I've written a lot about data analysis with Python recently. I wanted to explain why it's been a language of choice. Here are some of the reasons I find Python so easy to use, yet powerful. Python ...

Already using NumPy, Pandas, and Scikit-learn? Here are seven more powerful data wrangling tools that deserve a place in your ...

These simple operations and others are why NumPy is a building block for statistical analysis with Python. NumPy also makes ...

Overview PyCharm, DataSpell, and VS Code offer strong features for large projects.JupyterLab and Google Colab simplify data ...

What if the tools you already use could do more than you ever imagined? Picture this: you’re working on a massive dataset in Excel, trying to make sense of endless rows and columns. It’s slow, ...

What if you could turn Excel into a powerhouse for advanced data analysis and automation in just a few clicks? Imagine effortlessly cleaning messy datasets, running complex calculations, or generating ...

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 ...

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 ...