Lab4-jupyter-labs-eda-sql-coursera.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">Spacex DataSet</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Navigate to the 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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">link</a> for viewing  the   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": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting sqlalchemy==1.3.9\n",
      "  Downloading https://files.pythonhosted.org/packages/89/4e/f10fc5063d1048b3813c0caf99f06ec2b73851ae1a939feb85315dacb3fc/SQLAlchemy-1.3.9.tar.gz (6.0MB)\n",
      "Building wheels for collected packages: sqlalchemy\n",
      "  Running setup.py bdist_wheel for sqlalchemy: started\n",
      "  Running setup.py bdist_wheel for sqlalchemy: finished with status 'done'\n",
      "  Stored in directory: C:\\Users\\Xu Qiao Qi\\AppData\\Local\\pip\\Cache\\wheels\\23\\84\\f4\\c15e90bd4c87e0377dad50e670f4535715550f6fc3be27ea0f\n",
      "Successfully built sqlalchemy\n",
      "Installing collected packages: sqlalchemy\n",
      "  Found existing installation: SQLAlchemy 1.2.15\n",
      "    Uninstalling SQLAlchemy-1.2.15:\n",
      "      Successfully uninstalled SQLAlchemy-1.2.15\n",
      "Successfully installed sqlalchemy-1.3.9\n",
      "Collecting ibm_db_sa\n",
      "  Downloading https://files.pythonhosted.org/packages/3f/df/0659d08791752f51ac19aafc06e930d6c845d6f432e3776f6ddc5666bf43/ibm_db_sa-0.3.8-py3-none-any.whl\n",
      "Requirement already satisfied: sqlalchemy>=0.7.3 in c:\\users\\xu qiao qi\\anaconda3\\lib\\site-packages (from ibm_db_sa) (1.3.9)\n",
      "Collecting ibm-db>=2.0.0 (from ibm_db_sa)\n",
      "  Downloading https://files.pythonhosted.org/packages/8e/03/5f80440d4ca48679e7d0648590481bb03a0d6986ba3c10063d25dafbdfb8/ibm_db-3.1.3.tar.gz (1.4MB)\n",
      "  Installing build dependencies: started\n",
      "  Installing build dependencies: finished with status 'done'\n",
      "    Complete output from command python setup.py egg_info:\n",
      "    Traceback (most recent call last):\n",
      "      File \"<string>\", line 1, in <module>\n",
      "      File \"C:\\Users\\XUQIAO~1\\AppData\\Local\\Temp\\pip-build-env-00s4sk70\\Lib\\site-packages\\setuptools\\__init__.py\", line 18, in <module>\n",
      "        from setuptools.dist import Distribution\n",
      "      File \"C:\\Users\\XUQIAO~1\\AppData\\Local\\Temp\\pip-build-env-00s4sk70\\Lib\\site-packages\\setuptools\\dist.py\", line 34, in <module>\n",
      "        from ._importlib import metadata\n",
      "      File \"C:\\Users\\XUQIAO~1\\AppData\\Local\\Temp\\pip-build-env-00s4sk70\\Lib\\site-packages\\setuptools\\_importlib.py\", line 39, in <module>\n",
      "        disable_importlib_metadata_finder(metadata)\n",
      "      File \"C:\\Users\\XUQIAO~1\\AppData\\Local\\Temp\\pip-build-env-00s4sk70\\Lib\\site-packages\\setuptools\\_importlib.py\", line 30, in disable_importlib_metadata_finder\n",
      "        for ob in sys.meta_path\n",
      "      File \"C:\\Users\\XUQIAO~1\\AppData\\Local\\Temp\\pip-build-env-00s4sk70\\Lib\\site-packages\\setuptools\\_importlib.py\", line 31, in <listcomp>\n",
      "        if isinstance(ob, importlib_metadata.MetadataPathFinder)\n",
      "    AttributeError: module 'importlib_metadata' has no attribute 'MetadataPathFinder'\n",
      "    \n",
      "    ----------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Command \"python setup.py egg_info\" failed with error code 1 in C:\\Users\\XUQIAO~1\\AppData\\Local\\Temp\\pip-install-0yzomwq1\\ibm-db\\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting ipython-sql\n",
      "  Downloading https://files.pythonhosted.org/packages/f3/e4/39dff0f3d426d2aab04fba6f10edf57567656dbd9f760f097f4fd56de204/ipython_sql-0.4.1-py3-none-any.whl\n",
      "Requirement already satisfied: ipython>=1.0 in c:\\users\\xu qiao qi\\anaconda3\\lib\\site-packages (from ipython-sql) (7.2.0)\n",
      "Collecting prettytable<1 (from ipython-sql)\n",
      "  Downloading https://files.pythonhosted.org/packages/ef/30/4b0746848746ed5941f052479e7c23d2b56d174b82f4fd34a25e389831f5/prettytable-0.7.2.tar.bz2\n",
      "Requirement already satisfied: ipython-genutils>=0.1.0 in c:\\users\\xu qiao qi\\anaconda3\\lib\\site-packages (from ipython-sql) (0.2.0)\n",
      "Requirement already satisfied: sqlalchemy>=0.6.7 in c:\\users\\xu qiao qi\\anaconda3\\lib\\site-packages (from ipython-sql) (1.3.9)\n",
      "Collecting sqlparse (from ipython-sql)\n",
      "  Downloading https://files.pythonhosted.org/packages/97/d3/31dd2c3e48fc2060819f4acb0686248250a0f2326356306b38a42e059144/sqlparse-0.4.3-py3-none-any.whl (42kB)\n",
      "Requirement already satisfied: six in c:\\users\\xu qiao qi\\anaconda3\\lib\\site-packages (from ipython-sql) (1.12.0)\n",
