Over the last few blog posts, I’ve discussed some of the basics of what machine learning is and why it’s important: – Why machine learning will reshape software engineering – What is the core task of machine learning – How to get started in machine learning in R Throughout those posts, I’ve been using the … Read more
If you’ve been using R for a while, and you’ve been working with basic data visualization and data exploration techniques, the next logical step is to start learning some machine learning. To help you begin learning about machine learning in R, I’m going to introduce you to an R package: the caret package. We’ll build … Read more
One of the biggest issues that comes up when I talk to people who want to get started learning data science is the following: I don’t know where to get started! Recently, I argued that R is the best programming language to learn when you’re getting started with data science. While this helps you select … Read more
An important principle in analyzing data is “overview first, zoom and filter, then details on demand” (quote: Ben Shneiderman) In practice, this typically means starting at a high level with a single chart, and then “zooming into” the data by replicating that chart for specific subsets of the dataset. And, even more valuable is being … Read more
Last week, I was talking to a guy who’s learning analytics, coaching him on what skills to learn next and helping him plan a career path. He’s a smart guy with an analytical background and minor coding experience, but he’s new to R.
Towards the end of the conversation, I asked him, “what’s the biggest challenge you have right now, learning analytics.”
His response? “The code is intimidating.”