Data Science Resources

Steph Locke has some resources if you are interested in getting started with data science:

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data is written by Hadley Wickham and Garett Grolemund. You can buy it and you can also access it online.

If you’re interested in learning to actually start doing data science as a practitioner, this book is a very accessible introduction to programming.

Starting gently, this book doesn’t teach you much about the use of R from a general programming perspective. It takes a very task oriented approach and teaches you R as you go along.

This book doesn’t cover the breadth and depth of data science in R, but it gives you a strong foundation in the coding skills you need and gives you a sense of the of the process you’ll go through.

It’s a good starting set of links.

Related Posts

The Basics Of PCA In R

Prashant Shekhar gives us an overview of Principal Component Analysis using R: PCA changes the axis towards the direction of maximum variance and then takes projection on this new axis. The direction of maximum variance is represented by Principal Components (PC1). There are multiple principal components depending on the number of dimensions (features) in the […]

Read More

Investigating The gcForest Algorithm

William Vorhies describes a new algorithm with strong potential: gcForest (multi-Grained Cascade Forest) is a decision tree ensemble approach in which the cascade structure of deep nets is retained but where the opaque edges and node neurons are replaced by groups of random forests paired with completely-random tree forests.  In this case, typically two of […]

Read More