This week, here’s another map: a map of world oil production that you can create using R.
As I’ve mentioned, projects like this – a few hundred lines of code, with a variety of functions from the tidyverse – are excellent practice for intermediate data scientists.
In this post, I want to walk you through the logic of building a map, step by step …
How many data science books have you read? 5? 10? A few dozen? How many free online courses have you taken? A few? How many blog posts have you read? (I’d be willing to bet: you’ve read dozens.) If you’re like most budding data scientists, you’ve probably consumed a lot of material. You probably even … Read more
In my last article, I stated that for practitioners (as opposed to theorists), the real prerequisite for machine learning is data analysis, not math. One of the main reasons for making this statement, is that data scientists spend an inordinate amount of time on data analysis. The traditional statement is that data scientists “spend 80% … Read more
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