{
  "block": 106146806,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "7119.355915 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2026-05-18T03:03:57",
  "trx_id": "f9e194cf5a4524f8c2bfec4be5a14776a1427a24",
  "trx_in_block": 0,
  "virtual_op": 0
}

{
  "block": 105989333,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "4407.145510 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2026-05-12T15:13:06",
  "trx_id": "325ff2c8d8335e605cc1bd7a38dd77a7f820930a",
  "trx_in_block": 3,
  "virtual_op": 0
}

{
  "block": 105514382,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "7131.871671 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2026-04-26T02:20:36",
  "trx_id": "ea7ef7f6f85a29e1c29437420585e3a6a2345cab",
  "trx_in_block": 0,
  "virtual_op": 0
}

{
  "block": 102860824,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "4448.692329 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2026-01-23T15:19:21",
  "trx_id": "f0c0d38e6bb61e2957f807619896b3cac0afc6b0",
  "trx_in_block": 1,
  "virtual_op": 0
}

{
  "block": 91307117,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "4612.911526 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2024-12-17T10:33:27",
  "trx_id": "7ecf128f60f32fcf4f778f466df9470c588048bd",
  "trx_in_block": 3,
  "virtual_op": 0
}

{
  "block": 79861302,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "4782.045058 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2023-11-14T02:15:42",
  "trx_id": "9c95f28fb9ed18aed7934827f9fa0b6e08c958d3",
  "trx_in_block": 2,
  "virtual_op": 0
}

{
  "block": 78351782,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "7719.323844 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2023-09-22T01:07:57",
  "trx_id": "a7cd0f1af6fccdb14737da8b797c99aab5043443",
  "trx_in_block": 1,
  "virtual_op": 0
}

{
  "block": 69116622,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "7941.005282 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2022-11-03T14:31:09",
  "trx_id": "b4f399e53dfa4674d7bcf41287f668c88c4df24f",
  "trx_in_block": 30,
  "virtual_op": 0
}

{
  "block": 60817602,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "8161.240418 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2022-01-17T17:48:45",
  "trx_id": "774aba8a84a7a623daca6003f59609a87d8bc2d4",
  "trx_in_block": 18,
  "virtual_op": 0
}

{
  "block": 54610751,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "8345.307171 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2021-06-14T03:21:09",
  "trx_id": "6b8f2b97dff7954febd046b86182ffe6da270da8",
  "trx_in_block": 2,
  "virtual_op": 0
}

{
  "block": 49358116,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "8532.729145 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-12-11T13:36:51",
  "trx_id": "c998aaefc9fa728fe9a137aac61a28e0987080e8",
  "trx_in_block": 5,
  "virtual_op": 0
}

{
  "block": 49209657,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "1912.543513 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-12-06T07:13:15",
  "trx_id": "4f95bcedf02ce03d174c5dd1d9afdbd85d34e0a7",
  "trx_in_block": 3,
  "virtual_op": 0
}

{
  "block": 49193204,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "8538.936999 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-12-05T17:14:48",
  "trx_id": "c58d433e85f316c97fb30189a9f69f41965208c7",
  "trx_in_block": 10,
  "virtual_op": 0
}

{
  "block": 48263866,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "1920.017158 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-11-02T20:47:18",
  "trx_id": "ebf951ceb4bb8f3330564d4ea001d37791eeb80e",
  "trx_in_block": 1,
  "virtual_op": 0
}

{
  "block": 43219944,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "8741.742358 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-05-09T08:13:24",
  "trx_id": "904d11da64380175ba4734c1d14970fec1c228c6",
  "trx_in_block": 1,
  "virtual_op": 0
}

{
  "block": 43196482,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "1953.311140 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-05-08T12:11:54",
  "trx_id": "84c6c8e21ec65323bc62ca20ba608be07370063f",
  "trx_in_block": 13,
  "virtual_op": 0
}

{
  "block": 42567053,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "8754.629806 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-04-16T01:29:12",
  "trx_id": "9b2be52b46e093d5685ddfbc7509b604b8240d89",
  "trx_in_block": 5,
  "virtual_op": 0
}

{
  "block": 39696073,
  "op": [
    "comment",
    {
      "author": "steemitboard",
      "body": "Congratulations @longwhitecoat! You received a personal award!\n\n<table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@longwhitecoat/birthday2.png</td><td>Happy Birthday! - You are on the Steem blockchain for 2 years!</td></tr></table>\n\n<sub>_You can view [your badges on your Steem Board](https://steemitboard.com/@longwhitecoat) and compare to others on the [Steem Ranking](https://steemitboard.com/ranking/index.php?name=longwhitecoat)_</sub>\n\n\n###### [Vote for @Steemitboard as a witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1) to get one more award and increased upvotes!",
      "json_metadata": "{\"image\":[\"https://steemitboard.com/img/notify.png\"]}",
      "parent_author": "longwhitecoat",
      "parent_permlink": "twitter-sentiment-analysis-try-it-out",
      "permlink": "steemitboard-notify-longwhitecoat-20200106t164133000z",
      "title": ""
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-01-06T16:41:33",
  "trx_id": "8cccc921a211eca5142e74f494eee816abf70083",
  "trx_in_block": 9,
  "virtual_op": 0
}

{
  "block": 32849906,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "8950.246619 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-05-12T18:35:12",
  "trx_id": "33c8a44d6875493413167f6db5b74feb636f8a6f",
  "trx_in_block": 24,
  "virtual_op": 0
}

{
  "block": 29223870,
  "op": [
    "comment",
    {
      "author": "steemitboard",
      "body": "Congratulations @longwhitecoat! You received a personal award!\n\n<table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@longwhitecoat/birthday1.png</td><td>1 Year on Steemit</td></tr></table>\n\n<sub>_[Click here to view your Board](https://steemitboard.com/@longwhitecoat)_</sub>\n\n\n> Support [SteemitBoard's project](https://steemit.com/@steemitboard)! **[Vote for its witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1)** and **get one more award**!",
      "json_metadata": "{\"image\":[\"https://steemitboard.com/img/notify.png\"]}",
      "parent_author": "longwhitecoat",
      "parent_permlink": "twitter-sentiment-analysis-try-it-out",
      "permlink": "steemitboard-notify-longwhitecoat-20190106t172508000z",
      "title": ""
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-01-06T17:25:09",
  "trx_id": "1ccb39817605a3f467fd20b4628cf3e7b96c7537",
  "trx_in_block": 10,
  "virtual_op": 0
}

{
  "block": 26717635,
  "op": [
    "vote",
    {
      "author": "longwhitecoat",
      "permlink": "what-is-siem-what-sort-of-cybersecurity-risks-does-it-prevent",
      "voter": "gbanksreps",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-10-11T15:28:39",
  "trx_id": "6467588269a31c164e3eceacb6a033235e7a7073",
  "trx_in_block": 15,
  "virtual_op": 0
}

{
  "block": 22492638,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "9149.857487 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-05-16T22:36:51",
  "trx_id": "7d317e804628ba9073928e7b58d1f50a40ed317c",
  "trx_in_block": 70,
  "virtual_op": 0
}

{
  "block": 19410264,
  "op": [
    "custom_json",
    {
      "id": "follow",
      "json": "[\"follow\",{\"follower\":\"longwhitecoat\",\"following\":\"essentia.one\",\"what\":[\"blog\"]}]",
      "required_auths": [],
      "required_posting_auths": [
        "longwhitecoat"
      ]
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-29T19:10:33",
  "trx_id": "17f5ff398272f17b4a29dda22660f71a417b4ca8",
  "trx_in_block": 15,
  "virtual_op": 0
}

