Matt Dancho (Business Science) Profile picture
Jul 30, 2022 10 tweets 5 min read Read on X
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

Mar 17
OpenAI, Google, and Anthropic just published guides on:

• Prompt engineering
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• AI in business
• 601 AI use cases

9 of the best guides you can't miss: Image
1. AI in the Enterprise by OpenAI

Grab the PDF: cdn.openai.com/business-guide…Image
2. A practical guide to building agents by OpenAI

Download here: cdn.openai.com/business-guide…
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Mar 15
80% of data scientists say they want to build AI agents.

Almost none of them can answer this question:

Which agentic pattern should you actually use? Image
There are 7. And picking the wrong one breaks your entire workflow.

Here's the quick breakdown:
1. Parallel — multiple agents run at the same time. Use when tasks are independent. Faster output.

2. Sequential — agents run one after another. Use when each step depends on the last. More reliable.
Read 9 tweets
Mar 14
Harvard just open-sourced its entire ML Systems curriculum.
Free. Public. 6 pillars. Hundreds of pages.

And it won't get most data scientists any closer to a $150K AI role.

Here's why. Image
The book covers:

1. System Design
2. Data Engineering
3. Model Deployment
4. MLOps and Monitoring
5. Edge AI
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That's genuinely excellent material. Prof. Vijay Janapa Reddi built something worth bookmarking.

But here's what I've watched happen with 7,500 students over 8 years:

The ones who read everything and built nothing stayed stuck.
Read 9 tweets
Mar 1
🚨McKinsey just dropped how to build agentic AI (that works)

Here's everything you need to know in 2 minutes: Image
1. Stop building agents; Start fixing workflows

The mistake every organization makes: falling in love with your new AI agent.

The solution: Identify the pain points in your process. Then use agents to connect analytics and gen AI into 1 seamless process.
2. Not everything needs an Agent

Stop agent-ifying everything.

Ask: "Is this a problem that actually needs solving with agents?"

Alternatives to Agents:

- Automation
- NLP
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Read 9 tweets
Feb 24
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 5 tweets
Feb 22
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 11 tweets

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