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🔥 Matt Dancho (Business Science) 🔥 Profile picture
@mdancho84
May 20, 2023 • 7 tweets • 5 min read • Read on X
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As a data scientist, productivity is a 10X super power.

Here's a short list of AI tools to help data scientists with: 🧵

#ai #datascience #career #skills #tools Image
1. Writing code

AI pair programming is a huge benefit.

Tools like #chatgpt & github #copilot can help debug complex code and replace Googling + Stack Overflowing for common scripting.

Key skill: ChatGPT prompting (more on this in my free ChatGPT for Data Scientists) Image
2. Code Quality & Documentation

Great products have great documentation. AI can help produce documentation, comment code, and replace time-consuming manual documentation with automated AI docs.

Key Skill: Using @mintlify to build your docs: mintlify.com Image
3. Presentations

Great data scientists are storytellers. Use persuasion to your advantage.

Key Skill: Generating images with AI using @midjourney_ai . midjourney.com Image
I'm road-testing all of these.

And I've been quietly researching #ChatGPT for Data Scientists (My NUMBER 1 TOOL) for the past 4 months.

I have good news - I'm ready to reveal my chatgpt research!
If you want to understand how ChatGPT can make you a better data scientist (and mistakes to avoid)...

I'll be sharing my research in a Free WORKSHOP: ChatGPT for Data Scientists (Wednesday, June 7th)!
What's Your Next Step?

Join me and 1,000 data scientists as we crush AI in my LIVE ChatGPT for Data Scientists Workshop.

Seats are limited (1,000 max).

👉Register Here: us02web.zoom.us/webinar/regist… Image

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More from @mdancho84

🔥 Matt Dancho (Business Science) 🔥 Profile picture
May 23
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 13 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
May 22
Top 7 most important statistical analysis concepts that have helped me as a Data Scientist.

This is a complete 7-step beginner ROADMAP for learning stats for data science. Let's go: Image
Step 1: Learn These Descriptive Statistics

Mean, median, mode, variance, standard deviation. Used to summarize data and spot variability. These are key for any data scientist to understand what’s in front of them in their data sets. Image
2. Learn Probability

Know your distributions (Normal, Binomial) & Bayes’ Theorem. The backbone of modeling and reasoning under uncertainty. Central Limit Theorem is a must too. Image
Read 12 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
May 22
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 12 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
May 18
Stop doing Customer Segmentation with plain vanilla Scikit Learn.

Add these 7 Python libraries to your RFM, clustering, and
customer segmentation projects: Image
1. Data preparation

- load data with pandas
- impute/mask with Feature-engine

Website: feature-engine.trainindata.com/en/latest/inde…Image
2. Feature creation:

- derive recency/frequency/monetary features
- Use rfm or Lifetimes

Github: github.com/sonwanesuresh9…Image
Read 9 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
May 17
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 are 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. Image
Read 11 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
May 15
Understanding P-Values is essential for improving regression models.

In 2 minutes, I'll crush your confusion.

Let's go: 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. Image
2. Null Hypothesis (H₀):

The null hypothesis is the default position that there is no relationship between two measured phenomena or no association among groups. For example, under H₀, the regressor does not affect the outcome. Image
Read 15 tweets

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