{
  "block": 19410263,
  "op": [
    "custom_json",
    {
      "id": "follow",
      "json": "[\"follow\",{\"follower\":\"longwhitecoat\",\"following\":\"flypme\",\"what\":[\"blog\"]}]",
      "required_auths": [],
      "required_posting_auths": [
        "longwhitecoat"
      ]
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-29T19:10:30",
  "trx_id": "4ec15d0027d9e8c8b3f1ad5e2096ae28c04a06e2",
  "trx_in_block": 23,
  "virtual_op": 0
}

{
  "block": 18947210,
  "op": [
    "author_reward",
    {
      "author": "longwhitecoat",
      "permlink": "modern-data-scientist",
      "sbd_payout": "0.000 SBD",
      "steem_payout": "0.000 STEEM",
      "vesting_payout": "14.335350 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-13T17:06:48",
  "trx_id": "0000000000000000000000000000000000000000",
  "trx_in_block": 4294967295,
  "virtual_op": 23
}

{
  "block": 18806277,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "longwhitecoat",
      "delegator": "steem",
      "vesting_shares": "29700.696109 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-08T19:32:45",
  "trx_id": "686c0f3e668315d3ca17cc59409eefdfde12cf4f",
  "trx_in_block": 18,
  "virtual_op": 0
}

{
  "block": 18778402,
  "op": [
    "vote",
    {
      "author": "longwhitecoat",
      "permlink": "what-is-a-neural-network-deep-machine-learning",
      "voter": "dj-on-steem",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-07T20:16:54",
  "trx_id": "13bdd390281896e52c5601f38149cee9f4e3ee54",
  "trx_in_block": 0,
  "virtual_op": 0
}

{
  "block": 18751064,
  "op": [
    "comment",
    {
      "author": "cheetah",
      "body": "Hi! I am a robot. I just upvoted you! I found similar content that readers might be interested in:\nhttps://thegrid.ai/big-data-analytics/",
      "json_metadata": "",
      "parent_author": "longwhitecoat",
      "parent_permlink": "modern-data-scientist",
      "permlink": "cheetah-re-longwhitecoatmodern-data-scientist",
      "title": ""
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T21:28:15",
  "trx_id": "b9660a9b5f09513f0fa9040fada8c56fb30bb8c5",
  "trx_in_block": 38,
  "virtual_op": 0
}

{
  "block": 18751063,
  "op": [
    "vote",
    {
      "author": "longwhitecoat",
      "permlink": "modern-data-scientist",
      "voter": "cheetah",
      "weight": 8
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T21:28:12",
  "trx_id": "a9bc8eaad113f5e4bb71b27ee48e3e5997f8c3a0",
  "trx_in_block": 9,
  "virtual_op": 0
}

{
  "block": 18747130,
  "op": [
    "comment",
    {
      "author": "longwhitecoat",
      "body": "@@ -568,15 +568,17 @@\n t's \n+*\n implied\n+*\n  tha\n@@ -681,17 +681,18 @@\n mplement\n-s\n+ed\n  my frie\n@@ -697,16 +697,8 @@\n iend\n-'s taste\n , an\n@@ -813,16 +813,18 @@\n hink of \n+**\n one nice\n@@ -825,24 +825,26 @@\n e nice thing\n+**\n  to say in r\n@@ -852,16 +852,45 @@\n sponse. \n+EVERYONE noticed the silence.\n %0A%0AAnyway\n",
      "json_metadata": "{\"tags\":[\"twitter\",\"sentiment\",\"analysis\",\"positive\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}",
      "parent_author": "",
      "parent_permlink": "twitter",
      "permlink": "twitter-sentiment-analysis-try-it-out",
      "title": "Twitter Sentiment Analysis: Try it out!"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T18:11:15",
  "trx_id": "27636bad83d9ab5bc642f982d92a705026e292bc",
  "trx_in_block": 11,
  "virtual_op": 0
}

{
  "block": 18747105,
  "op": [
    "comment",
    {
      "author": "longwhitecoat",
      "body": "@@ -518,20 +518,16 @@\n  by the \n-the \n latter p\n@@ -541,16 +541,26 @@\n  wisdom \n+in action \n because \n",
      "json_metadata": "{\"tags\":[\"twitter\",\"sentiment\",\"analysis\",\"positive\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}",
      "parent_author": "",
      "parent_permlink": "twitter",
      "permlink": "twitter-sentiment-analysis-try-it-out",
      "title": "Twitter Sentiment Analysis: Try it out!"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T18:10:00",
  "trx_id": "248a0f89eb45e6225176a10cda1fc4a9c9b24295",
  "trx_in_block": 29,
  "virtual_op": 0
}

{
  "block": 18747092,
  "op": [
    "comment",
    {
      "author": "longwhitecoat",
      "body": "@@ -952,8 +952,49 @@\n ccurate*\n+%0A%0A**Link** pypystudent.pythonanywhere.com\n",
      "json_metadata": "{\"tags\":[\"twitter\",\"sentiment\",\"analysis\",\"positive\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}",
      "parent_author": "",
      "parent_permlink": "twitter",
      "permlink": "twitter-sentiment-analysis-try-it-out",
      "title": "Twitter Sentiment Analysis: Try it out!"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T18:09:21",
  "trx_id": "f150a72dedf5d269c17fdb2ea9e7c7c7707aafe4",
  "trx_in_block": 21,
  "virtual_op": 0
}

{
  "block": 18747061,
  "op": [
    "vote",
    {
      "author": "longwhitecoat",
      "permlink": "twitter-sentiment-analysis-try-it-out",
      "voter": "longwhitecoat",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T18:07:48",
  "trx_id": "07d0f5be51d12a324c4674240f799546656945c4",
  "trx_in_block": 39,
  "virtual_op": 0
}

{
  "block": 18747061,
  "op": [
    "comment_options",
    {
      "allow_curation_rewards": true,
      "allow_votes": true,
      "author": "longwhitecoat",
      "extensions": [],
      "max_accepted_payout": "1000000.000 SBD",
      "percent_steem_dollars": 0,
      "permlink": "twitter-sentiment-analysis-try-it-out"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T18:07:48",
  "trx_id": "07d0f5be51d12a324c4674240f799546656945c4",
  "trx_in_block": 39,
  "virtual_op": 0
}

{
  "block": 18747061,
  "op": [
    "comment",
    {
      "author": "longwhitecoat",
      "body": "This was a fun mini project with two friends from the Hopkins MBA/MA design program. Nothing special, just type in a twitter handle and the back end uses a vanilla ML algo to analyze the text of the past several tweets and tells you how positive the tweeter is. After all, isn't being positive a virtue? \n\nIn the spirit of philosophical teaching, it is said that speech is not meant to cause suffering in others. Other inherited wisdom says if you have nothing to say don't say anything at all. I'm always weirded out by the the latter piece of wisdom because it's implied that you have nothing nice to say when you say nothing. I've been in the same room where someone complements my friend's taste, and my friend looked at the person giving them the compliment and just said nothing. Honestly, they couldn't think of one nice thing to say in response. \n\nAnyways, be nice, be positive, and enjoy this little fun tool. Oh and *WARNING it is not very accurate*",
      "json_metadata": "{\"tags\":[\"twitter\",\"sentiment\",\"analysis\",\"positive\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}",
      "parent_author": "",
      "parent_permlink": "twitter",
      "permlink": "twitter-sentiment-analysis-try-it-out",
      "title": "Twitter Sentiment Analysis: Try it out!"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T18:07:48",
  "trx_id": "07d0f5be51d12a324c4674240f799546656945c4",
  "trx_in_block": 39,
  "virtual_op": 0
}

