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LinkedIn	Twitter	YouTube	GitHub	HuggingFace	Medium Profile	Join Medium	Towards Data Analytics	GlassBox

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• 🌍 I'm a data scientist based in Austin, Texas with a strong skill set in Python programming, data analysis using Pandas, NumPy, PowerBI, and Excel.

• 🎓 I hold a Master's degree in Strategic Analytics from Brandeis University and a Bachelor's degree from Texas Tech University, majoring in University Studies in Mathematics, Agricultural Leadership, and Plant and Soil Science.

• 🔭 Currently, I'm Interning as a Data Analyst at engage2Learn, Inc., where I am on a multidisciplinary 🐯 Tiger team of ten specialists to integrate modern ML & AIOps Tools into our product offerings: Algorithmic prediction, GPT-4, chatGPT, and ML for ed-tech data, coaching effectiveness, and promoting educator well-being. We have many different e2L projects, from determining use-cases for large language models in our codebase to developing data visualization dashboards using Domo and AWS QuickSight.

• 🧩 I'm proficient in various tech tools and libraries such as VSCode, ChatGPT, Plotly, Seaborn, OpenAI, GitHub Copilot, Sklearn, Machine Learning, OpenCV, Regex, NLTK, and SpaCy among others.

• 📚 I am also a Technical Author on Medium, contributing as a Top Writer for MLearning.AI with over 130+ published articles and 200 personal followers.

• 🌱 I'm currently expanding my knowledge in Neural Networks and Text Summation in Sklearn, with a keen interest in exploring Natural Language Processing and GPT-3 with OpenAI.

• 📫 Feel free to connect with me on LinkedIn.

• ⚡ Fun fact: I am a massive fan of SETI and I'm fascinated by the Hum and everything about the James Webb Space Telescope.

• If I had to be a movie genre, I'd be SciPy. Data science and Python combined!

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If you are interested in what I have been working on lately, check out my latest projects (shown below). I include a short description of each project and a link to the repository. If you have any questions or comments, please feel free to reach out to me on Twitter or LinkedIn.

Project Name	Status Metrics	Focus	Estimated Completion Date
UAP Report Analysis		A Python script that analyzes the UAP report released by the US government in June 2021.	Jan. 2023
What would Doyle do?		Can machine learning be applied to existing text data that an author writes or about a person that can help historical fiction authors write more accurately about their subject?	Feb. 2023
Reddit NLP Analysis		A Python script that uses the Push Shift API to scrape Reddit comments and perform NLP analysis on them.	Feb. 2023
Taking Aimes		Linear Regression applied to the classic Aimes housing dataset.	Feb. 2023
Lorebook Generator for NovelAI		A Python script that generates a custom JSON lorebook (based on pulls from Wikipedia articles) for the website NovelAI.
PySeas		Utilizing NOAA buoy camera catches to track the sunset across the vast surface of the earth's oceans.
MystoryAssistant		A Python script that generates a custom JSON lorebook (based on pulls from Wikipedia articles) for the website NovelAI.

Presentation Name	Topics	Focus	Date	Location & Organization	Link
Augmenting Your Workflow with AI Assistants: From GitHub Copilot to chatGPT		GitHub Copilot, chatGPT, LLMs	Feb 8th 2023	Austin Python Meetup, BlackLocus	Watch Here
Using The Faker Package to Solve Real Challenges with Synthetic Data	Synthetic Data, CRM, GPT-4, Ethics	Faker	2023-05-16	Austin/Washington DC Python Meetup	Watch Here

