Crafting compelling narratives often depends on presenting insights clearly and effectively. This skill is key for data science. For users of R’s {gt} package, it has provided a powerful and flexible way to create publication-quality tables in Quarto reports or Shiny apps such as clinical trial reports, research documents, ...

Machine learning with uncertainty quantification and interpretatbility

What: This article explores tips to use undetected ChromeDriver along with its benefits. Why: To guide users on how they can use undetected ChromeDriver without getting detected by a website bot. Chromedriver is a tool that enables communication between your computer and the Google Chrome web browser. It allows other ...

Working with Python code becomes significantly more manageable when objects are assigned clear, sensible names, and Excel is no different. This blog post covers how to create named ranges and tables in Excel using openpyxl. To begin this demonstration, we will import several modules from openpyxl, along with a sample ...

Several packages facilitate working with Python and Excel, but openpyxl stands out as an excellent choice due to its comprehensive features for reading and writing workbooks. In this blog post, we will explore how to get started with creating Excel workbooks from scratch and modifying existing ones using openpyxl. For ...

In a world where compute time is billed by the second, make every one of them count. There are zero valid reasons to utilize a quarter of your CPU and memory, but achieving complete resource utilization isn’t always a straightforward task. That is if you don’t know about ...

One of the key insights Excel users should appreciate about Copilot is its vital role in keeping Excel current in the AI era. It achieves this by innovatively integrating multiple intelligence sources, expanding Excel’s functions beyond traditional spreadsheets. Today’s era is characterized by growing system interoperability. In particular, ...

Parallelizing R dataframe computation is a guaranteed way to shave minutes or even hours from your data processing pipeline compute time. Sure, it adds more complexity to the code, but it can drastically reduce your computing bills, especially if you’re doing everything in the cloud. R doParallel package provides ...