The Human Dimension to Clean, Distributable, and Documented Data Science Code

Alternatively titled: How to Make Your Code Work for People

Hello fellow datanistas!

Ever wondered why some data science projects thrive while others barely make an impact? It's not just about the code—it's about making it accessible, understandable, and usable. In my latest blog post, I dive deep into the human aspects that can dramatically enhance the effectiveness of your data science work.

Since 2016, I've been on a mission to help data scientists apply basic software development practices to enhance their work's impact. This blog post is a culmination of years of experience and learning, presented at the pyOpenSci Fall Festival as a keynote. I've transformed my talk into a detailed, accessible written format to leave you with a resource that goes beyond a typical presentation.

We'll explore key concepts like readability, user-friendly installation, and the critical role of documentation. These elements are often overlooked but are fundamental to the success and impact of your projects. By focusing on these, we can make our tools and analyses not just computationally efficient but also a joy to use and build upon.

I also share personal anecdotes and lessons learned from my own experiences—both successes and missteps. These stories illustrate the real-world implications of our coding and documentation choices and how they can either facilitate or hinder collaboration and adoption.

By embracing these practices, we not only enhance our individual projects but also contribute to the growth and accessibility of the entire field of data science. Let's commit to making our code not just powerful, but also approachable and impactful.

If you find this topic as crucial as I do, please forward this post to others who might benefit from it. Let's spread the word and elevate the practice of data science together!

Cheers,
Eric