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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 20
Forecasting time series is what made me stand out as a data scientist.

But it took me 1 year to master ARIMA.

In 1 minute, I'll teach you what took me 1 year.

Let's go. 🧵 Image
1. ARIMA and SARIMA are both statistical models used for forecasting time series data, where the goal is to predict future points in the series.
2. Business Uses: I got my start with ARIMA using it for sales demand forecasting. But ARIMA and forecasting are also used heavily in econometrics, finance, retail, energy demand, and any situation where you need to know the future based on historical time series data.
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🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jul 20
Why should you learn Polars in Python?

This is why. 🧵

#python Image
Polars is a fast and efficient DataFrame library designed for data analysis and manipulation in Rust and Python.

It is built to provide high-performance data processing capabilities, often outperforming traditional libraries like pandas, especially with large datasets.
1. Performance: Polars is designed with performance in mind, leveraging Rust's speed and safety to handle large datasets efficiently.
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🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jul 18
Bayes' Theorem is a fundamental concept in data science.

But it took me 2 years to understand its importance.

In 2 minutes, I'll share my best findings over the last 2 years exploring Bayesian Statistics.

Let's go. 🧵 Image
1. Background:

"An Essay towards solving a Problem in the Doctrine of Chances," was published in 1763, two years after Bayes' death.

In this essay, Bayes addressed the problem of inverse probability, which is the basis of what is now known as Bayesian probability.
2. Bayes' Theorem:

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It describes how to revise existing predictions or theories in light of new evidence, a process known as Bayesian inference.
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🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jul 5
XGBoost is now the go-to number 1 must-have algorithm in my data science toolkit.

But for years, I had no clue what I was doing. In 3 minutes, I’ll share 3 months of research (business case included).

Let’s go: 🧵 Image
1. XGBoost, which stands for Extreme Gradient Boosting, is an advanced implementation of the gradient boosting machine (GBM) algorithm. It was developed to optimize both computational speed and model performance.
2. Gradient Boosting Machine (GBM): GBMs are an ensemble approach that combines multiple weak learners (typically decision trees) to create a strong predictive model.
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🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jul 3
I completed my first Python Sales Forecasting Project using Nixtla's StatsForecast and a NEW IDE for Data Scientists.

Here's how I want to help you do it too: 🧵

#python #rstats Image
Sales forecasting is a struggle for many data scientists.

Most college DS programs don't teach it (or gloss over it).

But it's one of the most important skills that companies need.

There are 2 incredible tools out there that I want to share with you:
1. Nixtla's StatsForecast

I love this library for classic econometric models like ARIMA, ETS (Exponential Smoothing), GARCH, and AutoARIMA.

It's super easy to automate forecasting.

And it's got awesome plotting functions, conformal intervals, and more.
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🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jun 29
Bayes' Theorem is a fundamental concept in data science.

But it took me 2 years to understand its importance.

In 2 minutes, I'll share my best findings over the last 2 years exploring Bayesian Statistics.

Let's go. Image
1. Background: "An Essay towards solving a Problem in the Doctrine of Chances," was published in 1763, two years after Bayes' death. In this essay, Bayes addressed the problem of inverse probability, which is the basis of what is now known as Bayesian probability.
2. Bayes' Theorem: Bayes' Theorem provides a mathematical formula to update the probability for a hypothesis as more evidence or information becomes available. It describes how to revise existing predictions in light of new evidence, a process known as Bayesian inference.
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