My master resume in a Markdown Jupyter Notebook

MasterResume.ipynb

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   "source": [
    "# Master Resume Info"
   ]
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   "source": [
    "## Master Resume Worksheet"
   ]
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   "source": [
    "### Heading\n",
    "- Full name: Nash Taylor\n",
    "- Email: ntaylorwss@outlook.com\n",
    "- Phone: 778-846-8957\n",
    "- LinkedIn: linkedin.com/in/nashtaylor22\n",
    "- GitHub: github.com/ntaylorwss\n",
    "- Twitter: twitter.com/NashtagTaylor\n",
    "- Medium: medium.com/@ntaylorwss"
   ]
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    "### Skills\n",
    "- Programming languages\n",
    "    - Python (advanced)\n",
    "    - R (proficient)\n",
    "    - SQL (moderate)\n",
    "    - Bash (moderate)\n",
    "    - JavaScript (working knowledge)\n",
    "    - HTML/CSS (working knowledge)\n",
    "- Libraries\n",
    "    - Python\n",
    "        - Numpy\n",
    "        - Pandas\n",
    "        - sklearn\n",
    "        - TensorFlow\n",
    "        - MySQLdb\n",
    "        - mongoengine\n",
    "        - matplotlib\n",
    "        - Scipy\n",
    "        - Anaconda\n",
    "        - Beautiful Soup\n",
    "        - Selenium\n",
    "    - R\n",
    "        - ggplot2\n",
    "        - dplyr\n",
    "    - JavaScript\n",
    "        - D3\n",
    "        - Dimple\n",
    "- IDEs\n",
    "    - Spyder\n",
    "    - Jupyter Notebook\n",
    "    - RStudio\n",
    "    - Terminal\n",
    "- Version Control Systems\n",
    "    - Git / GitHub\n",
    "- Databases\n",
    "    - MongoDB\n",
    "    - MySQL\n",
    "    - SparkSQL\n",
    "- Data formats\n",
    "    - CSV\n",
    "    - JSON\n",
    "    - XML\n",
    "    - XLSX\n",
    "    - SQL databases\n",
    "    - Mongo databases\n",
    "    - Web\n",
    "- Operating Systems\n",
    "    - Windows 7/8/8.1/10\n",
    "    - Ubuntu 16.04\n",
    "- Applicable Software\n",
    "    - Microsoft Office (Word, Excel, PowerPoint, Outlook, Access, OneNote, SharePoint)\n",
    "- Soft skills\n",
    "    - Self-taught everything, can learn anything quickly\n",
    "    - Easy to get along with, friendly"
   ]
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   "metadata": {},
   "source": [
    "### Relevant Experience"
   ]
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   "source": [
    "### Education\n",
    "- College/University: None\n",
    "- Nanodegrees\n",
    "    - Data Analyst Nanodegree\n",
    "    - Machine Learning Engineer Nanodegree\n",
    "- Courses Taken\n",
    "    - Udacity\n",
    "        - Intro to Computer Science\n",
    "        - Intro to Statistics\n",
    "        - Intro to Descriptive Statistics\n",
    "        - Intro to Inferential Statistics\n",
    "        - Intro to Data Science\n",
    "        - Data Wrangling with MongoDB\n",
    "        - Data Analysis with R\n",
    "        - Intro to Machine Learning\n",
    "        - Data Visualization and D3.js\n",
    "        - A/B Testing\n",
    "        - Model Evaluation and Validation\n",
    "        - Machine Learning: Supervised Learning\n",
    "        - Machine Learning: Unsupervised Learning\n",
    "        - Machine Learning: Reinforcement Learning\n",
    "        - Reinforcement Learning\n",
    "        - Deep Learning\n",
    "        - Artificial Intelligence for Robotics\n",
    "        - Intro to Computer Vision\n",
    "        - Machine Learning for Trading\n",
    "    - Khan Academy\n",
    "        - Differential Calculus\n",
    "        - Integral Calculus\n",
    "        - Multivariable Calculus\n",
    "        - Linear Algebra\n",
    "        - Differential Equations\n",
    "    - Other\n",
    "        - MIT 18.06 Linear Algebra\n",
    "        - edX CS105x Intro to Apache Spark\n",
    "- Certificates\n",
    "    - Microsoft Office Specialist certification in: Word (expert), Excel (expert), PowerPoint, Outlook, Access, OneNote, SharePoint"
   ]
  },
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   "source": [
    "### Projects\n",
    "#### Opponent Modeller for Texas Hold'em Poker\n",
    "- End-to-end development of a supervised learning model that predicts a poker player's action based on information about the game\n",
    "- Trained using a large hand-crafted feature set of over 100 features, 2GB in size, parsed from raw text logs\n",
    "- Combined Python with Bash and SQL to produce final dataset\n",
    "- Date: September 2016\n",
    "- Link: https://github.com/ntaylorwss/pokerAI\n",
    "\n",
    "#### Fantasy Football Draft Tracker\n",
    "- Created an environment for tracking and interacting with a fantasy football draft using an object-oriented design\n",
    "- Scraped football data from the web using Python libraries beautifulsoup and selenium\n",
    "- Used tool to win league with friends\n",
    "- Date: August 2016\n",
    "- Link: https://github.com/ntaylorwss/FantasyPrep2016\n",
    "\n",
    "#### Vegas Betting Visualization\n",
    "- Used D3.js JavaScript library to create animated and interactive visualization of betting losses on NFL football games\n",
    "- Scraped and formatted betting data from the web using various R packages\n",
    "- Incorporated feedback from friends to iterate on and improve the visualization\n",
    "- Date: December 2015\n",
    "- Link: https://github.com/ntaylorwss/Data-Analyst-Nanodegree/tree/master/Project%206%20-%20Data%20Viz%20with%20D3\n",
    "\n",
    "#### Exploratory Analysis of Fantasy Football\n",
    "- Explored, with visualizations, tables, and statistics, dataset of football player statistics\n",
    "- Generated a full report on findings in R Markdown\n",
    "- Date: November 2015\n",
    "- Link: https://github.com/ntaylorwss/Data-Analyst-Nanodegree/tree/master/Project%204%20-%20EDA%20with%20R\n",
    "\n",
    "#### Self-Driving SmartCab in a Grid World\n",
    "- Implemented Q-learning in Python to train a driving agent to navigate a small grid world accurately\n",
    "- Built on existing code to run and analyze the agent in a Pygame environment\n",
    "- Date: March 2016\n",
    "- Link: https://github.com/ntaylorwss/Machine-Learning-Nanodegree/tree/master/Project%204%20-%20Reinforcement%20Learning\n"
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   "source": [
    "### Interests\n",
    "- Games\n",
    "    - Game-playing AI (e.g. poker)\n",
    "- Sports\n",
    "    - Fantasy sports\n",
    "    - Fantasy football\n",
    "    - Fantasy football algorithms and analysis\n",
    "    - Maple Leafs, Eagles, Blue Jays\n",
    "    - Playing soccer and road hockey\n",
    "- Music\n",
    "    - Drumming\n",
    "- Video games\n",
    "    - Sports gaming"
   ]
  },
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   "source": [
    "### Awards\n",
    "- Canadian National Champion for Microsoft Excel 2013, June 2015\n",
    "- Silver medalist at Microsoft Office Specialist World Championships for Excel 2013, August 2015"
   ]
  }
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