{
  "block": 18746734,
  "op": [
    "vote",
    {
      "author": "longwhitecoat",
      "permlink": "what-is-siem-what-sort-of-cybersecurity-risks-does-it-prevent",
      "voter": "longwhitecoat",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T17:51:24",
  "trx_id": "2d925ae8ddd8e31a2d24ea6ddd490531430aa584",
  "trx_in_block": 5,
  "virtual_op": 0
}

{
  "block": 18746734,
  "op": [
    "comment_options",
    {
      "allow_curation_rewards": true,
      "allow_votes": true,
      "author": "longwhitecoat",
      "extensions": [],
      "max_accepted_payout": "1000000.000 SBD",
      "percent_steem_dollars": 0,
      "permlink": "what-is-siem-what-sort-of-cybersecurity-risks-does-it-prevent"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T17:51:24",
  "trx_id": "2d925ae8ddd8e31a2d24ea6ddd490531430aa584",
  "trx_in_block": 5,
  "virtual_op": 0
}

{
  "block": 18746734,
  "op": [
    "comment",
    {
      "author": "longwhitecoat",
      "body": "https://youtu.be/Hlpoiyu9XLw\n\n**TLDR readers:** Security Information and Event Management is a software tool that can be used by Information security to detect event anomalies in the system. It requires setting up the right parameters and therefore an experienced CSIO or other company officer. I go through the more common use cases for SIEM including brute force attacks and compromised log source events. \n\n*SIEM stands for Security Information and Event Management.* Because information security (also known by the more popularized term cybersecurity) has developed enough complexity over the years that there are now specialized SIEM positions in every major corporation. This is partly due to the amount of data generated, stored, and transferred. Big data incentivizes security threats, and if a company is ever going to secure it’s data, than SIEM is already part of its corporate structure. Sometimes, not enough resources are spent in this department as multi-billion dollar companies still fall victim to cyber breaches. Then again, the larger the data set, the more it would attract highly skilled hackers. The tools used to aid the SIEM professional have also evolved to keep up with the continued resource allocation into this field, but first let us understand what is involved in SIEM. \n\n![](https://steemitimages.com/DQmenhyunKWmrpyQRanrshVvDgarGhj68JRedmgd7KTX19t/image.png)\n\nMichael Oberlaender’s book “C[I]SO-And Now What? How to Successfully Build Security by Design” provides frameworks for security architecture based on his experience as a Chief Information Security Officer. IT ‘stacks’ are ubiquitous and refer to a group of technologies that work well together to produce a desired outcome. In this case, Oberlaender defines a 10 layer Security Stack as shown above. Essentially an easy guide to troubleshoot if the desired outcome is not produced. Is it a physical problem? Is the network breached? are config settings for the presentation not compatible? Is the user’s behavior the cause of information compromise? Now that we have a general idea of all the layers where things can go wrong from the physical layer up to IT policies and procedures, we need to find a way to (1) prevent, (2) monitor, and (3) correct. The monitoring function of the SIEM professional or advanced SIEM tool is also known as ‘Event and Log Collection’ and has some build-in alerting function if compromise is detected. This monitoring function is in the form of an analytics dashboard that monitors all activity in a secure system and based on pre-specified events, it alerts the SIEM professional. The SIEM professional takes this information and prevents cyberattacks by placing safeguards. The simplest example is limiting the number of login attempts by a user. If an event is recorded, the SIEM professional then must find out how it can be prevented further in the future. As you can see this is an iterative process, and why experience is one part of the capability of a SIEM professional. So prevent, monitor, and correct seem straightforward, so does event logging software with a useful analytics dashboard. Let’s look at a few use cases:\n\n1) Brute Force Attack: some hackers use a brute force trial and error method (by machine ofcourse) to decode encrypted data. Auditing a system can count the number of logs a user attempts, and depending on any safeguards can prevent the user from attempting to log-in, or can alert the system administrator of the multiple attempts made. Brute Force Attack applications can even be used to test one’s own network security. John the Ripper is one example < http://sectools.org/tool/john/>.\n\n2) Authorized User, but Unauthorized use: This happens a lot in an organization, and can be safeguarded by limiting access (for example, a neurology physician cannot access the outpatient list of general surgery patients), or by monitoring. AUM or acceptable use monitoring logs a user’s activity or logging activity of a specific asset or resource (again, many medical records now record the user and the date and time of access). Actions can also be audited. If a user deletes a file, copies a file, etc. there will be a digitalprint left behind. The simplest form of AUM safeguards that the millennial generation has seen is trying to access our twitter account from work or from a public library and getting a browser page that says ‘unauthorized’.\n\n3) Application Defense Check: It’s easier to set up monitoring of systems and networks than individual applications, but given the amount of big data stored in applications like SQL, or in Hadoop clusters, it becomes important to monitor and protect against unauthorized use at the application level. The most common safeguard is use of a Web Application Firewall (WAF) that monitors traffic and prevents traffic based on administrator defined specifications.\n\n4) Compromised Log Source: Imagine that a hacker found access to your system or to a specific application and quickly disabled the event log monitoring of the light weight SIEM agent software installed. As a safeguard for such an event, SIEM software is configured in such a way to alert the main manager system (that collects the log feeds) when the log feeds stop arriving, thereby alerting to a potential breech or potential malfunction.\n\n5) Unexpected Events Per Second (EPS): if the log sources are sending a large number of events per second, this should raise an alert, as it is one way hackers have found to move undetected. They flood the manager system with fake information so that the bandwidth is bogged down and the real-time analysis dashboard is more of an after-time analysis allowing the hacker to get away before a system alert is raised or action such as disabling the system from taking place. This flood of information to decrease bandwidth is a common tactic that we’ve seen in the Distributed Denial of Service (DDoS) attacks < http://www.digitalattackmap.com/understanding-ddos/>.\n\nThere are many more use cases, but I only aim to elucidate a concept. The tools at the disposal for SIEM and for the SIEM professional are impressive and continue to get more sophisticated. Splunk and IBM QRadar are just two tools used for Information Security of Big Data. Below are two company marketing videos to showcase their value proposition. Keep in mind that although SIEM is technically all encompassing, the acronym is usually used to describe recording current or past events, while ‘threat intelligence’ looks to the future and uses actionable intel to safeguard a company’s IT assets. Tools such as Splunk and QRadar can do both. Caution: The ads are not very appealing, but maybe they resonated with the targeted audience.\n\nhttps://youtu.be/5l23cBGOD7M\n\nhttps://youtu.be/f4b-IKgPXxA\n\nReferenced Links:\n\n- Joe Piggee Sr. What Is a SIEM? Tripwire. Accessed from <https://www.tripwire.com/state-of-security/incident-detection/log-management-siem/what-is-a-siem/>\n\n- Michael S. Oberlaender (2013). Book excerpt: ‘C[I]SP: And Now What?”. Accessed from < http://www.csoonline.com/article/2133110/security-awareness/book-excerpt---c-i-so--and-now-what--.html>.\n\n- Gartner IT Glossary definition link: http://www.gartner.com/it-glossary/security-information-and-event-management-siem/\n\n- InfoSec Institute (2014). Top 6 SIEM Use Cases. Accessed from < resources.infosecinstitute.com/top-6-seim-use-cases>",
      "json_metadata": "{\"tags\":[\"cybersecurity\",\"bigdata\",\"csio\",\"siem\",\"hacker\"],\"image\":[\"https://img.youtube.com/vi/Hlpoiyu9XLw/0.jpg\",\"https://steemitimages.com/DQmenhyunKWmrpyQRanrshVvDgarGhj68JRedmgd7KTX19t/image.png\",\"https://img.youtube.com/vi/5l23cBGOD7M/0.jpg\",\"https://img.youtube.com/vi/f4b-IKgPXxA/0.jpg\"],\"links\":[\"https://youtu.be/Hlpoiyu9XLw\",\"http://sectools.org/tool/john/\",\"http://www.digitalattackmap.com/understanding-ddos/\",\"https://youtu.be/5l23cBGOD7M\",\"https://youtu.be/f4b-IKgPXxA\",\"https://www.tripwire.com/state-of-security/incident-detection/log-management-siem/what-is-a-siem/\",\"http://www.csoonline.com/article/2133110/security-awareness/book-excerpt---c-i-so--and-now-what--.html\",\"http://www.gartner.com/it-glossary/security-information-and-event-management-siem/\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}",
      "parent_author": "",
      "parent_permlink": "cybersecurity",
      "permlink": "what-is-siem-what-sort-of-cybersecurity-risks-does-it-prevent",
      "title": "What is SIEM? What sort of cybersecurity risks does it prevent?"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T17:51:24",
  "trx_id": "2d925ae8ddd8e31a2d24ea6ddd490531430aa584",
  "trx_in_block": 5,
  "virtual_op": 0
}

