<aside> đź‘‹ Jason L. Hodson | Find me on LinkedIn | Find my projects on GitHub

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As a dedicated data practitioner, I am deeply committed to transforming abstract strategies, research, and concepts into practical, actionable data analytics solutions. It's been disheartening to see valuable resources like Medium restrict access through paywalls, which can be a barrier for those who are just starting their journey or lack the financial means to invest in their learning. Equally frustrating are the ad-laden, AI-generated, and often incomplete tutorials that more often confuse than clarify. In response, I've embraced Notion as my platform of choice, with a heartfelt mission to create thorough, step-by-step tutorials. My aim is to offer you a solid foundation in building enterprise data architectures, ensuring that every learner has the opportunity to grow, regardless of their financial situation.

I'm deeply convinced that AI technologies represent the most significant and comprehensive technological advancements humanity has ever witnessed. Recently, I've embarked on a meaningful journey to learn how to merge data with AI, particularly focusing on OpenAI's Assistants. This venture into uncharted territory is both exciting and challenging, but I'm eager to enhance my skills and share my discoveries along the way. As I progress, I anticipate revisiting and perhaps revising some of my learnings. Nonetheless, I firmly believe in the importance of freely sharing this knowledge and collaboratively building on it. By doing so, we can bridge the knowledge gap and combat the inequities that arise from it, fostering a more inclusive and equitable future for everyone interested in AI.

With over a decade of experience in the data field, my career has taken me from consulting at Accenture—where I played a key role in analytics projects for Fortune 50 companies—to leading the charge as the Director of Data and Analytics at innovative FinTech startups. Throughout these years, I've had the privilege of guiding teams of various sizes, from as many as 25 in implementation roles to as few as three in internal data support. My comfort level spans from the initial moments of conceptualizing solutions in SQL or Python on a blank notepad to strategizing about data at the executive level. I hope to leverage my experience in data engineering tools like dbt, and data modeling in platforms such as Redshift, Google BigQuery, and Oracle, along with semantic modeling and visualization in Looker, Tableau, and Oracle Analytics Cloud, to author pragmatic, easy-to-understand lessons. My goal is to make these complex concepts accessible to everyone, demystifying the often intimidating world of data analytics.

But enough about me, let’s get to work:

  1. **Introduction to Web Scraping with AWS ECR and Lambda:** Step into the world of web scraping with this detailed guide that breaks down the complexities of utilizing AWS Elastic Container Registry (ECR) and Lambda. This resource is designed for those looking to automate the extraction of data from the web efficiently. You'll need an AWS account with access to ECR and Lambda Functions, along with a basic grasp of Python.
  2. **Developer's Guide to Embedding AI Chatbots in Webpages Using AWS:** This guide is your pathway to building your very own AI-powered chat interface, seamlessly integrated within a webpage. It's a fantastic project for those looking to harness the capabilities of Large Language Models (LLMs) to enhance user engagement and interaction. This tutorial is particularly suited for developers with a background in Python and Django web application development, and it requires an AWS account with access to Elastic Beanstalk and the OpenAI Assistant API.

OpenAI Assistants and Vector Databases: A Guide to RAG on Pinecone.io

Developer's Guide to Embedding AI Chatbots in Webpages Using AWS

Introduction to Web Scraping with AWS ECR and Lambda

General Configuration Pages