Two new courses!

Discover Tree-Based models in R and Spatial Analysis with sf and raster! Learn to build high performance models and work with satellite imagery data! [Happy New Year! Save $151+ on DataCamp]( Commit to learning data science in 2018. Offer ends 1/9. [DataCamp]( New Courses! [ ]( [Play Now]( [ ]( Machine Learning with Tree-Based Models in R Taught by Gabriela de Queiroz, founder of R-Ladies and Erin LeDell, Machine Learning Scientist at H2O.ai In this course you'll learn how to work with tree-based models in R. This course covers everything from using a single tree for regression or classification to more advanced ensemble methods. These powerful techinques will allow you to create high performance regression and classification models for your data. [Start Learning]( [Spatial Analysis in R with sf and raster]( Spatial Analysis in R with sf and raster Taught by Zev Ross, President, ZevRoss Spatial Analysis There has never been a better time to use R for spatial analysis! The brand new sf package has made working with vector data in R a breeze and the raster package provides a set of powerful and intuitive tools to work gridded data like satellite imagery. In this course you will learn why the sf package is rapidly taking over spatial analysis in R. [Start Learning]( Machine Learning with Tree-Based Models in R: What You'll Learn Chapter 1: [Classification Trees]( This chapter covers supervised machine learning with classification trees. Chapter 2: [Regression Trees]( In this chapter you'll learn how to use a single tree for regression, instead of classification. Chapter 3: [Bagged Trees]( In this chapter, you will learn about Bagged Trees, an ensemble method, that uses a combination of trees (instead of only one). Chapter 4: [Random Forests]( Learn how to train, tune and evaluate Random Forest models in R. Chapter 5: [Boosted Trees]( In this chapter, you will see the boosting methodology with a focus on the Gradient Boosting Machine (GBM) algorithm. [Play Now]( Spatial Analysis in R with sf and raster: What You'll Learn Chapter 1: [Vector and Raster Spatial Data in R]( An introduction to import/export, learning the formats and getting to know spatial data. Chapter 2: [Preparing layers for spatial analysis]( Learn how to prepare layers so that you can conduct spatial analysis. Chapter 3: [Conducting spatial analysis with the sf and raster packages]( Now that you have learned about sf and raster objects and have prepared your layers for analysis we can begin conducting true spatial analysis. Chapter 4: [Combine your new skills into a mini-analysis]( You are now ready to combine your skills into a mini-analysis. The goal is to evaluate whether the average canopy density by NYC neighborhood is correlated with the number of trees by neighborhood and to create a nice plot of the result. [Play Now]( [DataCamp] [DataCamp]( DataCamp Inc. | 350 Fifth Avenue | Suite 7730 | New York, NY 10118 [Facebook] [Facebook]( [Twitter] [Twitter]( [LinkedIn] [LinkedIn]( [YouTube] [YouTube]( [Unsubscribe](