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Matt Dancho (Business Science) Profile picture
@mdancho84
May 16 • 6 tweets • 4 min read Twitter logo Read on Twitter
87% of data science projects fail.

One reason is the lack of production.

Here's an easy solution. 🧵

#datascience #python #R #rstats #career Image
This is called an #API (Application Programming Interface). Image
An API allows the user to make requests using a tool they are comfortable with.

The API runs a program that the data scientist has created on the server.

And the API returns a result.
Why an API?

1. Works with #Excel, #Tableau, #PowerBI (the same tools your company already uses)

2. It's fast to make with tools like FastAPI in #Python (and Plumber in #R).
Want to learn more about Python for #APIs, #MachineLearning, and #Production?

I have a new course coming soon: Python for Machine Learning and APIs.

It drops in 10 days.
You can register your interest by joining my course waitlist + special launch event

(which gets you access to exclusive perks and goodies)

Join 3,500+ on my course waitlist: learn.business-science.io/python-ml-apis…

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More from @mdancho84

Matt Dancho (Business Science) Profile picture
May 18
Over the past 3 years, I've been writing Python code daily.

And this coming Thursday, I'm excited to share what I've learned. 🧵

#datascience #career #python #rstats Image
Most of you know me as an #R guy. And I 1000% love R. ❤️

But I've also grown over the past 10+ years that I've been practicing data science as a data scientist, as a consultant, and as an educator.
What I've come to realize is that no one language is perfect.

Each has its strengths. And weaknesses.

And more often than not we get caught up in debating things that **don't** really matter...

Like R vs Python.
Read 11 tweets
Matt Dancho (Business Science) Profile picture
May 18
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.

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

#datascience #rstats Image
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.
Key Point:

Becoming good at time series is a massive advantage that automatically places you in the 95th percentile of applicants.

Try This:

Demonstrate an application of time series analysis in the interview, and set yourself apart from the crowd.
Read 13 tweets
Matt Dancho (Business Science) Profile picture
May 17
Becoming a 6-figure data scientist (with #R) in today's economy is different than it was in 2022.

It requires a transformation...

#datascience #career #rstats Image
And those that successfully make the transformation:

1. Get hired 75% faster (3-6 months vs 12+ months)

2. Earn 17% more ($145K vs $117K)

3. Get promoted 2X faster (1-2 years vs 2-4 years)
Want to learn how?

I have a LIVE Masterclass today where I will reveal the exact steps to take.

Strict limit: 500 Seats (and we are over-capacity)
Read 4 tweets
Matt Dancho (Business Science) Profile picture
May 16
There are 100,000 data scientists on the market. How can you possibly get a job?

This is one way… 🧵

#datascience #rstats Image
Imagine this.

You walk into the Central Bank of Canada, the backbone of Canada’s financial system.

You’re applying for a data science position.
On paper the odds are stacked against you.

You use R.

The whole team uses Python.

And you are a complete unknown.
Read 10 tweets
Matt Dancho (Business Science) Profile picture
May 14
What stresses me out isn’t the same thing that stressed my parents out.

True story. 🧵

#datascience #rstats Image
It was the day before R/Finance 2018.

I had a presentation for R/Finance where I was going to present a Shiny App that I was working on.

I updated my R software…

Big mistake.
My shiny app suddenly stopped working and I went into straight panic-mode. 😱

This was before I knew how to use GitHub & Docker like I do now.

So I was freaking out.
Read 12 tweets
Matt Dancho (Business Science) Profile picture
May 13
Becoming a data scientist in this economy isn't easy.

But it can be done.

Here are a few tips.

#datascience #career Image
First, build the foundational skills.

These include:

- Data Cleaning
- Data Wrangling
- Visualization
- EDA (Exploratory Data Analysis)
- Machine Learning
- Clustering
- Reporting
- Programming

Time Frame: 6 Weeks
Next, you need 3 of the most overlooked skills:

1. Problem-Solving Skills
2. Time Series
3. Production

These are the skills that will generate the most ROI for your company.

And it's what they will hire you in a recession for.
Read 7 tweets

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