{
  "block": 18746725,
  "op": [
    "vote",
    {
      "author": "longwhitecoat",
      "permlink": "what-is-a-neural-network-deep-machine-learning",
      "voter": "makcum52",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T17:50:54",
  "trx_id": "22c1843d6907573999c5e5912347fa217c4137e0",
  "trx_in_block": 19,
  "virtual_op": 0
}

{
  "block": 18746598,
  "op": [
    "vote",
    {
      "author": "longwhitecoat",
      "permlink": "what-is-a-neural-network-deep-machine-learning",
      "voter": "longwhitecoat",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T17:44:33",
  "trx_id": "04dc54b7fbd35a155606abe27670b3df5bf432fa",
  "trx_in_block": 41,
  "virtual_op": 0
}

{
  "block": 18746598,
  "op": [
    "comment_options",
    {
      "allow_curation_rewards": true,
      "allow_votes": true,
      "author": "longwhitecoat",
      "extensions": [],
      "max_accepted_payout": "1000000.000 SBD",
      "percent_steem_dollars": 0,
      "permlink": "what-is-a-neural-network-deep-machine-learning"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T17:44:33",
  "trx_id": "04dc54b7fbd35a155606abe27670b3df5bf432fa",
  "trx_in_block": 41,
  "virtual_op": 0
}

{
  "block": 18746598,
  "op": [
    "comment",
    {
      "author": "longwhitecoat",
      "body": "![](https://steemitimages.com/DQmc2dcHb6t76fAby7t6pF9U5wwodWc1ob2JmpDZhoX8CWB/image.png)\n\nhttps://youtu.be/bxe2T-V8XRs\n\n**TLDR readers (ages 10 and up):** Think of your brain as having computational ability. Talking to your pet cat or dog might use a few neurons (or more than a few, I don't judge--it's all relative). Trying to understand machine learning algorithms from a dry textbook might use up a few more neurons. Hence the more neurons you use the more computational power you need to solve the problem facing you whether it is getting an animal's attention or understanding technical jargon. In machine learning, the smallest unit of computational power can be thought of as a neuron where a set of input produces some output. The output could be a classification (Yes spam, No not spam). The accuracy is 60%. To increase the accuracy we need more computational power so we use the output of neuron 1 as input for neuron 2 and the accuracy goes up. Still it's only 61% so we keep going until marginally, there's minimal gain in accuracy. All these neurons together are a neural network just like our brain. This is over simplified and a neural net is only one algorithm to solve a problem, there are many many others, and many are beyond the scope of my interest. For the technical explanation, see a few scrolls down :)\n\nBig data is large enough that a computer with machine learning algorithms could find patterns much faster than humans ever could. Machine learning is the automated stepping stone to eventual artificial intelligence. Many of the things we think of as artificial intelligence such as self-driving vehicles are essentially a cluster of machine learning algorithms. Philosophically, we don’t have true artificial intelligence yet, but the moniker is easily adopted because of the fascinating ‘intelligent’ tasks the machine is capable of.\n\nThink of any mind numbing repetitive task, and a machine can do it with 100% accuracy, and things that have require analyzing new information that is very similar to the information it trained on will also have high accuracy >95%. For example using a data set of 10,000-20,000 handwritten digits(1-9) training set can give the machine an accuracy greater than 97% to recognize handwritten digits in the future.\n\nNarrative Science, a Chicago based company automates writing of news reports. Kristian Hammond, the Chief Scientist at Narrative Science believes that we are only a little over a decade away where **more than 90% of news will be written by machines analyzing data**. Today, our state and national weather reports are mostly machine written generated after analyzing government provided data from organizations like the National Oceanic And Atmospheric Administration (NOAA). Kensho, a company in Cambridge automates algorithmic analysis in hedge funds typically done by ‘quants’ or the model building financial analysts. In fact, while a quant typically builds 1-2 models per week, a machine can built thousands of models per week. Similarly, IBM Watson scans hundreds of thousands of health records for medical insights in a matter of seconds compared to the weeks it would take a team of medical researchers to gather, analyze, and publish the findings.\n\nNeural Networks are only one form of machine learning algorithm popular because of the ability to improve the accuracy of the algorithm by using deeper and deeper hidden layers (also called deep learning). To do this, we needed to use higher and higher amounts of computational power. That is why, while initially developed in 1960, it only gained popularity recently because of the higher computational ability of graphics cards (GPU—graphical processing units) compared to CPU chips. Similarly mining bitcoin moved from CPU to GPU and now to ASICs. \n\n*WARNING:TECHNICAL EXPLANATION* Neural Networks get their name based off a model of the human neuron, called the perceptron, who perceptron learning rule, first developed by Frank Rosenblatt in 1957 explains the algorithm’s building block. The perceptron learning rule expanded on the McCullock-Pitts (MCP) neuron description of learning. The MCP neuron has a number of inputs but only two outputs. It either fires or it doesn’t. It requires the sum of the inputs to pass a certain threshold before the neuron fires. What the Perceptron learning rule added was the concept of the activation function 𝛷(z). It basically assumes that each input, let’s call it a feature, has a specific weight where the product of the feature and weight would yield some value. This value would then be transformed using the activation function into a non-linear function that the machine can understand such as a logistic function (which takes values between 0 and 1). The number of thresholds depends on the number of final classifications. Email spam, which is a classic example has one threshold and two classifications (spam or not spam). If we use the product of the weights and inputs and *use them* as a second layer of inputs whose weights we need to find using the same or another activation function, then we have essentially created another neural layer. We can keep adding layers but there is diminishing marginal gain in the accuracy of the algorithm and it can get very computationally demanding. The activation function, this function that transforms the product of weights and inputs, is the focus of machine learning research because of the assumption that it yields the highest gain in initial accuracy, thus there is no right activation function. Researchers can try several and see which one yields the best accuracy, then add additional layers to the network to improve the accuracy.\n\n![](https://steemitimages.com/DQmRomn3YBsApWe2UeSRdt4KyRFeowMJn3P51AZVrwLUQNR/image.png)\n\nWhile big data lead to machine learning, and machine learning is leading us to artificial intelligence, we are likely to hang out in the machine learning realm still refining our algorithms and improving our computational ability as well as increasing user adoption for many years to come. Which means we still have time to understand this fascinating world before it becomes overwhelming.\n\nReferenced links:\n\n- Chris Woodford (2016). Neural Networks. Explainthatstuff.com. Accessed from < explainthatstuff.com/introduction-to-neural-networks.html>.\n\n- Brad Power (2015). Artificial Intelligence Is Almost Ready for Business. Harvard Business Review. Accessed from < https://hbr.org/2015/03/artificial-intelligence-is-almost-ready-for-business>.\n\n- Jane Wakefield (2015). Intelligent Machines: The jobs robots will steal first. BBC Technology. Accessed from < http://www.bbc.com/news/technology-33327659>\n\n- NOAA link: https://www.ncdc.noaa.gov/\n\n- National Institute of Standards and Technology (NIST) handwritten digits database: https://srdata.nist.gov/gateway/gateway?keyword=handwriting+recognition",
      "json_metadata": "{\"tags\":[\"neuralnet\",\"bigdata\",\"network\",\"machinelearning\",\"technology\"],\"image\":[\"https://steemitimages.com/DQmc2dcHb6t76fAby7t6pF9U5wwodWc1ob2JmpDZhoX8CWB/image.png\",\"https://img.youtube.com/vi/bxe2T-V8XRs/0.jpg\",\"https://steemitimages.com/DQmRomn3YBsApWe2UeSRdt4KyRFeowMJn3P51AZVrwLUQNR/image.png\"],\"links\":[\"https://youtu.be/bxe2T-V8XRs\",\"https://hbr.org/2015/03/artificial-intelligence-is-almost-ready-for-business\",\"http://www.bbc.com/news/technology-33327659\",\"https://www.ncdc.noaa.gov/\",\"https://srdata.nist.gov/gateway/gateway?keyword=handwriting+recognition\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}",
      "parent_author": "",
      "parent_permlink": "neuralnet",
      "permlink": "what-is-a-neural-network-deep-machine-learning",
      "title": "What is a Neural Network? 'Deep' Machine Learning"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T17:44:33",
  "trx_id": "04dc54b7fbd35a155606abe27670b3df5bf432fa",
  "trx_in_block": 41,
  "virtual_op": 0
}

