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Intro to Trifacta: Clean Your Data Quickly and Easily

Learn a free tool that will reduce your data cleaning and data preparation time
Instructor:
Curtis Seare and Ginette Methot
142 students enrolled
English [Auto-generated]
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

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.

Course Introduction

1
Course Trailer
2
Introduction

Trifacta Basics

1
Trifacta Download
2
Dataset 1
3
Dataset Upload
4
Flows
5
Grid Panel Overview
6
Ready-Made Recipe Steps
7
Quick Recipe Changes
8
Suggestion Cards
9
Keep and Delete
10
Drop-Down Menu Changes
11
Results Export
12
Trifacta Basics

This tests your understanding of Trifacta basics

13
Section 2 Conclusion

Text and Strings

1
Dataset 2
2
Intro to Section 3
3
Wrangle Language
4
Text and Strings Overview
5
Dataset Prep.
6
Derive Transform with Len Function
7
Data Fidelity
8
Column Splitting
9
Inconsistencies
10
Strings and Patterns Overview
11
String Cleanup
12
Pattern Building
13
Patterns with Replace Transform

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. 

14
Split, Merge, and Left
15
Special-Character Escape
16
Set Transform with If Function
17
Extract Transform
18
Derive Transform with Find Function
19
Coalesce Function
20
Numbering Systems
21
Pattern Symbols

This tests your understanding of pattern symbols.

22
Section 3 Conclusion

Data Filtering

1
Intro to Section 4
2
Dataset 3
3
Intro to Data Filtering
4
Deduplicate Function
5
Column Removal before Analysis
6
Isnull and Ismissing
7
Ismismatched and Isvalid
8
Comparison Operators
9
Logical Operators
10
String Value with In Function
11
Matches
12
Operators

This quiz tests on the various operators we've covered in this section.

13
Section 4 Conclusion
14
Course Conclusion
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Includes

2 hours on-demand video
3 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion