Do you want to learn how data exploration can be implemented in R? Without data exploration, the whole data analysis process gets inefficient and slow, but follow a good data exploration process and you’ll be guided to valuable insights. In this course, Exploring Your First Data Set with R, you will learn how new datasets are explored and analyzed in a quick and efficient way. First, you will learn the methods outlined, following a logical succession, which are applicable in most standard data frames. Then, you will discover how the process is divided into 3 steps: summary statistics, distribution checks, and relation analysis. These steps build on each other and you will find out which variables are worth further analysis and where variable dependencies exist. Finally, you will gain the knowledge of the ground work for machine learning and final data presentation. When you’re finished with this course, you’ll have the skills to properly structure and conduct data exploration in R.
Author Name: Martin Burger
Author Description:
Martin studied biostatistics and worked for several pharmaceutical companies before he became a data science consultant and author. He published over 15 courses on R, Tableau 9 and other data science related subjects. His main focus lies on analytics software like R and SPSS but he is also interested in modern data visualization tools like Tableau. If he is not busy coding, blogging or working out new teaching concepts you may find him skiing or hiking in the Alps.

Table of Contents

• Course Overview
  1min
• Background on Exploratory Data Analysis
  27mins
• First Level Data Exploration
  39mins
• Statistical Tests to Confirm Initial Findings
  36mins
• Looking Ahead and Summary
  19mins