#Shiny is now available in Python!

But with 24 web frameworks in python (that I could find), why should you care?

Here's why.

#datascience #rstats #python
Shiny - the ultra-popular R library for turning data science analysis into automated web applications - Was just released for Python.

And there's a big reason to start learning Shiny in Python...
...Because Posit (formerly Rstudio) has a team of engineers building the ecosystem

And they've already succeeded in making the best web app ecosystem in R.

And there's one more reason that 80% of R programmers are overlooking...
I've been getting a lot of questions on ways to learn Python if you're coming from R.

And if that sounds like you, then learning Shiny for python is a slam dunk. 🏀
***I'm putting together a free Shiny for Python training (that anyone can attend live).***

You will discover:
1. What packages are available for building web applications in python

And why I'm excited about shiny to fill a void
2. How to build a Shiny python app

The key differences when coming from R
3. How to make a complex shiny python app

Beyond what they show you in the beginner tutorials
4. Get ideas for your future job portfolio

(by adding Shiny Python Apps to your tool chest)
✅ And all attendees get a FREE Python CHEAT SHEET for attending live.

This is the "express path" to learning python.
What's your next step?

Just attend my free Shiny for Python training.

Register Here: bit.ly/shiny-py

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More from @mdancho84

Jan 7
Python has over 21 web application frameworks.

But this is the one I'm watching.

#python #rstats #datascience
Shiny is the defacto standard web application framework in R that has greatly contributed to my career.
1. Shiny has helped me consult

I was able to differentiate my offering while giving businesses massive value in weeks (not months or years).
Read 16 tweets
Jan 6
Amazon just cut another 18,000 tech jobs.

Stichfix just announced a 20% layoff.

Do this to avoid being a casualty.
The cold reality: In tough times, Businesses make business decisions.

But there’s one thing you can do to help you prosper in a downturn.

Provide business value.
If you save (or make) the company money.

It’s very painful for your business to let you go.

Providing business value = Increasing your value

Make sense?
Read 11 tweets
Jan 5
For the next 3 months, become obsessed with these 4 habits...

And you'll become unrecognizably better as a data scientist.

#datascience
The problem in learning data science is 3 fold:

1. too many resources
2. not sure where to start
3. get stuck and can't move forward

So we need to counteract these with habits that break us through.
Habit 1: Focus on one problem (in a target industry).

Instead of trying to learn skills that can apply to all problems, learn to solve a business problem in a niche area you enjoy.

You can find them easily.
Read 12 tweets
Jan 1
It took me 5 years to call myself a data scientist.

I'll teach in 2 minutes how to avoid mistakes that would have saved me 4.5 years:

#datascience #rstats #python
Mistake 1. Trying to learn everything.

Math, coding, stats, deep learning...

APIs, machine learning, algorithms...

SQL, R, Python, Julia...

C++, Rust, Spark...

Trying to learn all this stuff at once is impossible.
Do this instead:

Find a real project.

Something you can get excited about.

This will give you a roadmap for what to learn.
Read 13 tweets
Dec 31, 2022
Sad news - my friends Seth & Sarah had a house fire to their apartment.

Here’s what happened.
Few have been made aware…

…a little over a week ago, my college roommate Seth and his wife Sarah’s apartment in San Francisco caught fire.

ktvu.com/news/7-displac…
Luckily they weren’t home, and neighbors are safe.

However, it was a very serious fire.

And nearly everything they owned has been destroyed.

If not from fire, it was from smoke, soot, and/or water damage.
Read 5 tweets
Dec 24, 2022
There are more than 10,685,000 people learning to work with data.

Yet most data science jobs now have over 1000+ applicants.

And only 5 actually make it to an interview.

Use these 4 skills to stand out (free to steal & use to advance your career).

#datascience #rstats
1. Time series analysis

Up to 90% of companies need better forecasting.

And less than 5% of data scientists are competent in time series analysis.
So becoming good at time series is a massive advantage.

Demonstrate an application of time series analysis in the interview, and set yourself apart from the crowd.
Read 14 tweets

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