Discover and read the best of Twitter Threads about #R
Most recents (24)
“Why python vs R?”
“What’s inside your new python course?”
“What will it do for me (if I’m an #R user)?”
I’ve been getting a ton of questions so I figured Twitter can help me explain.
#python #rstats #datascience
“What’s inside your new python course?”
“What will it do for me (if I’m an #R user)?”
I’ve been getting a ton of questions so I figured Twitter can help me explain.
#python #rstats #datascience
1. R vs Python.
My guess is 90% of my followers use R.
So why am I promoting python?
Well I just wrote a full article on why R users should learn **some** Python…
Even if python is harder.
business-science.io/code-tools/202…
My guess is 90% of my followers use R.
So why am I promoting python?
Well I just wrote a full article on why R users should learn **some** Python…
Even if python is harder.
business-science.io/code-tools/202…
2. What’s inside my new python course?
I just put together a 5 minute video that shows one powerful use case for python.
And it’s the course project.
I just put together a 5 minute video that shows one powerful use case for python.
And it’s the course project.
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
And this coming Thursday, I'm excited to share what I've learned. 🧵
#datascience #career #python #rstats
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.
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.
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.
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
It requires a transformation...
#datascience #career #rstats
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)
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)
I have a LIVE Masterclass today where I will reveal the exact steps to take.
Strict limit: 500 Seats (and we are over-capacity)
87% of data science projects fail.
One reason is the lack of production.
Here's an easy solution. 🧵
#datascience #python #R #rstats #career
One reason is the lack of production.
Here's an easy solution. 🧵
#datascience #python #R #rstats #career
This is called an #API (Application Programming Interface).
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.
The API runs a program that the data scientist has created on the server.
And the API returns a result.
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
This is especially great for #R people that want to learn #Python.
Let me explain...
#DataScience #Rstats
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
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.
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.
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
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
Data science is different today than it was in 2022.
Why?
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)
The "hype" hiring was that AI would solve all problems...
Grow revenues.
Cut Costs.
Make MONEY (aka profit)
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
...(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
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
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
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
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
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
Spot BS and focus on these 4 steps to grow your career.
#datascience #rstats #career
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…
[1/8] 📚 Introducing #Quarto: A Versatile, New and Exciting Publishing Tool! 🌟
Quarto is a powerful, open-source, and user-friendly publishing framework that streamlines the process of creating beautiful books, documents, and websites. Let’s explore it now!
#RStats #DataScience
Quarto is a powerful, open-source, and user-friendly publishing framework that streamlines the process of creating beautiful books, documents, and websites. Let’s explore it now!
#RStats #DataScience
[2/8] 🤓 Language Agnostic: Quarto works seamlessly with multiple languages, including #Markdown, #LaTeX, #RMarkdown, and #Jupyter notebooks. So, whether you're a researcher or a creative writer, Quarto has you covered! 🌍
#DataScience #RStats
#DataScience #RStats
[3/8] 🔁 Format Flexibility: With Quarto, you can convert your content into various formats, such as PDF, HTML, EPUB, and even slide presentations. It makes sharing your work with diverse audiences a breeze! 🌬️
#RStats #DataScience
#RStats #DataScience
🧵 1/10 🧵
🎯 Demystifying the #Apply Functions Family in #R 🎯
Are you an #Rstats enthusiast? Let's dive into the powerful 'apply' family of functions to help you manipulate and analyze data efficiently! 👩💻👨💻
#DataScience #RStats
🎯 Demystifying the #Apply Functions Family in #R 🎯
Are you an #Rstats enthusiast? Let's dive into the powerful 'apply' family of functions to help you manipulate and analyze data efficiently! 👩💻👨💻
#DataScience #RStats
🧵 2/10
Meet the Family! 🏡
There are six main functions in the 'apply' family:
1️⃣ apply()
2️⃣ lapply()
3️⃣ sapply()
4️⃣ vapply()
5️⃣ mapply()
6️⃣ tapply()
Each has its own use case and is designed to work with different data structures. Let's explore them! 🕵️♂️🔍
#RStats
Meet the Family! 🏡
There are six main functions in the 'apply' family:
1️⃣ apply()
2️⃣ lapply()
3️⃣ sapply()
4️⃣ vapply()
5️⃣ mapply()
6️⃣ tapply()
Each has its own use case and is designed to work with different data structures. Let's explore them! 🕵️♂️🔍
#RStats
🧵 3/10 🧵
1️⃣ apply()
Use apply() for applying a function across the rows or columns of a matrix or array.
Syntax: apply(X, MARGIN, FUN, ...)
X: array or matrix
MARGIN: 1 for rows, 2 for columns
FUN: function to apply
... : additional arguments
#RStats #DataScience
1️⃣ apply()
Use apply() for applying a function across the rows or columns of a matrix or array.
