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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
Mar 13
PowerBI and Tableau are about to die.

Vanna AI is a new open-source Python framework that enables realtime analytics and SQL generation.

Let's explore: Image
Vanna is an MIT-licensed open-source Python RAG (Retrieval-Augmented Generation) framework for SQL generation and related functionality. Image
You can then use Vanna AI to interact with your SQL database or ERP system. Vanna connects:

- SQL Database
- Vector Storage
- LLMs
- Front Ends / APIs Image
Read 9 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Mar 12
Data scientists are out.

The Generative AI Data Scientist is in.

Let me explain: Image
Companies are sitting on mountains of unstructured data.

PDF
Word docs
Meeting notes
Emails
Videos
Audio Transcripts

This is useful data. But it's unusable in its existing form. Image
The AI data scientist builds the systems to analyze information, gain business insights, and automates the process.

- Models the system
- Use AI to extract insights
- Drives predictive business insights

Want to become a Generative AI Data Scientist in 2025? Image
Read 5 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Mar 11
Principal Component Analysis (PCA) is the gold standard in dimensionality reduction.

But PCA is hard to understand for beginners.

Let me destroy your confusion: Image
1. What is PCA?

PCA is a statistical technique used in data analysis, mainly for dimensionality reduction. It's beneficial when dealing with large datasets with many variables, and it helps simplify the data's complexity while retaining as much variability as possible.
2. PCA has 5 steps:

1. Standardization
2. Covariance Matrix Computation
3. Eigen Vector Calculation
4. Choosing Principal Components
5. Transforming the data
Read 11 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Mar 10
K-means is an essential algorithm for Data Science.

But it's confusing for beginners.

Let me demolish your confusion: Image
1. K-Means

K-means is a popular unsupervised machine learning algorithm used for clustering. It's a core algorithm used for customer segmentation, inventory categorization, market segmentation, and even anomaly detection. Image
2. Unsupervised:

K-means is an unsupervised algorithm used on data with no labels or predefined outcomes. The goal is not to predict a target output, but to explore the structure of the data by identifying patterns, clusters, or relationships within the dataset.
Read 12 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Mar 9
R-squared is one of the most commonly used metrics to measure performance.

But it took me 2 years to figure out mistakes that were killing my regression models.

In 2 minutes, I'll share how I fixed 2 years of mistakes (and made 50% more accurate models than my peers). Let's go:Image
1. R-squared (R2):

R2 is a statistical measure used in regression models that provides a measure of how well the observed outcomes are replicated by the model, based on the proportion of total variation of outcomes explained by the model.
2. Range (0 to 1):

R2 ranges from 0 to 1. A higher R2 value indicates a better fit between the prediction and the actual data. For example, an R2 value of 0.70 suggests that 70% of the variance in the dependent variable is predictable from the independent variable(s).
Read 10 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Mar 8
Correlation is the skill that has singlehandedly benefitted me the most in my career.

In 3 minutes I'll demolish your confusion (and share strengths and weaknesses you might be missing).

Let's go: Image
1. Correlation:

Correlation is a statistical measure that describes the extent to which two variables change together. It can indicate whether and how strongly pairs of variables are related.
2. Types of correlation:

There are several types of correlation used in statistics to measure the strength and direction of the relationship between variables. The three most common types are: Pearson, Spearman Rank, and Kendall's Tau. We'll focus on Pearson since that is 95% of the time what I use.
Read 11 tweets

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