Intro to Trifacta: Clean Your Data Quickly and Easily
In this course, you’ll walk through Trifacta *basics step by step. We’ll take you through not only how to use **Trifacta and its transforms and functions, but also what common pitfalls you might encounter along the way while cleaning data. You’ll see the real experience of data cleaning. Data cleaning isn’t always clearcut, and this is why we’ll show you what it looks like to iterate changes on your dataset as new information presents itself during the data preparation/data munging process.
*This is a very basic course geared toward people who have little experience with data cleaning.
**Please note that in this course, we use an older version of Trifacta Wrangler.
Note: Data analysts and scientists spend up to 80 percent of their time preparing and cleaning their data. This is a lot of time that could be used in more important phases of the data life cycle, so saving time at the data preparation stage gives you a competitive edge in the data space because you can use saved time toward more important things, like analyzing your data.
Forrester research identifies data preparation tools as “must haves.” Trifacta Wrangler is one of those tools and the product is guided by a board of advisors that has the likes of DJ Patil and Jeff Hammerbacher, among other notables. The company has designed the product to guide you through the data prep, requiring less coding skills.
This tests your understanding of Trifacta basics
Text and Strings
Correction: As we work to remove "WC" in this lecture, we also remove parts of last names, like "Li" or "de." To avoid this, you can simply replace the string "WC" instead of the more complicated pattern match we use in the video.
This tests your understanding of pattern symbols.
This quiz tests on the various operators we've covered in this section.