Intro to Trifacta: Clean Your Data Quickly and Easily
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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
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
1 student enrolled
Last updated 9/2017
English
English [Auto-generated]
Current price: $10 Original price: $35 Discount: 71% off
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Includes:
  • 2.5 hours on-demand video
  • 3 Articles
  • 2 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • 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
View Curriculum
Requirements
  • A burning desire to learn how to successfully clean data
  • The free version of Trifacta Wrangler
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.

By the end of this course, you'll feel like you're one of the data pros. All you'll need to do is continue using your newly acquired skills to keep them fresh!

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” and ranks Trifacta and one other competitor in the lead. Not only that, 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.

Who is the target audience?
  • You, if you are brand new to data cleaning
  • You, if you are brand new to using Trifacta software
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Curriculum For This Course
48 Lectures
02:26:31
+
Course Introduction
2 Lectures 02:44
+
Trifacta Basics
12 Lectures 31:06

Dataset 1
00:23



Grid Panel Overview
04:52

Ready-Made Recipe Steps
02:43

Quick Recipe Changes
01:04

Suggestion Cards
06:01

Keep and Delete
02:34

Drop-Down Menu Changes
05:21


This tests your understanding of Trifacta basics

Trifacta Basics
2 questions

Section 2 Conclusion
00:13
+
Text and Strings
21 Lectures 01:22:39
Dataset 2
00:08

Intro to Section 3
00:11

Wrangle Language
01:27

Text and Strings Overview
01:29

Dataset Prep.
01:03

Derive Transform with Len Function
03:25

Data Fidelity
01:16

Column Splitting
01:59

Inconsistencies
01:34

Strings and Patterns Overview
03:34

String Cleanup
07:44

Pattern Building
05:56

Patterns with Replace Transform
07:34

Split, Merge, and Left
08:21

Special-Character Escape
11:43

Set Transform with If Function
08:18

Extract Transform
05:38

Derive Transform with Find Function
02:05

Coalesce Function
07:51

Numbering Systems
01:14

This tests your understanding of pattern symbols.

Pattern Symbols
6 questions

Section 3 Conclusion
00:09
+
Data Filtering
13 Lectures 29:57
Intro to Section 4
00:08

Dataset 3
00:06

Intro to Data Filtering
02:10

Deduplicate Function
03:34

Column Removal before Analysis
00:52

Isnull and Ismissing
02:35

Ismismatched and Isvalid
03:17

Comparison Operators
04:24

Logical Operators
05:35

String Value with In Function
04:12

Matches
02:32

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

Operators
2 questions

Section 4 Conclusion
00:13

Course Conclusion
00:19
About the Instructor
Curtis Seare and Ginette Methot
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Curtis is Director of Analytics with 8+ Years of Experience

Hi there! I'm Curtis Seare, and I'm Ginette Methot, and we cohost an Austin-based podcast called Data Crunch. We are passionate that, no matter where you are or what work you do, you can learn to be data literate in a data-focused world, not only to understand a changing world culture, but also to do fascinating things, because you can with the right tools and instruction. We're excited to introduce you to a new world of wonder.  


Curtis Seare

I didn't know I would end up working with data. In fact, I thought I was headed to get a PhD in Chemistry, but that all changed when I decided to go into business instead. Now I've worked for over eight years in the data space. I received my master's from Northwestern in Predictive Analytics, and I am now the Director of Analytics at an Austin-based startup, where I work in the thick of data every day. I'll teach you what I've learned over the years. 

Ginette Methot

I'm new to data (so that's why I know that if I can pick it up, you definitely can!). My degrees are in the humanities and English, and I've worked as an editor and writer for many years—so very far from data. But is it? There's a TON of work being done in traditionally non-data-focused fields, including English and humanities. So let your imagination run wild with what might be possible with data in your field, and gain the tools to bring that dream to life.