Finance + data analysis + visualization + Shiny...

...in Python?

Here's what I'm up to... 🧵

#datascience #python #rstats #finance
Python is NOT in shortage of web frameworks.

In fact, I've tallied up 24 web application frameworks through my research.

But there's one that 99.9% of data scientists are overlooking.
Shiny for python was recently released in "beta".

But truth be told... It looks way better than beta.

It's ready for prime time in one of my data science workshops.
My Personal Goal:

Test out the waters with the BRAND NEW Shiny in Python package in my upcoming workshop.

One problem...
I want to go beyond the "beginner" tutorials.

(they aren't applied to any sort of business problem).

So I picked a financial application.
I'm going to make a small stock analyzer...

An app that uses:

- yfinance to pull in #stock data

- interactive visualizations with moving averages

- important financial metrics to help make investment decisions
Want to see what I do (and how I do it)?

I'm hosting a free Shiny in Python workshop where I'll unveil the app.

And it will be completely written in python.
This training is great for beginners and experts alike...

And it's perfect for those that want to learn data science for business (my love ❤ ).
What's the next step?

Register for my free Shiny in Python Learning Lab.

👉Register here: bit.ly/shiny-py

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More from @mdancho84

Jan 11
Marketing analytics is a $250,000,000,000 opportunity for companies.

But few companies have the data science talent to make it happen.

Let's fix that. 🧵

#datascience #rstats #python
McKinsey just released their state of AI report for 2022 (and beyond).

One of my key takeaways is how big of an impact AI is having on marketing and sales...

mckinsey.com/capabilities/q…
...But the sad fact is that the majority of companies STILL aren't using data science to improve.

Why?

They don't know how.
Read 11 tweets
Jan 8
#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...
Read 11 tweets
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

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