Useful tips for writing about your data science projects
Writing is something that everyone wants to do more of, yet we often find it difficult to get started. We know that writing about data science projects improves our communication abilities, opens doors, and makes us better data scientists, but we often struggle with thoughts that our writing isn’t good enough or that we don’t have the necessary background or education.
I’ve struggled with these feelings myself, and, over the past year, have developed a mindset to get through these barriers as well as general principles about data science writing. While there is no one secret to writing, there are practical tips that make it easier to establish a productive writing habit:
- Aim for 90%: the imperfect project that gets finished is better than the perfect project you never complete
- Consistency helps: the more you write, the easier it gets
- Don’t worry about credentials: in data science there are no barriers to prevent you from contributing or learning anything you want
- The best tool is the one that gets the job done: don’t over-optimize your writing software, blogging platform, or development environment
- Read widely and deeply: borrow, remix, and improve on other’s ideas
In this article, we’ll go through each point these briefly, and I’ll touch on ways in which I’ve implemented them to improve my writing. Over the course of dozens of articles, I’ve made lots of mistakes, and, rather than making these same errors yourself, you can learn from my experiences.