",
      "Connection info needed in SQLAlchemy format, example:\n",
      "               postgresql://username:password@hostname/dbname\n",
      "               or an existing connection: dict_keys([])\n"
     ]
    }
   ],
   "source": [
    "%sql ibm_db_sa://dmk94268:T0OsLnIy2r4JsLzw@b70af05b-76e4-4bca-a1f5-23dbb4c6a74e.c1ogj3sd0tgtu0lqde00.databases.appdomain.cloud:32716/db2://dmk94268:T0OsLnIy2r4JsLzw@b70af05b-76e4-4bca-a1f5-23dbb4c6a74e.c1ogj3sd0tgtu0lqde00.databases.appdomain.cloud:32716/bludb?authSource=admin&replicaSet=replset?security=SSL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tasks\n",
    "\n",
    "Now write and execute SQL queries to solve the assignment tasks.\n",
    "\n",
    "### Task 1\n",
    "\n",
    "##### Display the names of the unique launch sites  in the space mission\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment variable $DATABASE_URL not set, and no connect string given.\n",
      "Connection info needed in SQLAlchemy format, example:\n",
      "               postgresql://username:password@hostname/dbname\n",
      "               or an existing connection: dict_keys([])\n"
     ]
    }
   ],
   "source": [
    "%sql SELECT DISTINCT LAUNCH_SITE as \"Launch_Sites\" FROM SPACEXTBL;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2\n",
    "\n",
    "##### Display 5 records where launch sites begin with the string 'CCA'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment variable $DATABASE_URL not set, and no connect string given.\n",
      "Connection info needed in SQLAlchemy format, example:\n",
      "               postgresql://username:password@hostname/dbname\n",
      "               or an existing connection: dict_keys([])\n"
     ]
    }
   ],
   "source": [
    "%sql SELECT LAUNCH_SITE FROM SPACEXTBL WHERE LAUNCH_SITE LIKE 'CCA%' LIMIT 5;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3\n",
    "\n",
    "##### Display the total payload mass carried by boosters launched by NASA (CRS)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sql SELECT SUM (PAYLOAD_MASS__kg_) FROM SPACEXTBL WHERE CUSTOMER = 'NASA(CRS)' ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4\n",
    "\n",
    "##### Display average payload mass carried by booster version F9 v1.1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sql SELECT AVERAGE (PAYLOAD_MASS__KG_) FROM SPACEXTBL WHERE BOOSTER_VERSION = 'F9 v1.1';"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5\n",
    "\n",
    "##### List the date when the first successful landing outcome in ground pad was acheived.\n",
    "\n",
    "*Hint:Use min function*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sql SELECT MIN (DATE) AS \"First Successful Landing\" FROM SPACEXTBL WHERE LANDING_OUTCOME = 'Success (ground pad)';"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 6\n",
    "\n",
    "##### List the names of the boosters which have success in drone ship and have payload mass greater than 4000 but less than 6000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sql SELECT BOOSTER_VERSION FROM SPACEXTBL WHERE LANDING_OUTCOME = 'Success (drone ship)' AND PAYLOAD_MASS__KG_ > 4000 AND PAYLOAD_MASS__KG_ < 6000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 7\n",
    "\n",
    "##### List the total number of successful and failure mission outcomes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sql SELECT COUNT MISSION_OUTCOME AS \"succesful mission \"FROM SPACEXTBL WHERE MISSION_OUTCOME LIKE 'Success%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sql SELECT COUNT MISSION_OUTCOME AS \"failure mission \" FROM SPACEXTBL WHERE MISSION_OUTCOME LIKE 'Fail%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sql SELECT sum(case when MISSION_OUTCOME LIKE '%Success%' then 1 else 0 end) AS \"Successful Mission\", \\\n",
    "    sum(case when MISSION_OUTCOME LIKE '%Failure%' then 1 else 0 end) AS \"Failure Mission\" \\\n",
    "FROM SPACEXTBL;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 8\n",
