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🔥 Matt Dancho (Business Science) 🔥 Profile picture
@mdancho84
May 20, 2023 • 7 tweets • 5 min read • Read on X
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As a data scientist, productivity is a 10X super power.

Here's a short list of AI tools to help data scientists with: 🧵

#ai #datascience #career #skills #tools Image
1. Writing code

AI pair programming is a huge benefit.

Tools like #chatgpt & github #copilot can help debug complex code and replace Googling + Stack Overflowing for common scripting.

Key skill: ChatGPT prompting (more on this in my free ChatGPT for Data Scientists) Image
2. Code Quality & Documentation

Great products have great documentation. AI can help produce documentation, comment code, and replace time-consuming manual documentation with automated AI docs.

Key Skill: Using @mintlify to build your docs: mintlify.com Image
3. Presentations

Great data scientists are storytellers. Use persuasion to your advantage.

Key Skill: Generating images with AI using @midjourney_ai . midjourney.com Image
I'm road-testing all of these.

And I've been quietly researching #ChatGPT for Data Scientists (My NUMBER 1 TOOL) for the past 4 months.

I have good news - I'm ready to reveal my chatgpt research!
If you want to understand how ChatGPT can make you a better data scientist (and mistakes to avoid)...

I'll be sharing my research in a Free WORKSHOP: ChatGPT for Data Scientists (Wednesday, June 7th)!
What's Your Next Step?

Join me and 1,000 data scientists as we crush AI in my LIVE ChatGPT for Data Scientists Workshop.

Seats are limited (1,000 max).

👉Register Here: us02web.zoom.us/webinar/regist… Image

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More from @mdancho84

🔥 Matt Dancho (Business Science) 🔥 Profile picture
Feb 2
🚨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.
2. Not everything needs an Agent

Stop agent-ifying everything.

Ask: "Is this a problem that actually needs solving with agents?"

Alternatives to Agents:

- Automation
- NLP
- Basic Gen AI
- Predictive Analytics
Read 10 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jan 22
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
Chapter 2 introduces generative models, which are the large language models we commonly refer to today.

After presenting the basic process of building these models, you explore how to scale up model training and handle long texts. Image
Read 10 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jan 21
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
AI + Data Science is the future:

AI tools like:

- LangChain
- LangGraph
- OpenAI API

Are being combined with:

- SQL Databases
- Machine Learning
- Prediction

And the results are exactly what businesses need: real-time predictive insights. Image
Read 7 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jan 18
A Research Scientist at Google DeepMind just dropped a 58 page paper on building agents that specialize in game theory.

Here are the most important parts: Image
The problem with existing agents - You need to prompt the LLM to generate actions.

But this doesn't work in games that have perfect or imperfect information.

Instead, they implement a trick:
To describe a complex "world model", researchers simplify as a Partially Observed Stochastic Game.

It's represented below as a causal graph. Image
Read 9 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jan 18
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.)
1. Cost + efficiency

The quiet story of 2025: AI is getting dramatically cheaper + more efficient.

The report estimates price-performance improved ~30% per year and energy efficiency improved ~40% annually.

That’s why AI is moving from “demo” to “default.”
Read 16 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jan 17
This is huge.

A group of 50 AI researchers (ByteDance, Alibaba, Tencent + universities) just dropped a 303 page field guide on code models + coding agents.

And the takeaways are not what most people assume.

Here are the highlights I’m thinking about (as someone who lives in Python + agents):Image
1) Small models can punch way above their weight

If you do RL the right way (RLVR / verifiable rewards), a smaller open model can close the gap with the giants on reasoning-style coding tasks.
2) Python is weirdly hard for models

Mixing languages in pretraining helps… until it doesn’t. Python’s dynamic typing can create negative transfer vs. statically typed languages. Meanwhile pairs like Java↔C# or JS↔TS have strong “synergy.”
Read 15 tweets

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