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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
Jul 1
🚨 Say goodbye to manual ETL

Cleaned a 100k-word PDF dataset in 3 lines of Python code: Image
1. What is DocETL?

DocETL is a system for LLM-powered data processing.

You can create LLM-powered data processing pipelines. Image
2. Quick Example:

I made a quick, messy-PDF-to-Structured Output pipeline in 3 lines of Python: Image
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🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jun 30
🚨 Synthetic Data is the Future of AI

Introducing The Synthetic Data Vault (SDV).

This is what you need to know: Image
Synthetic Data is the Future of AI

Synthetic data keeps your data private.

SDV generates fake datasets that look REAL.

Here's how: Image
I built a 100k-row customer dataset in 4 lines:

Perfect for HIPAA-compliant Machine Learning & AI.

Google Colab Example: colab.research.google.com/drive/1L6i-JhJ…Image
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🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jun 29
Logistic Regression is the most important foundational algorithm in Classification Modeling.

In 2 minutes, I'll crush your confusion.

Let's dive in: Image
1. Logistic regression is a statistical method used for analyzing a dataset in which there are one or more independent variables that determine a binary outcome (in which there are only two possible outcomes). This is commonly called a binary classification problem.
2. The Logit (Log-Odds):

The formula estimates the log-odds or logit. The right-hand side is the same as the form for linear regression. But the left-hand side is the logit function, which is the natural log of the odds ratio. The logit function is what distinguishes logistic regression from other types of regression.Image
Read 9 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jun 27
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 12 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jun 27
🚨BREAKING: New Python library for Bayesian Marketing Mix Modeling and Customer Lifetime Value

It's called PyMC Marketing.

This is what you need to know: 🧵 Image
1. What is PyMC Marketing?

PyMC-Marketing is a state-of-the-art Bayesian modeling library that's designed for Marketing Mix Modeling (MMM) and Customer Lifetime Value (CLV) prediction.
2. Benefits

- Incorporate business logic into MMM and CLV models
- Model carry-over effects with adstock transformations
- Understand the diminishing returns
- Incorporate time series and decay
- Causal identification Image
Read 9 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jun 26
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

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