Book Image

Learning Alteryx

By : Renato Baruti
Book Image

Learning Alteryx

By: Renato Baruti

Overview of this book

Alteryx, as a leading data blending and advanced data analytics platform, has taken self-service data analytics to the next level. Companies worldwide often find themselves struggling to prepare and blend massive datasets that are time-consuming for analysts. Alteryx solves these problems with a repeatable workflow designed to quickly clean, prepare, blend, and join your data in a seamless manner. This book will set you on a self-service data analytics journey that will help you create efficient workflows using Alteryx, without any coding involved. It will empower you and your organization to take well-informed decisions with the help of deeper business insights from the data.Starting with the fundamentals of using Alteryx such as data preparation and blending, you will delve into the more advanced concepts such as performing predictive analytics. You will also learn how to use Alteryx’s features to share the insights gained with the relevant decision makers. To ensure consistency, we will be using data from the Healthcare domain throughout this book. The knowledge you gain from this book will guide you to solve real-life problems related to Business Intelligence confidently. Whether you are a novice with Alteryx or an experienced data analyst keen to explore Alteryx’s self-service analytics features, this book will be the perfect companion for you.
Table of Contents (17 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Chapter 6. Data Parsing Techniques

The Parse Tool palette offers parsing options that will save you time by having the tools that do it all for you, such as the Text to Columns tool, which allows you to split text from one field into multiple columns or rows. Don't forget the numerous times a file comes in with a string formatted field when it should be a date formatted field. This complexity is all solved by utilizing the DateTime tool. The opportunities for parsing are limitless with regular expressions, where you will dive into more advanced features of parsing your data.

This chapter will cover the following topics:

  • Text to columns
  • Converting string to dates and dates to strings
  • Regular expressions