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Matt Dancho (Business Science) Profile picture
Matt Dancho (Business Science)
@mdancho84
Generative AI, Data Science, Python, and Business (ROI). Join my next live AI workshop (free).👇
Apr 27 • 7 tweets • 3 min read
🚨 BREAKING: Microsoft launches a free Python library that converts ANY document to Markdown

Introducing Markitdown. Let me explain. 🧵 Image 1. Document Parsing Pipelines

MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. Image
Apr 22 • 9 tweets • 3 min read
Understanding regression models is essential in data science.

In 4 minutes, I'll demolish your confusion. Let's go: Image 1. The 6 Diagnostic Checks Every Data Scientist Should Run

Once you've built a regression model, your job isn't done. These 6 checks will tell you whether your model can actually be trusted.
Apr 19 • 10 tweets • 3 min read
Understanding probability is essential in data science.

In 4 minutes, I'll demolish your confusion.

Let's go! Image 1. Statistical Distributions:

There are 100s of distributions to choose from when modeling data. Choices seem endless. Use this as a guide to simplify the choice. Image
Apr 18 • 8 tweets • 2 min read
RIP manual research workflows.

Someone just open-sourced what comes after Karpathy’s AutoResearch.

It’s called AutoResearchClaw.

And this thing is insane. Image A few weeks ago, Karpathy showed where research was heading:

AI agents running the experiment loop.

That was already a big signal.

AutoResearchClaw takes it even further.

It doesn’t just help with research.

It tries to automate the entire scientific method end-to-end.
Apr 15 • 9 tweets • 3 min read
These 7 statistical analysis concepts have helped me as an AI Data Scientist.

Let's go: 🧵 Image Step 1: Learn These Descriptive Statistics

Mean, median, mode, variance, standard deviation. Used to summarize data and spot variability. These are key for any data scientist to understand what’s in front of them in their data sets. Image
Apr 7 • 7 tweets • 3 min read
🚨 BREAKING: Microsoft launches a free Python library that converts ANY document to Markdown

Introducing Markitdown. Let me explain. 🧵 Image 1. Document Parsing Pipelines

MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. Image
Apr 2 • 8 tweets • 3 min read
RIP document extractors.

Google just released LangExtract: Open-source. Free. Better than $100K enterprise tools.

Here’s what it does: 🧵 Image What it does:

→ Extracts structured data from messy text
→ Grounds every field to the exact source location
→ Handles 100+ page docs
→ Generates interactive HTML for verification
→ Works with Gemini + local models Image
Mar 31 • 7 tweets • 2 min read
Data science killed itself.

Not because AI showed up. Because too much of the field confused running a model with understanding one. Image For years, data science rewarded people for producing outputs:

A model score
A dashboard
A notebook
A prediction
A nice chart

And a lot of that work looked impressive.
Mar 22 • 8 tweets • 2 min read
Someone built a free 7-week RAG curriculum on GitHub.

And they're right — it's good.

But, you'll need 1 more thing to get an AI/DS job in 2026: Image Docker. FastAPI. PostgreSQL. OpenSearch. Airflow. Hybrid search. LangGraph. Production monitoring.

That's a serious architecture. Bookmark it. github.com/jamwithai/prod…
Mar 17 • 13 tweets • 5 min read
OpenAI, Google, and Anthropic just published guides on:

• Prompt engineering
• Building agents
• AI in business
• 601 AI use cases

9 of the best guides you can't miss: Image 1. AI in the Enterprise by OpenAI

Grab the PDF: cdn.openai.com/business-guide…Image
Mar 15 • 9 tweets • 2 min read
80% of data scientists say they want to build AI agents.

Almost none of them can answer this question:

Which agentic pattern should you actually use? Image There are 7. And picking the wrong one breaks your entire workflow.

Here's the quick breakdown:
Mar 14 • 9 tweets • 2 min read
Harvard just open-sourced its entire ML Systems curriculum.
Free. Public. 6 pillars. Hundreds of pages.

And it won't get most data scientists any closer to a $150K AI role.

Here's why. Image The book covers:

1. System Design
2. Data Engineering
3. Model Deployment
4. MLOps and Monitoring
5. Edge AI
6. Responsible AI
Mar 1 • 9 tweets • 2 min read
🚨McKinsey just dropped how to build agentic AI (that works)

Here's everything you need to know in 2 minutes: Image 1. Stop building agents; Start fixing workflows

The mistake every organization makes: falling in love with your new AI agent.

The solution: Identify the pain points in your process. Then use agents to connect analytics and gen AI into 1 seamless process.
Feb 24 • 5 tweets • 2 min read
RIP Data Scientists.

The Generative AI Data Scientist is NOW what companies want.

This is actually good news. Let me explain: Image Companies are sitting on mountains of unstructured data.

PDF
Word docs
Meeting notes
Emails
Videos
Audio Transcripts

This is useful data. But it's unusable in its existing form. Image
Feb 22 • 11 tweets • 3 min read
Understanding probability is essential in data science.

In 4 minutes, I'll demolish your confusion.

Let's go! Image 1. Statistical Distributions:

There are 100s of distributions to choose from when modeling data. Choices seem endless. Use this as a guide to simplify the choice. Image
Feb 21 • 15 tweets • 3 min read
Stanford just dropped a 457 page report on AI.

It's packed with data on: cost drops, efficiency, benchmarks, adoption.

This report is a cheat code for your career in 2026.

I pulled the most important charts + what they mean for your career: 🧵 Image First: this isn’t “AI hype.”

It’s measured trends on what’s getting cheaper, what’s getting better, and what’s spreading across the economy and regulation.

(Bookmark this. You’ll reuse it.)
Feb 21 • 8 tweets • 3 min read
This 277-page PDF unlocks the secrets of Large Language Models.

Here's what's inside: 🧵 Image Chapter 1 introduces the basics of pre-training.

This is the foundation of large language models, and common pre-training methods and model architectures will be discussed here. Image
Feb 20 • 8 tweets • 3 min read
🚨 BREAKING: Microsoft launches a free Python library that converts ANY document to Markdown

Introducing Markitdown. Let me explain. 🧵 Image 1. Document Parsing Pipelines

MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. Image
Feb 20 • 6 tweets • 2 min read
RIP BI Dashboards.

Tools like Tableau and PowerBI are about to become extinct.

This is what's coming (and how to prepare): Image I've never been a fan of Tableau and PowerBI.

Static dashboards don't answer dynamic business questions.

That's why a new breed of analytics is coming: AI Analytics. Image
Feb 19 • 7 tweets • 3 min read
🚨 BREAKING: IBM launches a free Python library that converts ANY document to data

Introducing Docling. Here's what you need to know: 🧵 Image 1. What is Docling?

Docling is a Python library that simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem. Image
Feb 5 • 14 tweets • 5 min read
OpenAI, Google, and Anthropic just published guides on:

• Prompt engineering
• Building agents
• AI in business
• 601 AI use cases

9 of the best guides you can't miss: Image 1. AI in the Enterprise by OpenAI

Grab the PDF: cdn.openai.com/business-guide…Image