Articles on Data Science

I watched a company burn $1.3M on AI while their data lived in 12 disconnected systems. Your AI project is doomed without a data foundation.

Anthropic analyzed 4M+ AI conversations to reveal where AI actually gets used. The data shows augmentation wins over automation, 57% to 43%.

Using ChatGPT for emails isn't 'doing AI.' Real implementation means building models, integrating systems, and learning from failure. Here's how.

NLP is the real technology behind LLMs, and it's quietly reshaping customer engagement. From chatbots to sentiment analysis, here's why it matters.

Drowning in data but starving for insights? A no-nonsense guide to machine learning techniques that turn business data into better decisions.

RLHF is how AI learns from human preferences. Here's how Reinforcement Learning from Human Feedback reshapes business decisions and customer experience.

Brilliant technical work gets ignored because it's presented wrong. Here's how data scientists bridge the gap to business leadership.

Data scientists, engineers, and business teams in separate corners kill AI projects. Here's how to build cross-functional teams that deliver.

In this post, I provide an overview of comparing different machine learning methods.

In a post titled Data Mining - A Cautionary Tale, I share the idea that data mining can be dangerous by sharing the story of Cornell's Brian Wansink, who

Want to be a data science rockstar? of course you do! Sorry for the clickbait headline, but I wanted to reach as many people as I can with this important

Just because your machine learning or AI models look good on paper does not guarantee they will work in the real world.

Stop thinking about big data technologies.  Think of ways to 'analyze, contextualize, internalize' your data instead.

Many organizations are jumping on the big data bandwagon (rightfully so). A good portion of them aren't thinking about data quality before data analytics.

Doing data science without 'science' is nothing more than throwing darts at a dart board and thinking the results are meaningful.

Why Python became my go-to tool for data analytics over R and Excel, plus the modules that make it possible.

Data preparation is extremely important to your data analytics / big data projects. Good data preparation can lead to good data analytics outcomes.

Good data science isn't about finding answers to questions. Good data science is about setting up your data and systems to allow you to find more questions.

An analysis of twitter messages using the #CIO hashtag and captured / analyzed for patterns and information.

Do small businesses have the tools and skills to take advantage of big data, or will it drive innovation for large companies and leave them behind?

An overview of my research into using Twitter sentiment and message volume to model stock price movements for the S&P 500 SPY ETF.

Can twitter be used to determine sentiment for making investing decisions?

I just finished reading Eric T. Peterson's post titled The Myth of the “Data-Driven” Business. I don't talk or write much about 'data'...mostly because