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πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
@mdancho84
Jul 30, 2022 β€’ 10 tweets β€’ 5 min read β€’ Read on X
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How my life is changing as a direct result of attending the #RStudioConf 🧡

#rstats
Just 3 days ago, I had the pleasure of watching the #rstudioconf2022 kick off.

I've been attending since 2018 and watching even longer than that.

And, I was just a normal spectator in the audience until this happened.
@topepos and @juliasilge's keynote showed all of the open source work their team has been working on to build the best machine learning ecosystem in R called #tidymodels.

And then they brought this slide up.
Max and Julia then proceeded to talk about how the community members have been working on expanding the ecosystem.

- Text Recipes for Text
- Censored for Survival Modeling
- Stacks for Ensembles

And then they announced me and my work on Modeltime for Time Series!!!
I had no clue this was going to happen.

Just a spectator in the back.

My friends to both sides went nuts. Hugs, high-fives, and all.

My students in my slack channel went even more nuts.
Throughout the rest of the week, I was on cloud-9.

My students that were at the conf introduced themselves.

Much of our discussions centered around Max & Julia's keynote and the exposure that modeltime got.
And all of this wouldn't be possible without the support of this company. Rstudio / posit.

So, I'm honored to be part of something bigger than just a programming language.

And if you'd like to learn more about what I do, I'll share a few links.
The first is my modeltime package for #timeseries.

This has been a 2-year+ passion project for building the premier time series forecasting system.

It now has multiple extensions including ensembles, resampling, deep learning, and more.

business-science.github.io/modeltime/
The second is my company @bizScienc.

For the past 4-years I've dedicated myself to teaching students how to apply data science to business.

I have 3000+ students worldwide.

Here are some of my tribe that I met at #rstudioconf2022.
The third is my 40-minute webinar.

I put a free presentation together to help you on your journey to become a data scientist.

A few things I talk about:

Modeltime for Time Series.
Tidymodels & H2O for Machine Learning
Shiny for Web Apps
and 7 more!

learn.business-science.io/free-rtrack-ma…

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More from @mdancho84

πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Jul 14
85% of data scientists do customer segmentation the WRONG WAY.

AI Agents fix thisβ€”here's how I made an AI that clusters customers & recommends marketing actions (and you can too). 🧡 Image
Traditional K-Means finds clusters, but that's just the start.

The real challenge?

Interpreting clusters for business value. Image
AI Agents summarize clusters, spot hidden patterns, and suggest personalized strategies. Image
Read 7 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Jul 14
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
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Jul 13
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. Image
2. Types of correlation:

Several types of correlation are 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 what I use 95% of the time.Image
Read 12 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Jul 11
When I was first exposed to the Confusion Matrix, I was lost.

There was a HUGE mistake I was making with False Negatives that took me 5 years to fix.

I'll teach you in 5 minutes. Let's dive in. 🧡 Image
1. The Confusion Matrix

A confusion matrix is a tool often used in machine learning to visualize the performance of a classification model. It's a table that allows you to compare the model's predictions against the actual values.
2. Correct Predictions:

True Positives (TP): These are cases in which the model correctly predicts the positive class.

True Negatives (TN): These are cases in which the model correctly predicts the negative class.
Read 11 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Jul 10
Understanding P-Values is essential for improving regression models.

In 2 minutes, I'll crush your confusion. Image
1. The p-value:

A p-value in statistics is a measure used to assess the strength of the evidence against a null hypothesis.
2. Null Hypothesis (Hβ‚€):

The null hypothesis is the default position that there is no relationship between two measured phenomena or no association among groups. For example, under Hβ‚€, the regressor does not affect the outcome.
Read 13 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Jul 7
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 or theories in light of new evidence, a process known as Bayesian inference.
Read 12 tweets

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