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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
Oct 26
This is wild.

A new paper shows how you can predict real purchase intent without asking people.

~90% of human test–retest reliability.

Here's what's inside the 28 page paper: Image
1. Problem with direct Likert from LLMs:

When you ask LLMs to output 1–5 ratings directly, the distributions are too narrow/skewed and don’t look like human survey data, limiting usefulness for concept testing. Image
2. Proposed fix — Semantic Similarity Rating (SSR):

Have the LLM write a short free-text purchase-intent statement, then map that text onto a 5-point Likert score using embedding cosine similarity to predefined anchor sentences (i.e., semantic matching instead of raw numbers). Image
Read 9 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Oct 22
How to build AI agents:

A great cheat sheet (bookmark for later).

Here's how to use it: Image
1️⃣ System Prompt: Define your agent’s role, capabilities, and boundaries. This gives your agent the necessary context.

2️⃣ LLM (Large Language Model): Choose the engine. GPT-5, Claude, Mistral, or an open-source model — pick based on reasoning needs, latency, and cost.
3️⃣ Tools - Equip your agent with tools: API access, code interpreters, database queries, web search, etc. More tools = more utility. Max 20.

4️⃣ Orchestration: Use frameworks (like LangChain, AutoGen, CrewAI) to manage reasoning, task decomposition, and multi-agent collaboration.
Read 7 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Oct 20
Understanding P-Values is essential for improving regression models.

In 2 minutes, I'll crush your confusion. Image
1. The p-value:

A p-value in statistics is a measure used to assess the strength of the evidence against a null hypothesis.
2. Null Hypothesis (H₀):

The null hypothesis is the default position that there is no relationship between two measured phenomena or no association among groups. For example, under H₀, the regressor does not affect the outcome.
Read 15 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Oct 20
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
2. Discrete Distributions:

Discrete distributions are used when the data can take on only specific, distinct values. These values are often integers, like the number of sales calls made or the number of customers that converted.
Read 13 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Oct 18
Top 10 Python Libraries for Generative AI You Need to Master in 2025

(The tools behind document agents, intelligent assistants, and next-gen interfaces.)

Everything you need to know: 🧵 Image
1. LangChain

The backbone of intelligent LLM apps.

Build agents that:
✅ Reason
✅ Use tools
✅ Remember conversations
✅ Access APIs

If you're building anything with GPTs, LangChain is your starting point.

langchain.com
2. LangGraph

LangChain + DAGs = LangGraph.

It powers:
- Multi-agent workflows
- Conditional logic
- Real-time state management

If you're serious about production AI agents, this is a must.
langgraph.dev
Read 15 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Oct 17
AI Engineering Toolkit

A curated list of 100+ LLM libraries and frameworks for training, fine-tuning, building, evaluating, deploying, RAG, and AI Agents.

100% Open Source Image
Get it here:

I have one more thing before you go.

If you want to become a generative AI data scientist in 2025 ($200,000 career), then I'd like to help:github.com/Sumanth077/ai-…
🚨 NEW WORKSHOP: I'm sharing one of my best AI Projects for FREE:

How I built an AI Customer Segmentation Agent with Python:

- Scikit Learn
- K-Means
- LangChain
- LangGraph
- OpenAI

👉Register here (740+ Registered): learn.business-science.io/ai-registerImage
Read 5 tweets

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