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Intro to Trifacta: Clean Your Data Quickly and Easily

2.5 hours on-demand video

$ 10.99

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Brief Introduction

Learn a free tool that will reduce your data cleaning and data preparation time

Description

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.

Requirements

Requirements
A burning desire to learn how to successfully clean data
The free version of Trifacta Wrangler

Knowledge

Navigate Trifacta's grid-view panel
Upload datasets to Trifacta
Begin a flow in Trifacta
Engage Trifacta's predictive capabilities
Use Trifacta's drop-down menus
Isolate and view transformed rows
Understand Wrangle language
Use the header, derive, set, delete, drop, deduplicate, extract, split, settype, replace, and rename transforms
Use the len, coalesce, find, if, in, ismissing, isvalid, isnull, ismismatched and coalesce functions
Build patterns
Escape special characters
Use logical and comparison operators
Employ matches
Differentiate Numbering Systems

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