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🔥 Matt Dancho (Business Science) 🔥 Profile picture
@mdancho84
Sep 17, 2022 • 10 tweets • 6 min read • Read on X
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Shiny is a powerful tool that data scientists can use for web apps & production.

But most data scientists struggle.

Here are 7 resources on shiny that helped me.

#rstats #shiny #excel #python
1. The Shiny website

The 1st place to go to learn shiny.

shiny.rstudio.com
2. Flexdashboard website

Flexdashboard combines Rmarkdown & Shiny to make quick apps.

pkgs.rstudio.com/flexdashboard/
3. Shiny Widgets gallery

See dozens of example reactive widget input / outputs for shiny

shiny.rstudio.com/gallery/widget…
4. shinyWidgets by dreamrs

Advanced & customizable reactive widgets that can really take your shiny apps to the next level

dreamrs.github.io/shinyWidgets/i…
5. HTML Widgets

Interactive visuals for shiny apps

htmlwidgets.org/showcase_leafl…
6. Shiny JS

Makes it easy to add JavaScript actions to your shiny apps.

deanattali.com/shinyjs/
7. Bslib

Upgrade shiny From Bootstrap 3 to 4 or 5 and makes it easy to make custom themes.

rstudio.github.io/bslib/
And if you want all of these 7 R packages plus 93 more in one consolidated #cheatsheet, download my ultimate #R cheat sheet.

business-science.io/r-cheatsheet.h…
One last resource.

If you've been struggling to learn R, I’d like to help.

I put together a free R webinar that consolidates the 10 secrets that helped me in my career.

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

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More from @mdancho84

🔥 Matt Dancho (Business Science) 🔥 Profile picture
Dec 8
🚨 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 10 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
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
🔥 Matt Dancho (Business Science) 🔥 Profile picture
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
🔥 Matt Dancho (Business Science) 🔥 Profile picture
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
🔥 Matt Dancho (Business Science) 🔥 Profile picture
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
🔥 Matt Dancho (Business Science) 🔥 Profile picture
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

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