Syntax: apply(X, MARGIN, FUN, ...)
X: array or matrix
MARGIN: 1 for rows, 2 for columns
FUN: function to apply
... : additional arguments
#RStats #DataScience
1/ 🎯 Introduction 📌
The #caret package in #R is a powerful tool for data pre-processing, feature selection, and machine learning model training. In this thread, we'll explore some useful tips & tricks to help you get the most out of caret. #DataScience #MachineLearning #RStats
The #caret package in #R is a powerful tool for data pre-processing, feature selection, and machine learning model training. In this thread, we'll explore some useful tips & tricks to help you get the most out of caret. #DataScience #MachineLearning #RStats
2/ 🧹 Data Pre-processing 📌
caret offers various data pre-processing techniques, like centering, scaling, and removing near-zero-variance predictors. Use the preProcess() function to apply these methods before model training.🧪 #RStats #DataScience
caret offers various data pre-processing techniques, like centering, scaling, and removing near-zero-variance predictors. Use the preProcess() function to apply these methods before model training.🧪 #RStats #DataScience
3/ ⚙️ Feature Selection 📌
Use the rfe() function for recursive feature elimination. This method helps you find the most important features in your dataset, improving model performance & interpretation.🌟 #RStats #DataScience
Use the rfe() function for recursive feature elimination. This method helps you find the most important features in your dataset, improving model performance & interpretation.🌟 #RStats #DataScience
1/🧶📝 Welcome to a Twitter thread discussing the pros & cons of the #R packages, #knitr and #sweave. These packages allow us to create dynamic, reproducible documents that integrate text, code, and results. Let's dive into the strengths and weaknesses of each. #Rstats
BIG NEWS: #ChatGPT breaks #Python vs #R Barriers in Data Science!
Data science teams everywhere rejoice.
A mind-blowing thread (with a FULL chatgpt prompt walkthrough). 🧵
#datascience #rstats
Data science teams everywhere rejoice.
A mind-blowing thread (with a FULL chatgpt prompt walkthrough). 🧵
#datascience #rstats
It's NOT R VS Python ANYMORE!
This is 1 example of how ChatGPT can speed up data science & GET R & PYTHON people working together.
(it blew my mind)
This is 1 example of how ChatGPT can speed up data science & GET R & PYTHON people working together.
(it blew my mind)
1/10: 🧵 Welcome to this thread on #regression modeling strategies in #R! We'll discuss key techniques and packages to help you build effective models. Ready to dive in? Let's go! 🚀 #RStats #DataScience #Statistics
2/10: 🌐 Linear Regression: Start with simple & multiple linear regression using 'lm()' function. Check out the 'broom' package for easy-to-use regression output! #RStats cran.r-project.org/web/packages/b…
3/10:🏞️ Polynomial Regression: When data is nonlinear, try polynomial regression! Use 'poly()' to create higher-order terms. Beware of overfitting! #RStats
What is one of the most exciting areas of chatgpt use in data science?
I’m most excited about web apps.
Here’s why. 🧵
#datascience #ChatGPT #rstats
I’m most excited about web apps.
Here’s why. 🧵
#datascience #ChatGPT #rstats
It’s no secret.
Most data scientists don’t know HTML, JavaScript or CSS.
This is why I’m a huge fanboy for #R #Shiny
Most data scientists don’t know HTML, JavaScript or CSS.
This is why I’m a huge fanboy for #R #Shiny
When Shiny came out 11 years ago, it was a game changer.
Data scientists could now put their algorithms inside of an app.
And Business People could use it!
Data scientists could now put their algorithms inside of an app.
And Business People could use it!
GAME CHANGER: This #r #shiny app was built using #chatgpt.
But the process is riddled with landmines that 90% of data scientists will screw up.
I want to help. Here's how... 🧵
#rstats #datascience
But the process is riddled with landmines that 90% of data scientists will screw up.
I want to help. Here's how... 🧵
#rstats #datascience
I'm not going to sugarcoat it...
Chatgpt got me 80% there.
Chatgpt got me 80% there.
The other 20% was a bit of a challenge...
There were mistakes you will make if you try to run raw chatgpt code.
And the best way to learn is to see someone do it live.
There were mistakes you will make if you try to run raw chatgpt code.
And the best way to learn is to see someone do it live.
People are shocked when they learn that I picked #R over #Python.
It’s a true story. 🧵
#datascience #rstats
It’s a true story. 🧵
#datascience #rstats
I began learning data science with Python.
This decision actually cost me 3 months and caused me to quit data science for a little bit.
Why?
This decision actually cost me 3 months and caused me to quit data science for a little bit.
Why?