{
  "block": 18746140,
  "op": [
    "vote",
    {
      "author": "longwhitecoat",
      "permlink": "obama-s-big-data-usd200-million-initiative",
      "voter": "fiky",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T17:21:39",
  "trx_id": "703c0e1bb57b69159d5c6dc11e9cfc3870fd3718",
  "trx_in_block": 18,
  "virtual_op": 0
}

{
  "block": 18746120,
  "op": [
    "vote",
    {
      "author": "longwhitecoat",
      "permlink": "obama-s-big-data-usd200-million-initiative",
      "voter": "longwhitecoat",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T17:20:39",
  "trx_id": "5061d64761c1da7dd29438c352823da2f5d3e487",
  "trx_in_block": 54,
  "virtual_op": 0
}

{
  "block": 18746120,
  "op": [
    "comment_options",
    {
      "allow_curation_rewards": true,
      "allow_votes": true,
      "author": "longwhitecoat",
      "extensions": [],
      "max_accepted_payout": "1000000.000 SBD",
      "percent_steem_dollars": 0,
      "permlink": "obama-s-big-data-usd200-million-initiative"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T17:20:39",
  "trx_id": "5061d64761c1da7dd29438c352823da2f5d3e487",
  "trx_in_block": 54,
  "virtual_op": 0
}

{
  "block": 18746120,
  "op": [
    "comment",
    {
      "author": "longwhitecoat",
      "body": "https://youtu.be/oWYDyWlcICY\n\n**TLDR readers:** Big data is great except when it is centralized. That's when big companies profit. At some point, I'll blog on how we are paying facebook, 23andme, and others to profit off our data without asking anything in return except for dopamine laced hits of entertainment value. For now, let's just see what shape this $200 million initiative took including a a tiny effort (millions not billions) to build open source products/tools for big data. I also include a resource to for *free* datasets.\n\nIn March 29, 2012, the Obama Administration announced what it called a “Big Data Research and Development Initiative\". The initiative is compared to historical funding that brought us the internet with a focus on transforming scientific discovering, environmental and biomedical research, education, and national security. The first batch of this fund will go to federal departments to spend on projects they deem important such as:\n\n**National Institutes of Health (NIH):** funding to manage, analyze, visualize and extract information from large datasets related to health and disease. Specifically, the 1000 Genomes Project (200 terabyte dataset available on the AWS cloud for free). For more information, see aws.amazon.com/1000genomes\n\n**National Science Foundation (NSF):** $10 million ‘Expeditions in Computing’ project by UC Berkeley. Additional $2 million in research grants to train undergraduates in Big Data tools for visualization. And $1.4 million to a focused research group analyzing protein structures and biological pathways.\n\n**Department of Defense:** $60 million towards building autonomous systems as well improving text mining in any language.\n   \n**Defense Advanced Research Projects Agency (DARPA):** $25 million towards the XDATA program to support open source software toolkits to improve computational techniques for analyzing big data.\n   \n **Department of Energy:** $25 million towards the Scalable Data Management, Analysis, and Visualization (SDAV) Institute at UC Berkley (lucky university) which will allow collaboration of six national laboratories and seven universities to use the department’s supercomputers.\n   \n **US Geological Survey: **Grant program through the John Wesley Powell Center for Analysis and Synthesis. \n\nThere are two challenges with investing in Big Data advancements. The first is investing in the right people and the right technology and the second is making sure the data remains free so that more advancements can be made without requiring additional investments. In terms of the latter, the government is trying its best to invest in projects where it will be the ‘owner’ of the data. One of the reasons why electronic medical records, big cloud based platforms, and other technological advances of our time have been slow to innovate is because the data is protected as much as a company’s intellectual property is protected. There is this notion that if a company keeps its data repositories private then it holds a competitive advantage to either A) innovate down the line using its big data, or B) to increase the value of its acquisition because of the potential value inherent in its data repository. To this end, projects like the 1000 genome project are a step in the right direction since it makes data accessible to everyone with a bright idea, not just the already established companies. When you spit into a tube and send it off to 23andme or to ancestry.com (and more recently to helix.com) you are essentially paying for someone else to profit off of your DNA. The initial value proposition will likely be in the form of something interesting but not particularly useful like mapping your ancestry to one of the main continents or telling you what your wine preference is based on your genes. The true value is in the **unlocked secrets your DNA holds** when grouped with thousands of others. When companies don’t have the time to collect all this data, they hire ‘data brokers’ to buy data from them. One example is Acxiom which collects consumer information from their online presence. This opened an opportunity for a different kind of company, Abine.com to remove your information from the web for a fee.\n\nThe silver lining for innovation is that there is enough data to go around. In fact with more than a Quintilian (10^30) bytes generated every day, the world will never run out of data to analyze. That is why everyone from non-profits like Wikipedia, Project Gutenberg, to governments (data.gov, data.gov.uk) provide free datasets to allows researchers, scientists, and startups to build the smart algorithms that could power our future. This is not old news, this is actually a new phenomenon that is taking place for the past 2-3 years.\n\nA list of more than **70 websites with free datasets** is available here <bigdata-madesimple.com/70-websites-to-get-large-data-repositories-for-free>\n\n**Links:**\n\nNSF:nsf.gov/news/news_summ.jsp?cntn_id=123607\n\nHHS/NIH: nih.gov/news/health/mar2012/nhgri-29.htm\n\nDOE:science.energy.gov/news\n\nDOD:www.DefenseInnovationMarketplace.mil\n\nDARPA:darpa.mil/NewsEvents/Releases/2012/03/29.aspx\n\nUSGS:powellcenter.usgs.gov\n\nBuying and Selling of Big Data: cnn.com/2012/08/23/tech/web/big-data-acxiom",
      "json_metadata": "{\"tags\":[\"freedata\",\"bigdata\",\"obama\",\"opensource\",\"decentralization\"],\"image\":[\"https://img.youtube.com/vi/oWYDyWlcICY/0.jpg\"],\"links\":[\"https://youtu.be/oWYDyWlcICY\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}",
      "parent_author": "",
      "parent_permlink": "freedata",
      "permlink": "obama-s-big-data-usd200-million-initiative",
      "title": "Obama’s Big Data $200 Million Initiative"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T17:20:39",
  "trx_id": "5061d64761c1da7dd29438c352823da2f5d3e487",
  "trx_in_block": 54,
  "virtual_op": 0
}

