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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
Jun 12
🚨BREAKING: New Python library for agentic data processing and ETL with AI

Introducing DocETL.

Here's what you need to know: Image
1. What is DocETL?

It's a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks.

It offers:

- An interactive UI playground
- A Python package for running production pipelines Image
2. DocWrangler

DocWrangler helps you iteratively develop your pipeline:

- Experiment with different prompts and see results in real-time
- Build your pipeline step by step
- Export your finalized pipeline configuration for production use Image
Read 8 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jun 11
Python is insane for time series.

Case in point: Pytimetk 📈 Image
PyTimetk’s Mission: To make time series analysis easier, faster, and more enjoyable in Python.

Pytimetk uses a Polars backend for massive speedups. Image
Pytimetk tutorials:

✅ Sales Analysis: business-science.github.io/pytimetk/tutor…Image
Read 10 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jun 5
A Python Library for Time Series using Hidden Markov Models.

Let me introduce you to hmmlearn. Image
1. Hidden Markov Models

A Hidden Markov Model (HMM) is a statistical model that describes a sequence of observable events where the underlying process generating those events is not directly visible, meaning there are "hidden states" that influence the observed data, but you can only see the results of those states, not the states themselvesImage
2. HMM for Time Series with hmmlearn

hmmlearn implements the Hidden Markov Models (HMMs).

We can use HMM for time series. Example: Using HMM to understand earthquakes.

Tutorial: hmmlearn.readthedocs.io/en/latest/auto…Image
Read 9 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jun 4
🚨NEW AI for Data Scientists Workshop

This is what's coming: Image
Generative AI is the future of Data Science.

Those who can build with LLMs and Python have unlimited career potential. Image
To help, I'm going to lift the curtains on one of my BEST AI projects that companies will pay you $$$ for.

Here's what I'll share in the training: Image
Read 7 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jun 4
❌Move over PowerBI. There's a new AI analyst in town.

💡Introducing ThoughtSpot. Image
1. AI Analyst

ThoughtSpot’s Spotter is an AI analyst that uses generative AI to answer complex business questions in natural language, delivering visualizations and insights instantly.

It supports iterative querying (e.g., “What’s next?”) without predefined dashboards. Image
2. Self-Service Analytics

Unlike Tableau and Power BI, which rely on structured dashboards, ThoughtSpot emphasizes self-service analytics with a search-based interface, making it accessible to non-technical users.

Its AI-driven approach feels like “ChatGPT for data.” Image
Read 8 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jun 3
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

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