Python was too much like coding. And I came from a business background.
My main tool was Excel.
Python was unnatural.
My main tool was Excel.
Python was unnatural.
This is a game-changer:
ChatGPT just made this #r #shiny app for me in under 15 minutes... 🧵
#rstats #datascience
ChatGPT just made this #r #shiny app for me in under 15 minutes... 🧵
#rstats #datascience
I asked chatgpt a simple question...
And here's what ChatGPT did:
1. It created the basic code for an XGBoost Machine Learning model for scoring the customer spend
(Note: I had to ask it to replace random forest with xgboost and caret with #tidymodels)
1. It created the basic code for an XGBoost Machine Learning model for scoring the customer spend
(Note: I had to ask it to replace random forest with xgboost and caret with #tidymodels)
Did you know that 90% of companies are NOT using customer network detection, and it could make you insanely valuable? 🧵
#rstats
#rstats
With #R, you can:
1. Identify the most influential customers in their network,
2. Craft messages to those leaders, and
3. Send those messages to their closest connections.
1. Identify the most influential customers in their network,
2. Craft messages to those leaders, and
3. Send those messages to their closest connections.
Imagine the impact this could have on your sales, customer satisfaction, and career advancement.
You could be the one to make your company millions of dollars and put yourself in a leading position for your next role.
You could be the one to make your company millions of dollars and put yourself in a leading position for your next role.
BREAKING NEWS: #ChatGPT Breaks #Python VS #R Barriers For #DataScience Teams Everywhere...
And Data Scientists everywhere are in shock.
Here's the LIVE story as it's unfolding.🧵
#rstats #pydata
And Data Scientists everywhere are in shock.
Here's the LIVE story as it's unfolding.🧵
#rstats #pydata
It's NOT #R VS #Python ANYMORE!
Let me explain with an example that was MIND BLOWING to me.
Here's the background:
Let me explain with an example that was MIND BLOWING to me.
Here's the background:
I'm an R guy.
I use R for research.
It's quick to make analysis.
And now I'm super good with it.
I use R for research.
It's quick to make analysis.
And now I'm super good with it.
Thanks to Hiroaki Yutani for putting this amazing example together.
This small example demonstrates how you can learn R and automatically use SQL.
This small example demonstrates how you can learn R and automatically use SQL.
Under the hood, the dbplyr library converts R’s tidyverse syntax to SQL
So we don’t need to memorize the SQL translation.
So we don’t need to memorize the SQL translation.
Remote sensing (RS) software tools can help you analyze and interpret satellite or airborne data. If you're on a tight budget, don't worry - there are plenty of great free options out there!#gischat #geospatial
#QGIS is a powerful and user-friendly open-source #GIS software that includes RS tools. You can use it to visualize, analyze, and process your RS data. It supports a wide range of file formats and has an active community.#gischat #geospatial
qgis.org/en/site/foruse…
qgis.org/en/site/foruse…
Sentinel Application Platform is a free, multi-platform tool for processing and analyzing data from various RS sensors, including Sentinel-1, Sentinel-2, and Sentinel-3. It includes a user-friendly GUI and a powerful toolbox. @esa #gischat
step.esa.int/main/download/…
step.esa.int/main/download/…
99% of data scientists CANNOT do #timeseries and #automation together.
I know this because almost all of my advanced students ask for help in these 2 areas.
So I made a FREE training.
Here's why + how to access it for free... 🧵
#datascience #rstats
I know this because almost all of my advanced students ask for help in these 2 areas.
So I made a FREE training.
Here's why + how to access it for free... 🧵
#datascience #rstats
What if you could use any #Python or #R library to do it?
Which would you pick?
Here's what I'd do and why.
Which would you pick?
Here's what I'd do and why.
1. Modeltime in R for Time Series
It's not a question. It's a fact.
Modeltime is the best forecasting library in R or Python ecosystems.
Sure the bar is high:
- forecast
- fable / tsibble
- statsmodels
- sktime
- pmdarima
- darts
- +20 more
Modeltime is the best.
It's not a question. It's a fact.
Modeltime is the best forecasting library in R or Python ecosystems.
Sure the bar is high:
- forecast
- fable / tsibble
- statsmodels
- sktime
- pmdarima
- darts
- +20 more
Modeltime is the best.
Want to learn prefect for data science automation? I'm teaching it as part of my Time Series Workshop tomorrow.
Here's what's coming...
#datascience #python #rstats #etl
Here's what's coming...
#datascience #python #rstats #etl
Automating Time Series Forecasting is a BIG challenge for companies.
To make matters worse, 80% of data scientists struggle with automation + time series.
Let's fix that.
To make matters worse, 80% of data scientists struggle with automation + time series.
Let's fix that.
