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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
Jul 17
Bayes' Theorem is a fundamental concept in data science.

But it took me 2 years to understand its importance.

In 2 minutes, I'll share my best findings over the last 2 years exploring Bayesian Statistics. Let's go. Image
1. Background:

"An Essay towards solving a Problem in the Doctrine of Chances," was published in 1763, two years after Bayes' death. In this essay, Bayes addressed the problem of inverse probability, which is the basis of what is now known as Bayesian probability. Image
2. Bayes' Theorem:

Bayes' Theorem provides a mathematical formula to update the probability for a hypothesis as more evidence or information becomes available. It describes how to revise existing predictions or theories in light of new evidence, a process known as Bayesian inference.
Read 11 tweets
Matt Dancho (Business Science) Profile picture
Jul 15
🚨 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
2. Document Conversion Architecture

For each document format, the document converter knows which format-specific backend to employ for parsing the document and which pipeline to use for orchestrating the execution, along with any relevant options. Image
Read 6 tweets
Matt Dancho (Business Science) Profile picture
Jul 14
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
The AI data scientist builds the systems to analyze information, gain business insights, and automates the process.

- Models the system
- Use AI to extract insights
- Drives predictive business insights

Want to become a Generative AI Data Scientist in 2026? Image
Read 4 tweets
Matt Dancho (Business Science) Profile picture
Jul 13
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 13 tweets
Matt Dancho (Business Science) Profile picture
Jun 30
A senior Google engineer dropped a 482 page PDF on agentic design patterns.

482 pages.

Most AI engineers bookmarked it and never opened it again.

I read the whole thing.

Here are the top 5 patterns (explained in plain English): Image
PATTERN 1 — Single Agent

The simplest and most common starting point.

One model. One system prompt. A bounded set of tools.

The model decides which tool to call, observes the result, and keeps going until it has enough to answer. Image
PATTERN 2 — Multi-Agent Sequential

Specialized agents run in a fixed order.

Each one's output feeds the next one's input. Image
Read 8 tweets
Matt Dancho (Business Science) Profile picture
Jun 9
🚨BREAKING: Google just DROPPED a masterclass on GPUs

Get it here 100% free: Image
FULL GUIDE: HOW TO SCALE YOUR MODEL: jax-ml.github.io/scaling-book/

PART 12: HOW TO THINK ABOUT GPUS: jax-ml.github.io/scaling-book/g…

I have one more thing before you go.

If you want to become a generative AI data scientist in 2026 ($200,000 career), then I'd like to help: Image
On June 24th, I am hosting a free workshop to help you get started with AI + DS projects in Python.

Register here (500 seats):   learn.business-science.io/ai-registerImage
Read 4 tweets

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