There are 43,515 github repositories that use ChatGPT.

Here are the 5 best. 🧵

#datascience #chatgpt #productivity #datascientist Image
1. Awesome ChatGPT Prompts (71.2K Stars)

This is a collection of prompt examples to be used with the ChatGPT model.

github.com/f/awesome-chat…
2. ChatGPT Desktop Application (34.2K Stars)

ChatGPT Desktop Application (Mac, Windows and Linux) with multiple features such as text-to-speech, multi-platform, export chat history and more.

github.com/lencx/ChatGPT
3. Open AI Cookbook (32.4K Stars)

Shares example code for accomplishing common tasks with the OpenAI API.

github.com/openai/openai-…
4. Open Assistant (31.2K Stars)

A chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.

github.com/LAION-AI/Open-…
5. Prompt Engineering Guide (27.3K Stars)

Guides, papers, lecture, notebooks and resources for prompt engineering

github.com/dair-ai/Prompt…
BONUS: Wednesday, I'm doing a free workshop on how to use ChatGPT for Data Scientists.

Sign up here - us02web.zoom.us/webinar/regist… Image

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More from @mdancho84

May 1
After 12 weeks of using #chatgpt for #datascience, I've learned one thing:

Prompts are ABSOLUTELY critical.

This is why. 🧵

#career #dataanalysis Image
4 weeks ago I made a full chatgpt for data scientists workshop where I showed...

...the MASSIVE amount of debugging I had to do to get ChatGPT to make a data science analysis "work".
Sure, chatgpt still saved me time.

But I was frustrated.

And, debugging was killing me. 😡
Read 10 tweets
Apr 20
BIG NEWS: #ChatGPT breaks #Python vs #R Barriers in Data Science!

Data science teams everywhere rejoice.

A mind-blowing thread (with a FULL chatgpt prompt walkthrough). 🧵

#datascience #rstats
It's NOT R VS Python ANYMORE!

This is 1 example of how ChatGPT can speed up data science & GET R & PYTHON people working together.

(it blew my mind)
This example combines #R, #Python, and #Docker.

I created this example in under 10 minutes from start to finish.
Read 25 tweets
Apr 18
If you're struggling to become a data scientist...

There's a 95% chance you are doing it wrong.

Master these 5 things to escape career boredom (and make 6-figures).🧵

#datascience #career Image
Would you believe me if I told you it took me 5 years to become a data scientist?

I've written 10+ R packages that have amassed 2.5 million downloads.

I've consulted for S&P Global, MRM McCann, and 3 more Fortune 500 companies.
But I made 5 critical mistakes that I want to teach you how to fix.

1. I was too focused on #DataScience

2. I couldn't relate it to the business

3. I struggled to communicate the value
Read 10 tweets
Apr 14
Attention Data Scientists:

ChatGPT made this web app for me (and I want to show you exactly how).

But read this first. 🧵

#datascience #chatgpt #rstats #python Image
ChatGPT is a game changer.

I've 10Xed my productivity on certain data science tasks.

But 99% of data scientists will struggle using ChatGPT (I did, and you will too).
I made 9 critical mistakes when I made my first web application.

And this caused me HOURS of REWORK.

And it killed my 1st impression of Chatgpt.

But, I have good news...
Read 9 tweets
Apr 14
Every data scientist I know lacks some skills (me included).

But, a change in your focus can take you from no job to $145K+.

Let me explain.

#datascience #career Image
The learning path for a data scientist is full of traps.

It goes something like this:
- Python (or R)
- Algorithms
- Math
- Stats
- Make a Portfolio
- Get some projects under your belt
- More math
- Neural networks
- Deep learning part 2
- More projects (now with cats & dogs)
...And it's a mess.

No wonder why you are struggling.

And it turns out that in this economy, it's even become a lot harder with the "traditional" approach.

Why?
Read 9 tweets
Apr 12
The time of bloated data science teams has come to an end.

And, there is a massive shift that's going on right now... 🧵

#datascience #career Image
Bulky data science teams are getting "restructured" as businesses demand more value from their investments.

They don't want 10+ number crunchers.

Especially if it takes 15 months to **maybe** get a project into production.
What they want is business value-

Results that drive revenue & growth.

And starting the year $1M+ in the hole with a bulky data science team isn't the answer.

In their place, a new breed of data scientist is forming.
Read 9 tweets

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