Welcome to Data for Public Health. I believe that anyone can
learn and apply data science techniques to understand public health data,
regardless of academic credentials. Cultivating the necessary tools and skills for
such analyses are readily available within everyone's grasp. This site features example applications of data science, machine learning, and
statistics using open-source software and public health data from sources around the globe.
Eventually, I’ll make an interactive intro video with a greenscreen and link it
HERE.
The main purpose of this website is to feature my explorations
of public health data from U.S. and global sources using open source platforms,
but I also provide a series of brief articles spanning basic data science
topics. These are intended primarily for my visiting students, as I currently teach health informatics at National University. Perhaps the curious will find some value in these offerings as well.
In addition to tags on each post, this website's content is laid out across five primary categories:
Intros: Broad walkthroughs of overarching
data science concepts, such as public data, open source coding, and how learn data
science.
Definitions: Brief clarifications on fundamental
data science concepts.
Techniques: Summaries of various methods
in statistics and artificial intelligence whereby data variability can be assessed,
predicted, and understood.
Philosophy: Axiomatic presuppositions and
intellectual contexts inherent to empirical data science.
Coding Projects: Explorations of our
world through public health data leveraging statistical analyses, machine
learning, and visualization libraries.
To assist me with this website project, I developed a
multi-agent system comprising seven chained LLMs under the direction of a
supervisor AI. This architecture leverages several advanced technologies,
including OpenAI, several Ollama models, Flowise, Grok, HuggingChat, Firecrawl,
and LangChain, along with a Serper API web scraper and a couple additional, uncensored
LLMs. (I find it easier to work with LLMs that haven't been programmed to fine tune me.) This also helped unlock translation of posts into other languages. If you're interested
in learning more about this project, I can be reached through LinkedIn or via
direct message on X.