๐Ÿ”ฅ Matt Dancho (Business Science) ๐Ÿ”ฅ Profile picture
Jul 30, 2022 โ€ข 10 tweets โ€ข 5 min read โ€ข Read on X
How my life is changing as a direct result of attending the #RStudioConf ๐Ÿงต

#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-maโ€ฆ

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More from @mdancho84

Sep 16
Tableau is about to die.

Introducing PandasAI, a free alternative for fast Business Intelligence.

Let dive in: Image
1. PandasAI

PandaAI transforms your natural language questions into actionable insights โ€” fast, smartly, and effortlessly.
2. Powerful dashboards in seconds

The problem with Tableau? Analysts have to build them from scratch.

PandasAI solves this problem making it lightning fast to create dashboards from multiple sources. Image
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Sep 15
RIP Tableau and PowerBI.

Enter Julius AI.

This is what Julius can do: Image
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That's why I'm so excited about this new tool: Julius AI Image
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Sep 14
R-squared is one of the most commonly used metrics to measure performance.

But it took me 2 years to figure out the 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).
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Sep 13
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
Sep 13
๐Ÿšจ BREAKING: Microsoft launches a free Python library that converts ANY document to Markdown

Introducing Markitdown. Let me explain. ๐Ÿงต Image
1. Document Parsing Pipelines

MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. Image
2. Supported Documents

MarkItDown supports:

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- Word
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- Audio (EXIF metadata and speech transcription)
- HTML
- Text-based formats (CSV, JSON, XML)
- ZIP files (iterates over contents)
- Youtube URLs
- EPubs Image
Read 10 tweets
Sep 8
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
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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
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Want to become a Generative AI Data Scientist in 2025? Image
Read 6 tweets

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