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πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
@mdancho84
May 20, 2023 β€’ 7 tweets β€’ 5 min read β€’ 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).

πŸ‘‰Register Here: us02web.zoom.us/webinar/regist… Image

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More from @mdancho84

πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Jul 18
The concept that helped me go from bad models to good models: Bias and Variance.

In 4 minutes, I'll share 4 years of experience in managing bias and variance in my machine learning models. Let's go. 🧡 Image
1. Generalization:

Bias and variance control your models ability to generalize on new, unseen data, not just the data it was trained on. The goal in machine learning is to build models that generalize well. To do so, I manage bias and variance.
2. Low vs High Bias:

Models with low bias are usually complex and can capture the underlying patterns in data very well. They are flexible enough to fit the training data closely. Models with high bias are overly simple and cannot capture the complexity in the data. They often underfit the training data, meaning they perform poorly even on the data they were trained on.
Read 13 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Jul 17
K-means is an essential algorithm for Data Science.

But it's confusing for beginners.

Let me demolish your confusion: Image
1. K-Means

K-means is a popular unsupervised machine learning algorithm used for clustering. It's a core algorithm used for customer segmentation, inventory categorization, market segmentation, and even anomaly detection. Image
2. Unsupervised:

K-means is an unsupervised algorithm used on data with no labels or predefined outcomes. The goal is not to predict a target output, but to explore the structure of the data by identifying patterns, clusters, or relationships within the dataset.
Read 13 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Jul 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 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 8 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Jul 14
85% of data scientists do customer segmentation the WRONG WAY.

AI Agents fix thisβ€”here's how I made an AI that clusters customers & recommends marketing actions (and you can too). 🧡 Image
Traditional K-Means finds clusters, but that's just the start.

The real challenge?

Interpreting clusters for business value. Image
AI Agents summarize clusters, spot hidden patterns, and suggest personalized strategies. Image
Read 7 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Jul 14
The 3 types of machine learning (that every data scientist should know).

In 3 minutes I'll eviscerate your confusion. Let's go: 🧡 Image
1. The 3 Fundamental Types of Machine Learning:

- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning.

Let's break them down:
2. Supervised Learning:

Supervised Learning maps a set of inputs (features) to an output (target). There are 2 types: Classification and Regression.
Read 11 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Jul 13
Correlation is the skill that has singlehandedly benefitted me the most in my career.

In 3 minutes I'll demolish your confusion (and share strengths and weaknesses you might be missing).

Let's go: Image
1. Correlation:

Correlation is a statistical measure that describes the extent to which two variables change together. It can indicate whether and how strongly pairs of variables are related. Image
2. Types of correlation:

Several types of correlation are used in statistics to measure the strength and direction of the relationship between variables. The three most common types are Pearson, Spearman Rank, and Kendall's Tau. We'll focus on Pearson since that is what I use 95% of the time.Image
Read 12 tweets

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