I've sought the help of the great Stack Overflow gods to help me many a time during my journey into data science. During the hunt for exactly the right syntax, I also enjoyed reading dozens of incredibly helpful and insightful blogs on data science, machine learning, natural language processing, and (now) deep learning. Janessa Lantz on stitchdata.com has the "Ultimate Guide to Data Science Blogs (and counting)".
Skimming through this list reminds me of many long days at the office figuring out how to do stuff, and whether the stuff I've done was actually right. The blogs I've frequented and had a few drinks with have been:
I'd love to get on that list by providing some of my own experiences, tricks, and insights from business. I've had the great opportunity to actually write scripts for real business problems to do things like identify cost savings for the government, classify military documents more easily, create risk scores for situational awareness, predict workplace behaviors, measure customer sentiment, and improve the government's understanding of what customers are saying. So, I think like many of the important blogs out there, I'm here to not only share my code and thoughts, but also experiences communicating these concepts to business leaders.
DataRobot CEO Jeremy Achin, mentions at a talk at Data Driven NYC https://www.youtube.com/watch?v=2iaOSny5EeU&t=12s, where I screenshot'd minute 15:16, where he talks about how the ability to tackle real work problems has a high bar in terms of ability (and probably just opportunities, as well). This really spoke to me as well, as did this article: https://www.insidehighered.com/news/2017/03/30/report-urges-data-science-course-work-all-undergraduates-close-growing-skills-gap about the "Data Science Disconnect", essentially how higher education is struggling to keep up. Maybe one way to counter this is ambitious revolutionary automated ML technology like DataRobot and Google Cloud, but there's also the need for people who can just plain, easily, talk about it to people who don't understand it but need to make decisions from it.