week2_EDA_with_SQL.ipynb

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    "<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": [
    "### 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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">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"
   ]
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     "text": [
      "Collecting sqlalchemy==1.3.9\n",
      "  Downloading SQLAlchemy-1.3.9.tar.gz (6.0 MB)\n",
      "\u001b[K     |████████████████████████████████| 6.0 MB 4.2 MB/s eta 0:00:01\n",
      "\u001b[?25hBuilding wheels for collected packages: sqlalchemy\n",
      "  Building wheel for sqlalchemy (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for sqlalchemy: filename=SQLAlchemy-1.3.9-cp39-cp39-macosx_11_0_arm64.whl size=1155188 sha256=3eb001073f36f5d186d4e4ba463473dd7696700afb88b1ba6a679e466267c8f3\n",
      "  Stored in directory: /Users/ap/Library/Caches/pip/wheels/5b/43/0d/de1699809f9e6aaa54a97275298fa07075cb19acc557b18955\n",
      "Successfully built sqlalchemy\n",
      "Installing collected packages: sqlalchemy\n",
      "Successfully installed sqlalchemy-1.3.9\n",
      "Collecting ibm_db_sa\n",
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      "\u001b[?25h  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h  Installing backend dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h    Preparing wheel metadata ... \u001b[?25ldone\n",
      "\u001b[?25hBuilding wheels for collected packages: ibm-db\n",
      "  Building wheel for ibm-db (PEP 517) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for ibm-db: filename=ibm_db-3.1.1-cp39-cp39-macosx_11_0_arm64.whl size=32832276 sha256=3ee63fa0999be276ceffb836c6a6890124e780dd812fa1fbcab23462a36b32ee\n",
      "  Stored in directory: /Users/ap/Library/Caches/pip/wheels/6d/bc/32/4d6aea1a8aeb16c77cd00d26a35435498d4fa8830ccfc96cec\n",
      "Successfully built ibm-db\n",
      "Installing collected packages: ibm-db, ibm-db-sa\n",
      "Successfully installed ibm-db-3.1.1 ibm-db-sa-0.3.8\n",
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      "  Downloading sqlparse-0.4.2-py3-none-any.whl (42 kB)\n",
      "\u001b[K     |████████████████████████████████| 42 kB 4.6 MB/s  eta 0:00:01\n",
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      "Building wheels for collected packages: prettytable\n",
      "  Building wheel for prettytable (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for prettytable: filename=prettytable-0.7.2-py3-none-any.whl size=13714 sha256=9f51a6b576a5e892c9fb45464836dc56d60127049df69cacbe842f7b845ed5ed\n",
      "  Stored in directory: /Users/ap/Library/Caches/pip/wheels/75/f7/28/77a076f1fa8cbeda61aca712815d04d7a32435f04a26a2dd7b\n",
      "Successfully built prettytable\n",
      "Installing collected packages: sqlparse, prettytable, ipython-sql\n",
      "Successfully installed ipython-sql-0.4.0 prettytable-0.7.2 sqlparse-0.4.2\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": 3,
   "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": 6,
   "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",
      "dlopen(/Users/ap/miniforge3/envs/machine_learning/lib/python3.9/site-packages/ibm_db.cpython-39-darwin.so, 0x0002): symbol not found in flat namespace '_SQLAllocHandle'\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://
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Cell magic `%%sql` not found.\n"
     ]
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "select unique(launch_site) from SPACXTBL;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
    "\n",
    "select DISTINCT launch_site from SPACXTBL;"
   ]
  },
  {
   "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": 6,
   "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 * from SPACXTBL\n",
    "where launch_site LIKE 'CCA%'\n",
    "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": 7,
   "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 customer, sum(payload_mass__kg_) as sum_payload from spacxtbl\n",
    "where customer = 'NASA (CRS)'\n",
    "GROUP BY customer;"
   ]
  },
  {
   "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": 8,
   "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 booster_version, AVG(payload_mass__kg_) as avg_payload_mass\n",
    "from spacxtbl\n",
    "where booster_version = 'F9 v1.1'\n",
    "GROUP BY booster_version;"
   ]
  },
  {
   "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": 9,
   "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 min(DATE) from spacxtbl\n",
    "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": 10,
   "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",
    "\n",
    "select booster_version\n",
    "from spacxtbl\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": 11,
   "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 mission_outcome, count(*)\n",
    "from spacxtbl\n",
    "group by mission_outcome;\n"
   ]
  },
  {
   "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": 12,
   "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 booster_version\n",
    "from spacxtbl\n",
    "where payload_mass__kg_ = (select max(payload_mass__kg_) from spacxtbl);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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 * from spacxtbl\n",
    "limit 1;"
   ]
  },
  {
   "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": 14,
   "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 landing__outcome, booster_version, launch_site\n",
    "from spacxtbl \n",
    "where YEAR(DATE) = 2015 AND 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": 15,
   "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 landing__outcome, count(*)\n",
    "from spacxtbl \n",
    "group by landing__outcome\n",
    "order by count(*) 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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