• If you are not already a member of DSC, set up an account on write for us data science Central or one of its related accounts.
• Wait for approval (you should receive an email letting you know when we add you to the membership roster).
• Once you are a member, log in, then select Members/Add Blog Post from the menu at top.
• All content is moderated. This means that if the editor decides not to publish your article, they will not publish your article. If they do, then it will be published, typically within 2-3 days. If you have any questions, please contact the editors.
• Data Science Central does not pay for content. On the other hand, the platform has more than half a million subscribers, so it is a fantastic place to post for exposure, and we do our best to promote content that we feel is worthwhile.

While the mechanics are important, it’s also worth spending some time trying to understand what DSC is looking for in content:

First up: Topics. When DSC was a brand new site, way back in 2012, the term Data Science itself was very novel, and it usually meant people who were able to use a new breed of programming tools (most specifically R write for us data science, but later Python), to do analytics work, in the wake of the Big Data and Hadoop revolution that was going on at the time. Data Science Central was a cool, pithy name for the site, and as interest in the field grew, so did DSC.

Coming up on a decade later, things have changed. Being a data scientist has overtaken programming as the wish list career topper that all aspiring nerds want to be when they grow up. Machine learning algorithms and convolutional neural networks are increasingly replacing traditional programming for a variety of activities, and data is becoming strategic within organizations rather than simply tactical.

To that end, what we at DSC are looking for are stories about data. This can include data analysis tools and modeling, neural networks and data storage and access strategies, modeling, and knowledge representation. It also includes the strategic uses of data, governance, provenance, quality and protection, visualization and creative data storytelling. We’re also expanding into those areas of artificial intelligence that are critical to cognitive computing, knowledge graphs, mathematics, and science. Why? Because data science is as much about science as it is about algorithms.

Finally, DSC will focus more on the implications of data transformations on businesses, government, manufacturing, society and the individuals within it.