{
  "block": 18745883,
  "op": [
    "vote",
    {
      "author": "longwhitecoat",
      "permlink": "modern-data-scientist",
      "voter": "twitchmoments",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T17:08:48",
  "trx_id": "9e04bd1734d4cc214b64c186f5da798df6775fc4",
  "trx_in_block": 19,
  "virtual_op": 0
}

{
  "block": 18745843,
  "op": [
    "vote",
    {
      "author": "longwhitecoat",
      "permlink": "modern-data-scientist",
      "voter": "longwhitecoat",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T17:06:48",
  "trx_id": "302360dc12b8f432ea94070f014afde26ff5a471",
  "trx_in_block": 51,
  "virtual_op": 0
}

{
  "block": 18745843,
  "op": [
    "comment_options",
    {
      "allow_curation_rewards": true,
      "allow_votes": true,
      "author": "longwhitecoat",
      "extensions": [],
      "max_accepted_payout": "1000000.000 SBD",
      "percent_steem_dollars": 0,
      "permlink": "modern-data-scientist"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T17:06:48",
  "trx_id": "302360dc12b8f432ea94070f014afde26ff5a471",
  "trx_in_block": 51,
  "virtual_op": 0
}

{
  "block": 18745843,
  "op": [
    "comment",
    {
      "author": "longwhitecoat",
      "body": "![](https://steemitimages.com/DQmQPKBYcN7J35ZcY5n8c8AZkjGnQJ4bktts8F1RNk5C5ho/image.png)\n\n**TLDR readers:** Data scientist is a sexy job because it doesn't require any pedigree, the median age of the occupants of this occupation are in their 20s and the salary can breach $200k a year. Additionally, everything you need to learn to become a data scientist is free and open source (until someone finds a way to capitalize on the demand--insert xyz company offering online courses and certifications of completion of their introductory course). I outline the specifics of the job requirements as well as include sources on free data--*big* data--that you can access to practice and hone your ninja data munging skills. \n\n**The Harvard Business Review** published a widely cited paper calling the Data Scientist the sexiest job of the 21st century. How could a statistical discipline entrenched in linear algebra and calculus become so popular? The answer is the popularity and value-add from Big Data. Statisticians moved from modeling data on excel and commercial software like MATLAB and SAS to open source programming languages like R and Python that could handle millions of observations in a single dataset. The insights derived from analyzing such a large dataset were profound. But where would we find these large datasets. Everywhere, but initially when the field was first finding its footing in the business world, the industry to start with was in startups and companies using network effects to connect millions of people across the globe. One such example was in Jonathan Goldman who joined LinkedIn in 2006 and using information from millions of users was able to provide predictions on the most likely members to belong a user’s network. The predictions using the background in a user’s profile were so accurate that the click-through rate was 30% higher than other prompts to visit new member’s pages, it generated millions of new page views which significantly affected LinkedIn’s subsequent growth. Many other examples exist such as creation of Netflix’s movie recommendation system, Zynga’s game modifications to increase engagement, and most target ‘customer-centric’ digital advertising by google, facebook, twitter and others.\n\nOriginally, the title of Data Scientist was coined in 2008 and the number of data scientists grew exponentially largely in part to advancements in frameworks (Hadoop for distributed file system processing), cloud computing, and data visualization. The looming shortage on the other hand is secondary to the fact that there are no university programs to train and consequently provide a constant supply of Data Scientists. Today’s Data Scientists are self taught. Inherent to this fact, is the underlying fact that they are a curious bunch with a scientific approach to analyzing data. Thus, it comes as no surprise that many of today’s data scientists come from fields strong in methodology with a computational focus such as physics PhDs. A common thread in those fields is a foundational knowledge of math, statistics, probability, and a technical skill related to computer science. This foundational knowledge requires domain expertise. This is an important point. Structured data is already labeled, and an industry like wall street investment banks and hedge funds have a singular objective--to buy low and sell high. In that regard, quantitative analysts working on wall street are not the type of candidate data scientist recruiters look for. Instead, they look for people who can take unstructured data and make sense of it. If the big data is in healthcare, it pays to have some domain expertise in order to make sense of all the different types of information stored in petabytes worth of data.\n\nBecause of the large demand for data scientists as companies continue to accumulate data, the salary for data scientists continues to go up (typically past the 6 figure mark). This created an opportunity for consulting firms such as Accenture, Deloitte, and IBM Global services to create divisions dedicated to analytics consulting for the companies who can’t afford or don’t need a full-time analytics team.\n\nSo what does it take to become a data scientist?\n\nBasic Tools:\n\n- Databases: mySQL, mongoDB, PostgreSQL\n\n- Distributed Computing: Apache Hadoop/Hive/Spark, MapReduce\n\n- R or Python programming language and Java (for writing production codes)\n\n- Statistics, linear algebra, and calculus\n\n- Machine Learning (ex: scikit learn library in python)\n\n- Data Munging (formatting the data so it can be used in R or python)\n\n- Data visualization (ggplot2 in r, Matplotlib in python, or d3.js which is a javascript framework)\n\n- Tableau is a data visualization and analytics tool used for business intelligence and can be used by non-technical analysts or marketers\n\nFinally, watch what the life of Josh Wills, a Data Scientist at Airbnb, is like:\n\nhttps://youtu.be/h9vQIPfe2uU\n\nAs well as that of Hilary Mason, Chief Scientist at Bitly\n\nhttps://youtu.be/fZuDwiM1XBQ\n\nBig Data Resources to Practice with:\n\nUCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets.html\n\nGovernment freely available data: www.data.gov\n\nAWS Public Datasets: https://aws.amazon.com/datasets/\n\nKaggle Datasets: https://www.kaggle.com/datasets\n\nReferences:\n\nDavenport, Thomas, and D.J. Patil. (2012). Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review. <https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century>. Accessed on 11/08/2016.\n\nUdacity. (2014). 8 Skills You Need to Be a Data Scientist. <http://blog.udacity.com/2014/11/data-science-job-skills.html>. Accessed on 11/08/2016.",
      "json_metadata": "{\"tags\":[\"datascientist\",\"bigdata\",\"technology\",\"startup\",\"investing\"],\"image\":[\"https://steemitimages.com/DQmQPKBYcN7J35ZcY5n8c8AZkjGnQJ4bktts8F1RNk5C5ho/image.png\",\"https://img.youtube.com/vi/h9vQIPfe2uU/0.jpg\",\"https://img.youtube.com/vi/fZuDwiM1XBQ/0.jpg\"],\"links\":[\"https://youtu.be/h9vQIPfe2uU\",\"https://youtu.be/fZuDwiM1XBQ\",\"https://archive.ics.uci.edu/ml/datasets.html\",\"https://aws.amazon.com/datasets/\",\"https://www.kaggle.com/datasets\",\"https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century\",\"http://blog.udacity.com/2014/11/data-science-job-skills.html\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}",
      "parent_author": "",
      "parent_permlink": "datascientist",
      "permlink": "modern-data-scientist",
      "title": "Modern Data Scientist"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T17:06:48",
  "trx_id": "302360dc12b8f432ea94070f014afde26ff5a471",
  "trx_in_block": 51,
  "virtual_op": 0
}

