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

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.

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Jan 21
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Tools like Tableau and PowerBI are about to become extinct.

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That's why a new breed of analytics is coming: AI Analytics. Image
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Jan 18
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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.

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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.

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Here are the highlights I’m thinking about (as someone who lives in Python + agents):Image
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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

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Jan 17
OpenAI, Google, and Anthropic just published guides on:

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Grab the PDF: cdn.openai.com/business-guide…Image
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Download here: cdn.openai.com/business-guide…
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