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.
But, they also take the most time to build these skills.

And there are a lot of mistakes that even I made when picking these up.

Time Frame: 4 months
If you want help, I have a free live masterclass on Wednesday, May 17th, where I will lay out a full plan to build each of these skills...

...And I share everything you need to be able to land a 6-figure data science career (even in a recession).
I'll be covering the 3 most overlooked skills...

...That will get you a 6-Figure data science job in any economy.
What's the next step?

Just register and attend my training on Wednesday.

P.S. Seats are limited (500 Seats)

Register Here: learn.business-science.io/registration-2… Image

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More from @mdancho84

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
May 12
I continue to be impressed by the ease of doing machine learning with Pycaret.

This is especially great for #R people that want to learn #Python.

Let me explain...

#DataScience #Rstats Image
As many of you know, my primary data science toolkit is #R. ❤️

I've been doing data science in production at @bizScienc and developing open-source R software for over 10 years.

- Modeltime (+4 ecosystem pkgs)
- Timetk
- Tidyquant
- CorrelationFunnel
But, I interact with team members and interface with clients...

and their language of choice is often Python.

It's refreshing that I can quickly switch to Python when needed, and not need to write 5000 lines of #ScikitLearn code to do basic machine learning.
Read 5 tweets
May 12
#AI isn't going to take your data science job.

But the #recession might.

Here's how this trend is unfolding from Big #Tech to the broader job market (and how to prepare)...

#datascience #career Image
For the better part of a decade, Big #Tech was on a hiring spree.

Companies like #Amazon, #Microsoft, and #Meta overhired. And that's OK when things are going well.

But then 2022 hit and Big Tech had its worst decline since 2008.
And in January 2023, with CEOs seeking to protect their stock prices, companies like:

Amazon,
PayPal, and
Microsoft

...all announced deep cuts to employees.
Read 11 tweets
May 11
Data science is evolving.

And the cold, hard fact is: Your Career Is On The Line.

If your projects aren't making it into production, your job has a bullseye on it 🎯

Time to smarten up. Here's how.

#datascience #career #python Image
Data science is different today than it was in 2022.

Why?
In 2022, companies were OK with hiring 15+ person data science teams...

The "hype" hiring was that AI would solve all problems...

Grow revenues.
Cut Costs.
Make MONEY (aka profit)
Read 16 tweets
May 10
Learning data science on your own is tough...

...(ahem, it took me 6 years)

So here's some help.

5 Free Books to Cut Your Time In HALF.

Let's go! 🧵

#datascience #rstats #R Image
1. Mastering #Spark with #R

This book solves an important problem- what happens when your data gets too big?

For example, analyzing 100,000,000 time series.

You can do it in R with the tools covered in this book.

Website: therinspark.com Image
2. Geocomputation with #R

Interested in #Geospatial Analysis?

This book is my go-to resource for all things geospatial.

This book covers:
-Making Maps
-Working with Spatial Data
-Applications (Transportation, Geomarketing)

Website: r.geocompx.org Image
Read 8 tweets
May 9
A big mistake I see data scientists making: Learning tools they never use.

I did this.

And here’s how to fix it.

#datascience #rstats Image
A classic case of learning everything is mean, median, and mode.

Data scientists learn how to use all 3 of these.

Median and mean I use almost every day.

But I can count on one hand how often I have used mode.
The same applies to machine learning, data wrangling, and visualization.

There are 1000's of things you could learn.

But what should you spend your time learning?
Read 7 tweets

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