Cleaning data in Excel : fixing names, addresses, emails, and phone numbers.

Format:

Video

Contributor:

Walsh, Susan (Data management consultant), instructor.

O'Reilly (Firm), publisher.

Language:

English

Subjects (All):

Microsoft Excel (Computer file).

Physical Description:

1 online resource (1 video file (3 hr., 21 min.)) : sound, color.

Edition:

[First edition].

Place of Publication:

[Place of publication not identified] : O'Reilly Media, Inc., 2024.

Summary:

Dirty data is everywhere. Have you had a letter addressed to you with your name misspelled? That's dirty data. Do you have trouble finding a product on a website because it's not in the right category? That's dirty data. Do you have trouble finding a person or company name in your system because of a typo? That's dirty data. And it causes problems, lots of problems. Before software implementation, machine learning and AI, you need clean data. It's THE most important thing to fix before doing any of these things, and we're all expected to be able to clean or fix this data, yet no one tells us how to do it! Some of the most common data problems occur in name, address, email and phone number data, and this course will cover this off. While there are many data-cleaning tools out there, almost everyone uses Excel, including non-data professionals, and it's important to have the skills to be familiar with and be able to get to know your data before you use these tools. It's essential to be able to spot and fix errors in Excel first, and understand when the data isn't right so you know if you can trust those fancy tools or not! This course will help you achieve this and you'll learn: What is dirty data? The importance of data quality Tips for cleaning data in Excel How to clean names, addresses, emails and phone numbers in Excel You'll even get your own dirty data set to work on! This course is for you because... You have data quality issues in your organization and would like your team to understand the importance of data quality and how to improve it. You're a data professional spending a significant amount of time cleaning data and would like to work more efficiently. You're not a data professional, but work with data and want to work more efficiently with it. Prerequisites: Some experience of Excel would be beneficial.

Notes:

OCLC-licensed vendor bibliographic record.

OCLC:

1423521812

Publisher Number:

0636920975403