    "\n",
    "##### List the   names of the booster_versions which have carried the maximum payload mass. Use a subquery\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sql SELECT DISTINCT BOOSTER_VERSION AS \"Booster Versions which carried the Maximum Payload Mass\" FROM SPACEXTBL \\\n",
    "WHERE PAYLOAD_MASS__KG_ =(SELECT MAX(PAYLOAD_MASS__KG_) FROM SPACEXTBL);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 9\n",
    "\n",
    "##### List the failed landing_outcomes in drone ship, their booster versions, and launch site names for in year 2015\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sql SELECT BOOSTER_VERSION, LAUNCH_SITE FROM SPACEXTBL WHERE DATE LIKE '2015-%' AND \\\n",
    "LANDING__OUTCOME = 'Failure (drone ship)';"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 10\n",
    "\n",
    "##### Rank the count of landing outcomes (such as Failure (drone ship) or Success (ground pad)) between the date 2010-06-04 and 2017-03-20, in descending order\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sql SELECT LANDING__OUTCOME as \"Landing Outcome\", COUNT(LANDING__OUTCOME) AS \"Total Count\" FROM SPACEX \\\n",
    "WHERE DATE BETWEEN '2010-06-04' AND '2017-03-20' \\\n",
    "GROUP BY  LANDING__OUTCOME \\\n",
    "ORDER BY COUNT(LANDING__OUTCOME) DESC ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reference Links\n",
    "\n",
    "*   <a href =\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DB0201EN-SkillsNetwork/labs/Labs_Coursera_V5/labs/Lab%20-%20String%20Patterns%20-%20Sorting%20-%20Grouping/instructional-labs.md.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01&origin=www.coursera.org\">Hands-on Lab : String Patterns, Sorting and Grouping</a>\n",
    "\n",
    "*   <a  href=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DB0201EN-SkillsNetwork/labs/Labs_Coursera_V5/labs/Lab%20-%20Built-in%20functions%20/Hands-on_Lab__Built-in_Functions.md.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01&origin=www.coursera.org\">Hands-on Lab: Built-in functions</a>\n",
    "\n",
    "*   <a  href=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DB0201EN-SkillsNetwork/labs/Labs_Coursera_V5/labs/Lab%20-%20Sub-queries%20and%20Nested%20SELECTs%20/instructional-labs.md.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01&origin=www.coursera.org\">Hands-on Lab : Sub-queries and Nested SELECT Statements</a>\n",
    "\n",
    "*   <a href=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DB0201EN-SkillsNetwork/labs/Module%205/DB0201EN-Week3-1-3-SQLmagic.ipynb?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Hands-on Tutorial: Accessing Databases with SQL magic</a>\n",
    "\n",
    "*   <a href= \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DB0201EN-SkillsNetwork/labs/Module%205/DB0201EN-Week3-1-4-Analyzing.ipynb?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Hands-on Lab: Analyzing a real World Data Set</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Author(s)\n",
    "\n",
    "<h4> Lakshmi Holla </h4>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other Contributors\n",
    "\n",
    "<h4> Rav Ahuja </h4>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change log\n",
    "\n",
    "| Date       | Version | Changed by    | Change Description        |\n",
    "| ---------- | ------- | ------------- | ------------------------- |\n",
    "| 2021-10-12 | 0.4     | Lakshmi Holla | Changed markdown          |\n",
    "| 2021-08-24 | 0.3     | Lakshmi Holla | Added library update      |\n",
    "| 2021-07-09 | 0.2     | Lakshmi Holla | Changes made in magic sql |\n",
    "| 2021-05-20 | 0.1     | Lakshmi Holla | Created Initial Version   |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <h3 align=\"center\"> © IBM Corporation 2021. All rights reserved. <h3/>\n"
   ]
  }
 ],
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    "name": "ipython",
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