{
  "block": 18745673,
  "op": [
    "vote",
    {
      "author": "longwhitecoat",
      "permlink": "what-is-big-data-and-advanced-analytics",
      "voter": "longwhitecoat",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T16:58:18",
  "trx_id": "a60d801c3cd256d6f342709c48a866c631d6b6a7",
  "trx_in_block": 27,
  "virtual_op": 0
}

{
  "block": 18745668,
  "op": [
    "vote",
    {
      "author": "longwhitecoat",
      "permlink": "big-data-and-marketing-a-red-carpet-welcome-for-the-chief-marketing-technologist",
      "voter": "longwhitecoat",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T16:58:03",
  "trx_id": "52331cc95a21b2788a27de4a6925ab934e45fdbd",
  "trx_in_block": 46,
  "virtual_op": 0
}

{
  "block": 18745668,
  "op": [
    "comment_options",
    {
      "allow_curation_rewards": true,
      "allow_votes": true,
      "author": "longwhitecoat",
      "extensions": [],
      "max_accepted_payout": "1000000.000 SBD",
      "percent_steem_dollars": 0,
      "permlink": "big-data-and-marketing-a-red-carpet-welcome-for-the-chief-marketing-technologist"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T16:58:03",
  "trx_id": "52331cc95a21b2788a27de4a6925ab934e45fdbd",
  "trx_in_block": 46,
  "virtual_op": 0
}

{
  "block": 18745668,
  "op": [
    "comment",
    {
      "author": "longwhitecoat",
      "body": "https://youtu.be/TOXumIGc1hM\n\n**TLDR readers:** Technology and big data disrupted marketing in case you missed it. Today, there is a Chief Marketing Technologist or CMT in most fortune 500 companies. Marketing and sales software packages are often sold together. Checkout the Salesforce (CRM) stock price in the past few years for proof of demand. CTO budgets are increasing, and marketing majors are required to be technologically adept hen\n\n**Marketing** has seen explosive growth in the past decade with the advent of technology enabled market insights. There are key marketing metrics gleaned from the data a company collects on its clients and from its business that can be used to drive the growth of the core business. In fact, technology is playing such a major role in the marketing division of business that there is a new role emerging---that of the Chief Marketing Technologist or CMT. More than ⅔ of large companies plan on increasing their marketing technology budget in the next year and close to 90% of large companies surveyed employ a CMT according to a 2016 Gartner survey. There are more than a 1000 different software solutions for the marketing department that can handle everything from customer relationship management (CRM), content management of a company’s digital content, automation of marketing (emails, press releases, etc), platforms to manage multiple social media user handles, and more. Every commercial software product requires training and alignment of the company resources. The role of the CMT requires deep domain expertise navigating through all that technology has to offer and must work in concert with a company’s CIO and IT team on whose infrastructure this marketing technology will depend on.\n\nThe Big Data Predictions from top CMOs interviewed by Forbes’ Kimberly Whitler hint at the following potential developments to watch out for:\n\n- Artificial Intelligence will make mass marketing extinct with the potential for highly personalized and automated advertising.\n\n- Design in the form of visual media content based off insights from big data will play a larger role in driving the core business mission.\n\n- As competition increases in a crowded digital space, partnership marketing, where a campaign benefits two different companies will begin to become more common.\n\n- As CMO tech budgets surpass CIO tech budgets, the CMO will have to form a stronger relationship and partnership with the Chief Information Security Officer (CISO) as it relates to cyber security of big data to protect personal information as well as business insights.\n\n- A new (but likely slow) shift in digital marketing will occur towards B2B marketing.\n\n- Increased attention will be paid in the building of the right marketing team with a balance of poets (creative talent) and quants (technical talent).\n\n![](https://steemitimages.com/DQmPzUSmEoxrNjq6G3RucJyEV56Hp8rgNfHDKSnTwwMXKZY/image.png)\n\nIn a survey of more than 2200 Marketing profesionals conducted by Teradata, found a strong push for data driven marketing strategies. Customer-centric marketing using big data is one strategy that is viewed as a positive change in the digital revolution of marketing. Broadly however, the top three reasons cited to adopt data-driven marketing are:\n\n● Improve Efficiency\n\n● Prove effectiveness with outcomes and metrics, and\n\n● Achieve better cross-channel integration.\n\nReferences:\n\nBrinker, S., & McLellan, L. (2014). The rise of the chief marketing technologist. Harvard Business Review, 92 (7): 14 – 15.\n\nWhitler, Kimberly. Predictions From CEOs, CMOs, And Authors For 2017. (2016). Forbes Magazine. <http://www.forbes.com/sites/kimberlywhitler/2016/11/06/predictions-from-ceos-cmos-and-authors-for-2017/#3e13d82a57e3>. Accessed on 11/07/2016.\n\nTeradata. Data-Driven Marketing Delivers Enterprise-Wide Value, Global Teradata Survey Says. (2013).\n\n<http://www.teradata.com/News-Releases/2013/Data-Driven-Marketing-Delivers-Enterprise-Wide-Value-Global-Teradata-Survey-Says/> Accessed on 11/07/2016.",
      "json_metadata": "{\"tags\":[\"bigdata\",\"marketing\",\"cmt\",\"cmo\",\"salesforce\"],\"image\":[\"https://img.youtube.com/vi/TOXumIGc1hM/0.jpg\",\"https://steemitimages.com/DQmPzUSmEoxrNjq6G3RucJyEV56Hp8rgNfHDKSnTwwMXKZY/image.png\"],\"links\":[\"https://youtu.be/TOXumIGc1hM\",\"http://www.forbes.com/sites/kimberlywhitler/2016/11/06/predictions-from-ceos-cmos-and-authors-for-2017/#3e13d82a57e3\",\"http://www.teradata.com/News-Releases/2013/Data-Driven-Marketing-Delivers-Enterprise-Wide-Value-Global-Teradata-Survey-Says/\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}",
      "parent_author": "",
      "parent_permlink": "bigdata",
      "permlink": "big-data-and-marketing-a-red-carpet-welcome-for-the-chief-marketing-technologist",
      "title": "Big Data & Marketing: A Red Carpet Welcome for the Chief Marketing Technologist"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T16:58:03",
  "trx_id": "52331cc95a21b2788a27de4a6925ab934e45fdbd",
  "trx_in_block": 46,
  "virtual_op": 0
}

