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

Explaining Confidence Intervals

Mala Mahadevan explains what confidence intervals are: Suppose I look at a sampling of 100 americans who are asked if they approve of the job the supreme court is doing. Let us say for simplicity’s sake that the only two answers possible are yes or no. Out of 100, say 40% say yes. As an […]

Read More

Long Live The DBA

Kellyn Pot’vin-Gorman notes that the “Gone will be the DBA” trend has hit Oracle as well: Any DBA who specializes in optimization knows that hardware offers around 15% overall opportunity for improvement.  My favorite quote from Cary Millsap, “You can’t hardware your way out of a software problem” is quite fitting, too.  A hardware upgrade […]

Read More

Categories