At Springboard, we pair mentors with learners in data science. We often get questions about whether to use Python or R – and we’ve come to a conclusion thanks to insight from our community of mentors ...

R vs Python: What are the main differences? Your email has been sent More people will find their way to Python for data science workloads, but there’s a case to for making R and Python complementary, ...

But data science is a specific field, so while Python is emerging as the most popular language in the world, R still has its place and has advantages for those doing data analysis. Hoping to settle ...

Reticulate is a handy way to combine Python and R code. From the reticulate help page suggests that reticulate allows for: "Calling Python from R in a variety of ways including R Markdown, sourcing ...

Here’s how the general-purpose favorite of scientists stacks up against the stat head’s data-honed tool of choice The boss’s boss looks out across the server farm and sees data—petabytes and petabytes ...

Why write SQL queries when you can get an LLM to write the code for you? Query NFL data using querychat, a new chatbot component that works with the Shiny web framework and is compatible with R and ...

When it comes to choosing a programming language, there really are only two choices if you’re working with data. For data science, machine learning, statistics, IoT technology and even automation, the ...

Java can handle large workloads, and even if it hits limitations, peripheral JVM languages such as Scala and Kotlin can pick up the slack. But in the world of data science, Java isn't always the go-to ...

As I frequently travel in data science circles, I’m hearing more and more about a new kind of tech war: Python vs. R. I’ve lived through many tech wars in the past, e.g. Windows vs. Linux, iPhone vs.