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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
Scrolly
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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πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture

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More from @mdancho84

πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Feb 23
6 statistical methods that can be used for A/B Testing (and when to use them). 🧡 Image
A/B Testing is a staple of data science and data analyst interviews.

And it's the Number 1 technique that companies benefit from in improving customer revenue.

So here's a 6 of the most common stat methods used in A/B testing.

Let's dive in.
1. Z-Test (Standard Score Test):

Ideal for large sample sizes (typically over 30) and when the population variance is known.

Compares the mean of two groups to see if they are different from each other.

Often used in conversion rate optimization, click-through rates.
Read 11 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Feb 22
It took me 5 years to master all 24 of these machine learning concepts.

In the next 24 days, I'll teach them to you one by one (with examples of how I've used them). Here's what's coming:

1. Linear Regression
2. Clustering
3. Decision Tree
4. Neural Networks
5. Reinforcement Learning
6. Logistic Regression
7. Naive BayesImage
8. Supervised Learning
9. Support Vector Machine
10. Probability
11. Random Forest
12. Variance
13. Evaluation Metrics
14. Bagging
15. Data Wrangling
16. Dimensionality Reduction
17. K-nearest Neighbors Algorithm
18. Programming
19. Regularization
20. Statistics
21. Binomial Distribution
22. Bootstrap Sampling
23. Exploratory Data Analysis
24. Data Collection
25. There's a new problem that has surfaced that is changing data science-- Companies NOW want AI.

AI is the single biggest force of our decade.

Yet 99% of data scientists are ignoring it.

That's a huge advantage to you. I'd like to help.
Read 5 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Feb 20
Data Science for Business.

The book that helped me connect the dots. Let's dive in: Image
1. CRISP Data Mining Process

The foundation for applying data science to business is the CRISP method.

This is a helpful framework for integrating data science with the business understanding. Image
2. Machine Learning Predictions as Probabilities

One of the most important part of machine learning is probability.

We can estimate the probability from machine learning predictions.

Once you get this, the next framework opens up: Image
Read 7 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Feb 20
90% of data scientists can improve their SQL for business intelligence.

In 3 minutes, learn the 20% of SQL gets 80% of results: Image
πŸ” SELECT Basics:

Start with SELECT * FROM table to retrieve all rows & columns.

Remember, SQL isn’t case-sensitiveβ€”but capitalizing keywords (SELECT, FROM) makes your queries easier to read. Image
πŸ“ Choosing Specific Fields:

Instead of the star (*), list only the fields you need (e.g., field1, field2) and rename them using AS for clarity.

Clear queries lead to clear insights. Image
Read 10 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Feb 19
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 β€” 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
Read 7 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Feb 19
Decision trees are a fundamental tool for every Data Scientist.

But for 3 years, I was hesitant to use them.

In 3 minutes, I'll destroy your confusion. Let's dive in: Image
1. Decision Tree:

A decision tree is a graphical representation used for decision-making and data analysis. It resembles a tree structure and is commonly used in machine learning, specifically in classification and regression tasks.
2. Structure:

A decision tree consists of nodes and branches. The top node is known as the root node, and it represents the entire dataset.

Decision Nodes: These are where the splits happen, based on a certain condition or attribute.

Leaf/Terminal Nodes: These nodes represent the outcome of the decision process.
Read 11 tweets

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