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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 15
The AI Agent Development Process.

How to go from idea to production.

(A thread) 🧵 Image
1. What is the AI Agent Development Process?

A repeatable path to ship an agent from idea to production: define → design → build → train → validate → deploy → monitor → improve.
2. Phases of the Process

Step 1: Defining Purpose

- Identify goals & users
- Determine objectives and success criteria (SLA, budget, accuracy)
Read 11 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Nov 11
K-means is one of the most powerful algorithms for data scientists.

But it's confusing for beginners. Let's fix that: Image
1. What is K-means?

Is a popular unsupervised machine learning algorithm used for clustering. It's a core algorithm used for customer segmentation, inventory categorization, market segmentation, and even anomaly detection. Image
2. Unsupervised:

K-means is an unsupervised algorithm that is used on data with no labels or predefined outcomes. The goal is not to predict a target output, but to explore the structure of the data by identifying patterns, clusters, or relationships within the dataset. Image
Read 11 tweets
🔥 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

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