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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
Apr 22
Understanding regression models is essential in data science.

In 4 minutes, I'll demolish your confusion. Let's go: Image
1. The 6 Diagnostic Checks Every Data Scientist Should Run

Once you've built a regression model, your job isn't done. These 6 checks will tell you whether your model can actually be trusted.
2. Posterior Predictive Check

Ask yourself: do the model-predicted lines resemble the observed data line? If your model is a good fit, simulated data from it should look similar to your actual data. When they diverge wildly, your model is missing something important. Image
Read 9 tweets
Matt Dancho (Business Science) Profile picture
Apr 19
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 10 tweets
Matt Dancho (Business Science) Profile picture
Apr 18
RIP manual research workflows.

Someone just open-sourced what comes after Karpathy’s AutoResearch.

It’s called AutoResearchClaw.

And this thing is insane. Image
A few weeks ago, Karpathy showed where research was heading:

AI agents running the experiment loop.

That was already a big signal.

AutoResearchClaw takes it even further.

It doesn’t just help with research.

It tries to automate the entire scientific method end-to-end.
You give it a raw idea.

One CLI command.

Then it runs.

Not just “brainstorming.”

Not just “summarizing.”

Actually running the workflow.
Read 8 tweets
Matt Dancho (Business Science) Profile picture
Apr 15
These 7 statistical analysis concepts have helped me as an AI Data Scientist.

Let's go: 🧵 Image
Step 1: Learn These Descriptive Statistics

Mean, median, mode, variance, standard deviation. Used to summarize data and spot variability. These are key for any data scientist to understand what’s in front of them in their data sets. Image
2. Learn Probability

Know your distributions (Normal, Binomial) & Bayes’ Theorem. The backbone of modeling and reasoning under uncertainty. Central Limit Theorem is a must too. Image
Read 9 tweets
Matt Dancho (Business Science) Profile picture
Apr 7
🚨 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 7 tweets
Matt Dancho (Business Science) Profile picture
Apr 2
RIP document extractors.

Google just released LangExtract: Open-source. Free. Better than $100K enterprise tools.

Here’s what it does: 🧵 Image
What it does:

→ Extracts structured data from messy text
→ Grounds every field to the exact source location
→ Handles 100+ page docs
→ Generates interactive HTML for verification
→ Works with Gemini + local models Image
What it replaces:

→ Regex/fragile parsing
→ Custom NER pipelines
→ Expensive extraction APIs
→ Manual data entry Image
Read 8 tweets

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