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
3. Tidy Finance with #R

What tools exist in R for #Finance?
And how do I use them?

Answers to these questions are covered in this book!

P.S.- This book uses my R package, #tidyquant

Website: tidy-finance.org Image
4. Text Mining with R

This is a fantastic introduction to text analysis and text mining with the #tidytext R package.

This book singlehandedly made me MORE CONFIDENT with text analysis.

Website: tidytextmining.com Image
5. #Forecasting Principles and Practice

This is the best “theory” book on #timeseries analysis and forecasting.

Topics Covered:
- ARIMA,
- Exponential Smoothing,
- TimeSeries Decomposition
- A lot more!

Website: otexts.com/fpp3/ Image
1-Dollar Bonus Book:

This is a massive value- Gives you a complete plan for EVERYTHING you need to know about learning data science.

It's only a buck.

And it will cut 2-3 years off your journey.

Website: learn.business-science.io/if-i-had-to-le… Image
Want even more help becoming a 6-figure data scientist?

I have a free workshop that will help you become a $100K+ earner as a #DataScientist even in a Recession.

👉Register Here: us02web.zoom.us/webinar/regist… Image

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More from @mdancho84

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
May 9
If you're starting out in data science (or if your wondering what you need to learn), don't believe everything you read. 🧵

Spot BS and focus on these 4 steps to grow your career.

#datascience #rstats #career Image
My friend Rafael Nicolas Fermin Cota (Nico) pointed me to this modified graphic from a Harvard Business Review Article on "Prioritizing Which Data Science Skills Your Company Needs".
With ChatGPT, AI, and the "trendiness" of buzzwords, this graphic becomes even more dangerous. hbr.org/2018/10/priori…
Read 13 tweets
May 8
Here’s a list of 7 problem-solving skills that will help you become a 6-figure data scientist (with resources).

#datascience #rstats #career Image
Whether you are trying to transition into data science, building your skills, or seeking to land your first data science position...

It can be a big challenge to figure out what businesses want.

And that's why I put together this list of curated resources.
7 Skills To Grow Your Data Science Career

1. Costing A Problem:

Quantify the effect of no-change in a business process to show opportunity for improvement.

Use dollar value to demonstrate opportunity.

Resource: investopedia.com/terms/c/cost-b…
Read 12 tweets
May 8
There's a reason that companies are "restructuring" #datascience teams.

And, it's actually an opportunity to make 17% more.

Let me explain.

#datascience #rstats #career Image
Companies are getting frustrated.

In 2020-22 they hired 10+ person data science teams with the **hope** that they would bring:

1. Increased productivity
2. predictive analytics,
3. improved customer experience
4. And more revenue + profit
But instead what they got was:

1. 15 Months for project schedules
2. 87% of projects fail
3. $1,000,000+ cost for a data science team
Read 8 tweets
May 1
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
Read 7 tweets
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

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