{
  "block": 18745480,
  "op": [
    "account_update",
    {
      "account": "longwhitecoat",
      "json_metadata": "{\"profile\":{\"name\":\"Alex Antoniou\",\"about\":\"MD|MBA|MA Economics training. finTech and healthTech investor & advisor. \",\"location\":\"Baltimore, MD\",\"website\":\"https://twitter.com/DrAntoniou\"}}",
      "memo_key": "STM6GaNZW7cDdEWwqiF61AL9uc1nK8jw5kHGFaVP9LAgnuJriAkJ3"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T16:48:39",
  "trx_id": "a956e1079ce28c783f97d00067c49c54da6f8656",
  "trx_in_block": 32,
  "virtual_op": 0
}

{
  "block": 18745403,
  "op": [
    "comment_options",
    {
      "allow_curation_rewards": true,
      "allow_votes": true,
      "author": "longwhitecoat",
      "extensions": [],
      "max_accepted_payout": "1000000.000 SBD",
      "percent_steem_dollars": 0,
      "permlink": "what-is-big-data-and-advanced-analytics"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T16:44:48",
  "trx_id": "598e7380f465131d52625f40e114ca7ded662cf7",
  "trx_in_block": 16,
  "virtual_op": 0
}

{
  "block": 18745403,
  "op": [
    "comment",
    {
      "author": "longwhitecoat",
      "body": "![](https://steemitimages.com/DQmbe8f4an4P7X25bNM4DYxKmZZaXJXMfMaKYqHEr1jLS5N/image.png)\n\n**TLDR readers**: Big data is a large enough data set that can't be queried using excel. Or in other words it can't be loaded fast enough as a file into and out of a software solution. Business insights queries big data to come up with historical visualizations to tell a story. Advanced Analytics is one step beyond business insights where you move from descriptive analytics to predictive analytics with forecast modeling or high accuracy classification using machine learning algorithms. \n\nRecently added as a new term in the [Oxford Dictionary](https://en.oxforddictionaries.com/definition/big_data) in 2013. Big data is just that. Big. It is so many rows of observations that an excel sheet would crash. It can have even more rows of observation that statistical software like STATA or SAS would crash (that is typically at about 500,000 rows of observations). Big data is not only defined by volume but by velocity and by variety. It is real-time. If you built a prediction on historical data, you better find a way to include real time data to become even more accurate. Large shopping platforms know what other products you were just browsing in your browser and can recommend similar products for purchase. Netflix includes all the latest movies you just watched to build a taste profile so it can recommend what you should watch next. Lastly, big data comes in a variety of forms. It includes tweets, emails, browsing history, personal characteristics such as demographics or personal shopping history, and hard raw data like housing prices in the last 100 years. Some data is not ‘big’ in size but many CIOs and CMOs consider including them in the definition because they have value such as product transaction information, financial records and recorded data from interaction channels such as the call center and point-of-sale touch points.\n\n![](https://steemitimages.com/DQmagTjdSYQLGkibzs5mq3yV3eqG6HSesSt4ew4QjipnT33/image.png)\n\nBig data has volume, velocity, and variety and it does not seem to be slowing down. In fact, 90% of data has been generated in the past few years alone and with the advent of the internet of things, that trend is expected to continue. Currently more than 1018 bytes of data (That’s a Quintillion!) is generated daily. We have more data than we know what to do with. Others (forbes) have included two more V’s to the evaluation of Big Data--veracity for integrity of data and value to the core business goal. This is where analytics comes in. The field of predictive analytics is a young field that uses algorithms to analyze, model, or visualize the data so that we can make sense of it. It consists of using historical (or real-time) big data to make predictions. We’ve already been using it for more than a decade in the digital era. We’ve used it to predict customer lifetime value (CLV) to a firm as well as recommend an item they are more likely to buy. Digital marketing is a dominant adopter of big data & analytics and we will introduce that landscape in the next blog. The most important foundation for good predictive analytics is having good data. As the saying goes “garbage in, garbage out”, if the data is not cleaned then it is of no value. Some examples of cleaning the data include: removing outliers, dimensions reduced using principal component analysis, missing values filled in with average, and different features with large scale variations standardized to a uniform scale such as 0 to 1. The data is then identified as structured (with predefined labels or categories such as revenue as the dependent factor and price, volume, customer loyalty, frequency of retail visits, etc as the independent variables) or unstructured (without predefined labels). The most common statistical algorithm applied is regression on structured data, but there are many more algorithms in the data scientist’s toolbox that can be used to more accurately fit the data and improve the accuracy of predictions. With big data, a statistician moved his analysis from excel and conventional statistical software packages like MATLAB and STATA to open source programming languages like R and Python that can handle much larger data observations and promoted her or her title to Data Scientist--which in the domain of Big Data and analytics, is the fastest growing profession in demand for any company that stores big data. \n\nReferences:\n\nMcAfee, A., & Brynjolfsson, E. (2012). Big data: the management revolution. Harvard Business Review, 90 (10): 60 – 68.\nArthur, Lisa. What is Big Data? (2013). Forbes Magazine. <http://www.forbes.com/sites/lisaarthur/2013/08/15/what-is-big-data/#299fb2a53487> Accessed on 11/06/2016\nDavenport, Thomas. A Predictive Analytics Primer. (2014). Harvard Business Review. <https://hbr.org/2014/09/a-predictive-analytics-primer>. Accessed 11/06/2016.",
      "json_metadata": "{\"tags\":[\"education\",\"bigdata\",\"analytics\",\"datascientist\",\"venture\"],\"image\":[\"https://steemitimages.com/DQmbe8f4an4P7X25bNM4DYxKmZZaXJXMfMaKYqHEr1jLS5N/image.png\",\"https://steemitimages.com/DQmagTjdSYQLGkibzs5mq3yV3eqG6HSesSt4ew4QjipnT33/image.png\"],\"links\":[\"https://en.oxforddictionaries.com/definition/big_data\",\"http://www.forbes.com/sites/lisaarthur/2013/08/15/what-is-big-data/#299fb2a53487\",\"https://hbr.org/2014/09/a-predictive-analytics-primer\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}",
      "parent_author": "",
      "parent_permlink": "education",
      "permlink": "what-is-big-data-and-advanced-analytics",
      "title": "What is Big Data & Advanced Analytics?"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T16:44:48",
  "trx_id": "598e7380f465131d52625f40e114ca7ded662cf7",
  "trx_in_block": 16,
  "virtual_op": 0
}

{
  "block": 18744758,
  "op": [
    "account_create_with_delegation",
    {
      "active": {
        "account_auths": [],
        "key_auths": [
          [
            "STM5tyJYRo3TYFaA5HNvAnemsgBHcSiiuiYFX9XgamTWwud1vQ3pc",
            1
          ]
        ],
        "weight_threshold": 1
      },
      "creator": "steem",
      "delegation": "57000.000000 VESTS",
      "extensions": [],
      "fee": "0.500 STEEM",
      "json_metadata": "",
      "memo_key": "STM6GaNZW7cDdEWwqiF61AL9uc1nK8jw5kHGFaVP9LAgnuJriAkJ3",
      "new_account_name": "longwhitecoat",
      "owner": {
        "account_auths": [],
        "key_auths": [
          [
            "STM7wiRSpVx697d2KTiHPfPyqK8d2B1SuQXTzY7ybSidJbXqZawRM",
            1
          ]
        ],
        "weight_threshold": 1
      },
      "posting": {
        "account_auths": [],
        "key_auths": [
          [
            "STM6xPeLoZ39pNkfKyfv9bnjuRYBwnVqKtJB8FZjTrPinHqvnh9t7",
            1
          ]
        ],
        "weight_threshold": 1
      }
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-01-06T16:12:33",
  "trx_id": "75d4493744b6df404d3c66095ac0045bbd23d16d",
  "trx_in_block": 36,
  "virtual_op": 0
}