New courses: Statistical Thinking & Geospatial Data

Expand your hacker statistics skills and learn to visualize spatial data with our newest courses! In both courses, you will work with real-world data so that the skills you learn will transfer over to new data science applications! [DataCamp] New Courses! [Continue Statistics in Python and Discover Geospatial Data!] [Statistical Thinking in Python (part 2)] [In this course, you will expand your probabilistic mindset and hone your hacker stats skills. You will work with real data sets as you learn, including an analysis of Darwin's famous finches.] [Play now ›] [Working with Geospatial Data in R] [Learn to work with spatial data using both data frames and objects used to represent spatial data. You'll read, explore, and manipulate these objects and ultimately make your own maps!] [Play now ›] Statistical Thinking in Python (part 2): What You'll Learn Chapter One: Parameter Estimation by Optimization Learn how to find optimal parameters that best describe your data. Chapter Two: Bootstrap Confidence Intervals This chapter will introduce you to bootstrapping, an extraordinarily powerful tool in statistical inference. Chapter Three: Introduction to Hypothesis Testing Hypothesis tests are the icing on the inference cake—learn to construct and test hypotheses. Chapter Four: Hypothesis Test Examples Gain valuable practice testing your hypotheses. Chapter Five: Putting it all Together: A Case Study Use your skills in statistical inference to witness evolution in action using data on Darwin's finches. [Play now ›] --------------------------------------------------------------- Working with Geospatial Data in R: What You'll Learn Chapter One: Basic Mapping with ggplot2 and ggmap Learn what makes spatial data special and familiarize yourself with common types of spatial data. Chapter Two: Point and Polygon Data Learn how to make maps easily using special spatial objects. Chapter Three: Raster Data and Color Learn about the raster package and how to use color effectively. Chapter Four: Data Import and Projections Go through the process of creating a polished map from raw spatial data files. [Play now ›] --------------------------------------------------------------- Coming Soon: Statistical Modeling in R (part 1) This course is designed to get you up to speed with the most important and powerful methodologies in statistics. [Stat Modeling 1] Statistical Modeling in R (part 2) In Part 2, you'll learn about effect size and interaction, sampling variability, collinearity, and more. [Stat modeling 2] Data Visualization with ggplot2 (part 3) In the 3rd ggplot2 course, learn about geoms commonly used in maths and sciences, handling large data sets, specialty plots, and more. [ggplot part 3] DataCamp Inc. 2067 Massachusetts avenue Cambridge MA 02141 [Unsubscribe]