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

• • •

Missing some Tweet in this thread? You can try to force a refresh
 

Keep Current with 🔥 Matt Dancho (Business Science) 🔥

🔥 Matt Dancho (Business Science) 🔥 Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @mdancho84

Dec 6
Agentic AI: A comprehensive survey of architectures, appications, and future directions (A 37 page PDF)

Here are the best parts: Image
1. The Dual-Paradigm Framework

The survey argues that Agentic AI research must be categorized to avoid conceptual retrofitting (applying old symbolic models to new systems).
2. Key Findings and Implications

Paradigm-Market Fit: The choice of architecture is strategic and dictated by domain constraints (e.g., symbolic for high-stakes, neural for complex data analysis). Image
Read 8 tweets
Dec 1
RIP BI Dashboards.

Tools like Tableau and PowerBI are about to become extinct.

This is what's coming (and how to prepare): Image
I've never been a fan of Tableau and PowerBI.

Static dashboards don't answer dynamic business questions.

That's why a new breed of analytics is coming: AI Analytics. Image
AI + Data Science is the future:

AI tools like:

- LangChain
- LangGraph
- OpenAI API

Are being combined with:

- SQL Databases
- Machine Learning
- Prediction

And the results are exactly what businesses need: real-time predictive insights. Image
Read 7 tweets
Nov 30
Stop Prompting LLMs.
Start Programming LLMs.

Introducing DSPy by Stanford NLP.

This is why you need to learn it: Image
1. Why DSPy?

DSPy is the open-source framework for programming—rather than prompting—language models.

It allows you to iterate fast on building modular AI systems.
2. Modules that express AI programmer-centric way

To build enterprise-grade AI, you need to be able to build modular codebases.

DSPy makes it easy to create these LLM tasks modularly. Image
Read 8 tweets
Nov 29
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.

After presenting the basic process of building these models, you explore how to scale up model training and handle long texts. Image
Read 10 tweets
Nov 28
🚨BREAKING: New Python library for agentic data processing and ETL with AI

Introducing DocETL.

Here's what you need to know: Image
1. What is DocETL?

It's a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks.

It offers:

- An interactive UI playground
- A Python package for running production pipelines Image
2. DocWrangler

DocWrangler helps you iteratively develop your pipeline:

- Experiment with different prompts and see results in real-time
- Build your pipeline step by step
- Export your finalized pipeline configuration for production use Image
Read 8 tweets
Nov 27
🚨 BREAKING: Microsoft launches a free Python library that converts ANY document to Markdown

Introducing Markitdown. Let me explain. 🧵 Image
1. Document Parsing Pipelines

MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. Image
2. Supported Documents

MarkItDown supports:

- PDF
- PowerPoint
- Word
- Excel
- Images (EXIF metadata and OCR)
- Audio (EXIF metadata and speech transcription)
- HTML
- Text-based formats (CSV, JSON, XML)
- ZIP files (iterates over contents)
- Youtube URLs
- EPubs Image
Read 9 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us!

:(