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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
Aug 9
Stop doing Customer Segmentation with plain vanilla Scikit Learn.

Add these 7 Python libraries to your RFM, clustering, and
customer segmentation projects: Image
1. Data preparation

- load data with pandas
- impute/mask with Feature-engine

Website: feature-engine.trainindata.com/en/latest/inde…Image
2. Feature creation:

- derive recency/frequency/monetary features
- Use rfm or Lifetimes

Github: github.com/sonwanesuresh9…Image
Read 9 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Aug 9
RIP Data Scientists.

The Generative AI Data Scientist is NOW what companies want.

This is actually good news. 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 Image
Read 6 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Aug 8
RIP Tableau and PowerBI.

Enter Julius AI.

This is what Julius can do: Image
1. The $10 Billion problem with Tableau and PowerBI?

Dashboards are static.

But businesses are dynamic.

That's why I'm so excited about this new tool: Julius AI Image
2. Julius AI is for Data Analytics

Julius AI is built to analyze any database, PDF, or spreadsheet, and combine results into summarized business intelligence in seconds. Image
Read 11 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Aug 8
Boxplots are one of the most useful tools in my Data Science arsenal.

In 6 minutes, I'll eviscerate your confusion.

Let's dive in. Image
1. What is a boxplot?

A boxplot is a standardized way of displaying the distribution of data based on a five-number summary: minimum, first quartile (Q1), median, third quartile (Q3), and maximum. Image
2. Invention:

The boxplot was invented in 1969 by John Tukey, as part of his pioneering work in data visualization. Tukey's EDA emphasized the importance of using simple graphical and numerical methods to start understanding the data before making any assumptions about its underlying distribution or applying complex statistical models. The boxplot emerged from this philosophy. Tukey's boxplot was designed to be a quick and easy way to visualize the distribution of data.
Read 12 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Aug 7
The 3 types of machine learning (that every data scientist should know).

In 3 minutes I'll eviscerate your confusion. Let's go: 🧵 Image
1. The 3 Fundamental Types of Machine Learning:

- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning.

Let's break them down:
2. Supervised Learning:

Supervised Learning maps a set of inputs (features) to an output (target). There are 2 types: Classification and Regression.
Read 11 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Aug 7
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 13 tweets

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