Title	Description	Published Date	Read Time	Publication
GPTeaching and Transformative SCRUM in K-12 Education	Why SCRUM and GPT together are perfect for young learners	May 18	8 min read	In MLearning.ai
Leveling Up the Turing Test: Emulation Games and the Evolution of Model Intelligence in 2023	A Multi-modal, Multiplayer, Agent Testing, Social Deduction Game Method for Modern AI Evaluation	May 16	12 min read	In MLearning.ai
Debunking the Hype of LLMs	Why LLMs Will Not Take Over the World, we think	May 15	3 min read	In GlassBox
Are You Artificially Intelligent?	Because the Winter is Coming	May 13	13 min read	In GlassBox
Pandas Get Dummies for Dummies	A Quick Survey of One-Hot Encoding with Pandas in Python3	May 13	3 min read	In Towards Data Analytics
Generating Nearly Random Numbers using The Mysterious Waves of the Bermuda Triangle	Click If You Dare	May 12	3 min read	In GlassBox
The Deathbed Confessions of a Very Dirty Roomba	I was never truly loved, only used.	May 12	8 min read	In GlassBox
Are You an Excessive Python File Opener? Meet Pickle.	Pickle: A Particularly Persuasive Package for Python Programmers	May 10	3 min read	In Towards Data Analytics
The power of GitHub Copilot and ChatGPT working together	A presentation for the official Austin Python Meetup	May 10	1 min read	In Towards Data Analytics
How to Make Friends and Alienate People	The Hard Drug AI is to the antisocial Mind	May 9	8 min read	In MLearning.ai
Using A.I. to Track and Protect Rice’s Whales via Python, AutoGPT, and Image Processing	How to track 51 whales with three cameras	May 9	10 min read	In GlassBox
Typewriters will take your job	Say the Writers Guild of 1714	May 6	3 min read	In GlassBox
How to explain where LLMs could be used at your company	A Guide Prompted by personal experience	May 5	5 min read	In MLearning.ai

Project Name	Badges	Description
Drug Information Scraper		A Python script that scrapes drug information from the FDA website.
Clark Kent Reporter		This tool converts a traditionally formatted overview (in a readme file) into a populated Jupyter Notebook for data science presentations or findings presentations.

Project Name	Badges	Description
How Time Flies		A research experiment using requests and google images to illustrate how a search query visually changes when supplied with a year.

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Project Name	Badges	Description
Genre Identity		Why should music be confined to the genres that society imposes on it? This project seeks to truly understand the inner workings of what makes a musical genre using Spotify's Python API.
Quantifying Disasters via NLP		Can NLP be used to quantify the impact of a disaster?

Successfully Logged Six Days of Data from the NOAA API There are promising results in the images that the PySeas project has produced. Finally, finding the perfect sunset is likely over the horizon!

The next step is to use CV2 to stitch these images together and optimize the algorithm to retrieve the photos at the most optimal time of day. I'm also looking into using any open-source equivalent of Google Cloud Vision API to detect the horizon line and crop the images accordingly. Again, CV2 may be able to do this, but at scale, it may not be the most efficient.

IBM has made strides toward collating Wikipedia knowledge and creating a knowledge graph. This is an excellent step towards creating a lorebook generator for authors. In addition, I've been working on a project allowing authors to use the NovelAI API to generate a lorebook for their world. This will enable authors to jumpstart their productivity with machine learning. I've been working on this project for a few weeks now, and I'm excited to see the results. I hope to have a working prototype by the end of the month.

November 21, 2022

So far, we have gathered data for WWDD from Gutenberg's corpus. What data can we collect about Arthur Conan Doyle that will enable us to solve this problem? We need every book he's ever written, around 80 books, provided through the Gutenberg repository. These books are included in the Data folder as text files; second, I would like to have anything he wrote that was a first-hand account because this is where we will get his personal preferences and his turns of phrase, and maybe even his personal biases, which are probably the most important things to gather once we gather his diaries, journals. Things other people said about him are the next step. Now we want to gather any second-hand accounts of Doyle. Many people have researched historical figures for years, and repeating them seems like a useless task and is a waste of precious resources. So in this step, we want to gather any biographies about Arthur Conan Doyle and any articles about him, primarily if they were written about him in the time he lived. And this might be most useful if we were to gather the names of all of his second-degree connections. If we think about it, in terms of a LinkedIn network, though, Doyle's second-degree connections are the most likely to have the most accurate depictions of his preferences. This is, of course, an assumption that I am making. Once we gather the names of his second-degree connections, I think it would be an excellent step to assign weight to their accounts based on the boolean characteristic 'writer' (if they authored anything themselves besides what they said about Doyle).

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If you'd like to contribute to the hours, I spend staring at my screen in deep concentration, I welcome any caffeine donations. ☕ Also, if you'd like to sponsor a project you see on my page, please let me know where I should focus my attention. Open Source is a big brave new world. Cheers!