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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
May 8
K-means is one of the most powerful algorithms for data scientists.

But it's confusing for beginners. Let's fix that: Image
1. What is 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 that is 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. Image
Read 11 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
May 6
🚨BREAKING: OpenAI just dropped a practical guide to building agents

There's a secret.

It unlocks agential workflows for data analysts, data scientists, and data engineers.

This is how: 🧵 Image
Here are my key takeaways by section of the report specifically addressing how data professionals can use the report.

1: What Is an Agent?

Agents are LLM‑powered systems that autonomously execute multi‑step workflows on a user’s behalf—going beyond single‑turn prompts to manage tool calls, conditional logic, and goal‑based loopsImage
Ideas for data professionals:

Data Analyst: Embed an agent in your BI dashboard

Data Scientist: Wrap your model evaluation pipeline in an agent that flags model drift and performance degradation

Data Engineer: Build an agent to monitor ETL jobs
Read 15 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
May 4
90% of data scientists struggle with time series.

But all it takes is mastering 1 technique: time series decomposition.

Here's why: Image
1. What is Time Series Decomposition?

A statistical method used to deconstruct a time series into several components, each representing underlying patterns in the data.

There are 3 key components: Trend, Seasonal, and Residual. Let's break them down. Image
2. Trend:

Trend is the long-term movement of the series. Typically, we use a smoother (LOESS, LOWESS) or moving average to calculate the trend. The key is that it removes the seasonal variation from the time series. Image
Read 8 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
May 3
A Python Library for Time Series by Salesforce.

Let me introduce you to Merlion. Image
1. What is Merlion?

Merlion is a Python library for time series intelligence.

It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance. Image
2. GUI Dashboard

Merlion makes time series analysis easier with a web-based dashboard.

This dashboard provides a great way to quickly experiment with many models on your own custom datasets. Image
Read 9 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
May 2
A Python Library for Time Series using Hidden Markov Models.

Let me introduce you to hmmlearn. Image
1. Hidden Markov Models

A Hidden Markov Model (HMM) is a statistical model that describes a sequence of observable events where the underlying process generating those events is not directly visible, meaning there are "hidden states" that influence the observed data, but you can only see the results of those states, not the states themselvesImage
2. HMM for Time Series with hmmlearn

hmmlearn implements the Hidden Markov Models (HMMs).

We can use HMM for time series. Example: Using HMM to understand earthquakes.

Tutorial: hmmlearn.readthedocs.io/en/latest/auto…Image
Read 9 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Apr 27
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 13 tweets

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