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🔥 Matt Dancho (Business Science) 🔥 Profile picture
@mdancho84
Aug 21, 2022 • 9 tweets • 3 min read • Read on X
What could go wrong?

LOL. 😂

Plus the 3 #datascience books that helped me learn #stats the most. 🧵

#rstats Image
I’m not saying you need to be an expert in advanced calculus to do machine learning…

BUT, there is a big difference between someone that does vs someone that does NOT have a good foundation in stats when it comes to getting & explaining business results.
My thought process back in the day was to obtain a great foundation in stats and machine learning at the same time.

So here’s what helped me. I read a ton of books.

Here are the 3 books that helped me learn data science the most...
1. R for Data Science (Wickham & Grolemund) r4ds.had.co.nz
2. Introduction to Statistical Learning (James, Witten, Hastie, & Tibshirani) statlearning.com
3. Applied Predictive Modeling (Kuhn & Johnson) appliedpredictivemodeling.com
Keep in mind that I’ve read 300+ books on stats, ML, time series, …

But these were the 3 best. Ones I got a ton of applied value out of.
Now you’re probably thinking reading these 3 books will take a long time, and still might not get you the whole way to data scientist.

That’s why I want to help you speed up the process.

So it doesn’t take you 5 years to learn data science (like it did me).
I compiled the top 10 most important skills that helped me learn and get results from data science.

And I put these top 10 data science skills into a FREE 40-minute webinar.

Enjoy!

learn.business-science.io/free-rtrack-ma… Image

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More from @mdancho84

🔥 Matt Dancho (Business Science) 🔥 Profile picture
Nov 1
🚨NEW Whitepaper on AI Agents by OpenAI

The maker of ChatGPT shares how it builds AI Agents.

Get the 34-page white paper here: Image
This Whitepaper covers:

1. Building, evaluating, and deploying AI agents
2. Architectures, tool integration, and scaling
3. Agent ops and evaluation frameworks

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:cdn.openai.com/business-guide…Image
🚨WANT TO BECOME A GENERATIVE AI DATA SCIENTIST IN 2025 ($200,000 career)?

Discover how I built an AI Customer Segmentation Agent with Python:

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

👉Register here (500 seats): learn.business-science.io/ai-registerImage
Read 5 tweets
🔥 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

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