      "Requirement already satisfied: pickleshare in c:\\users\\xu qiao qi\\anaconda3\\lib\\site-packages (from ipython>=1.0->ipython-sql) (0.7.5)\n",
      "Requirement already satisfied: backcall in c:\\users\\xu qiao qi\\anaconda3\\lib\\site-packages (from ipython>=1.0->ipython-sql) (0.1.0)\n",
      "Requirement already satisfied: jedi>=0.10 in c:\\users\\xu qiao qi\\anaconda3\\lib\\site-packages (from ipython>=1.0->ipython-sql) (0.13.2)\n",
      "Requirement already satisfied: pygments in c:\\users\\xu qiao qi\\anaconda3\\lib\\site-packages (from ipython>=1.0->ipython-sql) (2.3.1)\n",
      "Requirement already satisfied: setuptools>=18.5 in c:\\users\\xu qiao qi\\anaconda3\\lib\\site-packages (from ipython>=1.0->ipython-sql) (41.0.1)\n",
      "Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in c:\\users\\xu qiao qi\\anaconda3\\lib\\site-packages (from ipython>=1.0->ipython-sql) (2.0.7)\n",
      "Requirement already satisfied: colorama; sys_platform == \"win32\" in c:\\users\\xu qiao qi\\anaconda3\\lib\\site-packages (from ipython>=1.0->ipython-sql) (0.4.1)\n",
      "Requirement already satisfied: decorator in c:\\users\\xu qiao qi\\anaconda3\\lib\\site-packages (from ipython>=1.0->ipython-sql) (4.3.0)\n",
      "Requirement already satisfied: traitlets>=4.2 in c:\\users\\xu qiao qi\\anaconda3\\lib\\site-packages (from ipython>=1.0->ipython-sql) (4.3.2)\n",
      "Requirement already satisfied: parso>=0.3.0 in c:\\users\\xu qiao qi\\anaconda3\\lib\\site-packages (from jedi>=0.10->ipython>=1.0->ipython-sql) (0.3.1)\n",
      "Requirement already satisfied: wcwidth in c:\\users\\xu qiao qi\\anaconda3\\lib\\site-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython>=1.0->ipython-sql) (0.1.7)\n",
      "Building wheels for collected packages: prettytable\n",
      "  Running setup.py bdist_wheel for prettytable: started\n",
      "  Running setup.py bdist_wheel for prettytable: finished with status 'done'\n",
      "  Stored in directory: C:\\Users\\Xu Qiao Qi\\AppData\\Local\\pip\\Cache\\wheels\\80\\34\\1c\\3967380d9676d162cb59513bd9dc862d0584e045a162095606\n",
      "Successfully built prettytable\n",
      "Installing collected packages: prettytable, sqlparse, ipython-sql\n",
      "Successfully installed ipython-sql-0.4.1 prettytable-0.7.2 sqlparse-0.4.3\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": 2,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connection info needed in SQLAlchemy format, example:\n",
      "               postgresql://username:password@hostname/dbname\n",
      "               or an existing connection: dict_keys([])\n",
      "Can't load plugin: sqlalchemy.dialects:ibm_db_sa\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://cmm40461:AIUzVMpDJlyoQboT@9938aec0-8105-433e-8bf9-0fbb7e483086.c1ogj3sd0tgtu0lqde00.databases.appdomain.cloud:32459/bludb?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": 4,
   "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\n",
    "select distinct(Launch_Site) from spaceX"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%sql\n",
    "select * from spaceX 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\n",
    "select sum(PAYLOAD_MASS__KG_) as total_payload_mass_NASA_CRS from spaceX 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\n",
    "select avg(PAYLOAD_MASS__KG_) as average_payload_mass_F9_v1_1 from spaceX 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\n",
    "select min(Date) as first_success_on_ground_pad from spaceX 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\n",
    "select Booster_Version from spaceX \n",
    "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\n",
    "select count(Landing__Outcome) as total_outcomes from spaceX"
   ]
  },
  {
   "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\n",
    "select Booster_Version from spaceX where PAYLOAD_MASS__KG_ = (select max(PAYLOAD_MASS__KG_) from spaceX)"
   ]
  },
  {
   "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\n",
    "select Booster_Version, Launch_Site from spaceX where Landing__Outcome = 'Failure (drone ship)' and year(Date) = '2015'"
   ]
  },
  {
   "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\n",
    "select Landing__Outcome, count(Landing__Outcome) as counts from spaceX \n",
    "where Date > '06-04-2010' and Date < '03-20-2017' group by Landing__Outcome order by counts 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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01&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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01&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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01&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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">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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