Prior to leading the Course Development team at DataCamp, Nick earned his master's degree at Johns Hopkins Biostatistics and worked as a data scientist for McKinsey. Nick's passion for teaching data science began in graduate school, where was heavily involved in tutoring fellow students, developing the Johns Hopkins Data Science Specialization, and building the swirl R package.
It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time actually analyzing it. For this reason, it is critical to become familiar with the data cleaning process and all of the tools available to you along the way. This course provides a very basic introduction to cleaning data in R, so that you can get from raw data to awesome insights as quickly and painlessly as possible!
This chapter will give you an overview of the process of data cleaning with R, then walk you through the basics of exploring raw data.
This chapter will give you an overview of the principles of tidy data, how to identify messy data, and what to do about it.
This chapter will teach you how to prepare your data for analysis. We will look at type conversion, string manipulation, missing and special values, and outliers and obvious errors.
In this chapter, you will practice everything you've learned from the first three chapters in order to clean a messy dataset using R.