Intro to Trifacta: Clean Your Data Quickly and Easily

Name: Intro to Trifacta: Clean Your Data Quickly and Easily
Rating: 4.5 (223 reviews)

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

Created byData Crunch Training

Last updated 9/2017

English

English [Auto],Korean [Auto],

What you'll 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

Course content

4 sections • 48 lectures • 2h 26m total length

Course Trailer1:35
Introduction1:09
Explore the three data cleaning principles: identify errors, remove irrelevant data, and clarify datasets, using Smithsonian volcanic eruptions dataset, then upload data, build a recipe, and create transforms in Trifacta.

Dataset 10:24
Trifacta Download3:04
Dataset Upload2:12
Flows0:50
Grid Panel Overview4:52
Navigate the grid panel with rows and columns using touch scrolling. Use the left data-type dropdown and right drop-downs to transform data, and read data quality bar and column histogram.
Ready-Made Recipe Steps2:43
Quick Recipe Changes1:04
Suggestion Cards6:01
Keep and Delete2:34
Drop-Down Menu Changes5:21
Results Export1:49
Trifacta Basics
Section 2 Conclusion0:13

Dataset 20:11
Intro to Section 30:11
Wrangle Language1:27
Text and Strings Overview1:29
Learn to manipulate strings and format text from messy data, clean names and tweets, and explore a volcano references dataset with fields like volcano name, authors, year, title, and source.
Dataset Prep.1:03
apply a header transform to promote the first row to column names, correcting misaligned headers in the raw data for clean dataset prep in Trifacta.
Derive Transform with Len Function3:25
Data Fidelity1:16
Column Splitting1:59
Inconsistencies1:34
Strings and Patterns Overview3:34
String Cleanup7:44
Learn how Trifacta uses pattern matching, alpha and digit matchers, curly brackets, spaces and end of string, literals, and or logic to clean data and replace initials with commas.
Pattern Building5:56
Patterns with Replace Transform7:34
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.
Split, Merge, and Left8:21
Special-Character Escape11:43
Set Transform with If Function8:18
Learn to apply set transform and the if function to replace missing author four with at all, merge author short with author four, and manage delimiters for clean data.
Extract Transform5:38
Explore how the extract transform pulls URLs from a source column into a new URL column, using patterns to capture http(s) links and improve data accessibility.
Derive Transform with Find Function2:05
Coalesce Function7:51
Numbering Systems1:14
Explain numbering systems in Trifacta, contrasting the character count system (positive integers, no zero) with the character index system (zero allowed, spaces counted), as shown in the information aid box.
Pattern Symbols
Section 3 Conclusion0:09
Apply string operations in Trifacta, mastering patterns, set, extract, split, merge, and left techniques as you wrap up section 3 and move forward.

Intro to Section 40:08
Curtis presents data filtering techniques from a fresh angle. Build on concepts we've already touched on to filter data quickly and effectively.
Dataset 30:06
Intro to Data Filtering2:10
Deduplicate Function3:34
Column Removal before Analysis0:52
Isnull and Ismissing2:35
Ismismatched and Isvalid3:17
Comparison Operators4:24
Logical Operators5:35
Apply logical operators in Trifacta to streamline data filtering, combining conditions with and, or, and not, and choose between delete and keep transforms to remove rows efficiently.
String Value with In Function4:12
Matches2:32
Operators
Section 4 Conclusion0:13
Course Conclusion0:19
Conclude by applying and retaining the data cleaning skills you've learned, and stay engaged by reaching out with any questions; we’re here to help you continue using these techniques.

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.

*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.

Who this course is for:

You, if you are brand new to data cleaning
You, if you are brand new to using Trifacta software

Intro to Trifacta: Clean Your Data Quickly and Easily

What you'll learn

Explore related topics

Course content

Course Introduction2 lectures • 3min

Trifacta Basics12 lectures • 31min

Text and Strings21 lectures • 1hr 23min

Data Filtering13 lectures • 30min

Requirements

Description

Who this course is for: