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🔥 Matt Dancho (Business Science) 🔥 Profile picture
@mdancho84
Aug 21, 2022 • 9 tweets • 3 min read • Read on X
What could go wrong?

LOL. 😂

Plus the 3 #datascience books that helped me learn #stats the most. 🧵

#rstats Image
I’m not saying you need to be an expert in advanced calculus to do machine learning…

BUT, there is a big difference between someone that does vs someone that does NOT have a good foundation in stats when it comes to getting & explaining business results.
My thought process back in the day was to obtain a great foundation in stats and machine learning at the same time.

So here’s what helped me. I read a ton of books.

Here are the 3 books that helped me learn data science the most...
1. R for Data Science (Wickham & Grolemund) r4ds.had.co.nz
2. Introduction to Statistical Learning (James, Witten, Hastie, & Tibshirani) statlearning.com
3. Applied Predictive Modeling (Kuhn & Johnson) appliedpredictivemodeling.com
Keep in mind that I’ve read 300+ books on stats, ML, time series, …

But these were the 3 best. Ones I got a ton of applied value out of.
Now you’re probably thinking reading these 3 books will take a long time, and still might not get you the whole way to data scientist.

That’s why I want to help you speed up the process.

So it doesn’t take you 5 years to learn data science (like it did me).
I compiled the top 10 most important skills that helped me learn and get results from data science.

And I put these top 10 data science skills into a FREE 40-minute webinar.

Enjoy!

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

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More from @mdancho84

🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jul 25
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 6 of the most common stat methods used in A/B testing.
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 10 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jul 23
Type 1 and Type 2 errors are confusing.

In 3 minutes, I'll demolish your confusion.

Let's dive in. 🧵 Image
1. Type 1 Error (False Positive): This occurs when the pregnancy test tells Tom, the man, that he is pregnant. Obviously, Tom cannot be pregnant, so this result is a false alarm. In statistical terms, it's detecting an effect (in this case, pregnancy) when it actually doesn't exist.
2. Type 2 Error (False Negative): This happens when Lisa, who is actually pregnant, takes the test, and it tells her that she's not pregnant. The test failed to detect the real condition of pregnancy. In statistical terms, it's failing to detect a real effect (pregnancy) that is there.
Read 10 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jul 23
Why all data scientists should learn Polars in Python.

This is why: 🧵 Image
The more I use Pandas, the more I become frustrated.

1. Pandas is slow.

The Polars API is between 3X and 3500X faster depending on the task. With large data, Polars is routinely 20X faster.
2. Pandas API is a mess. Things I hate that polars doesn't do:

- Index and multi-index.
- iloc vs loc vs at vs iat.
- Axis parameters (is 90% axis = 0 rows when I want 1 for columns)
- Inconsistent output: Sometimes I get a series, other times a data frame.
Read 7 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jul 20
Forecasting time series is what made me stand out as a data scientist.

But it took me 1 year to master ARIMA.

In 1 minute, I'll teach you what took me 1 year.

Let's go. 🧵 Image
1. ARIMA and SARIMA are both statistical models used for forecasting time series data, where the goal is to predict future points in the series.
2. Business Uses: I got my start with ARIMA using it for sales demand forecasting. But ARIMA and forecasting are also used heavily in econometrics, finance, retail, energy demand, and any situation where you need to know the future based on historical time series data.
Read 12 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jul 20
Why should you learn Polars in Python?

This is why. 🧵

#python Image
Polars is a fast and efficient DataFrame library designed for data analysis and manipulation in Rust and Python.

It is built to provide high-performance data processing capabilities, often outperforming traditional libraries like pandas, especially with large datasets.
1. Performance: Polars is designed with performance in mind, leveraging Rust's speed and safety to handle large datasets efficiently.
Read 8 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jul 18
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 8 tweets

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