Cleaning data in Excel: fixing names, addresses, emails, and phone numbers
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 sys...
Gespeichert in:
| Weitere beteiligte Personen: | |
|---|---|
| Format: | Elektronisch Video |
| Sprache: | Englisch |
| Veröffentlicht: |
[Place of publication not identified]
O'Reilly Media, Inc.
2024
|
| Ausgabe: | [First edition]. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/0636920975403/?ar |
| Zusammenfassung: | 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. |
| Beschreibung: | Online resource; title from title details screen (O'Reilly, viewed February 27, 2024) |
| Umfang: | 1 Online-Ressource (1 video file (3 hr., 21 min.)) sound, color. |
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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. 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| spelling | Cleaning data in Excel fixing names, addresses, emails, and phone numbers [First edition]. [Place of publication not identified] O'Reilly Media, Inc. 2024 1 Online-Ressource (1 video file (3 hr., 21 min.)) sound, color. zweidimensionales bewegtes Bild tdi rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Online resource; title from title details screen (O'Reilly, viewed February 27, 2024) 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. Microsoft Excel (Computer file) Instructional films Nonfiction films Internet videos Films de formation Films autres que de fiction Vidéos sur Internet Walsh, Susan MitwirkendeR ctb O'Reilly (Firm), Verlag pbl |
| spellingShingle | Cleaning data in Excel fixing names, addresses, emails, and phone numbers Microsoft Excel (Computer file) Instructional films Nonfiction films Internet videos Films de formation Films autres que de fiction Vidéos sur Internet |
| title | Cleaning data in Excel fixing names, addresses, emails, and phone numbers |
| title_auth | Cleaning data in Excel fixing names, addresses, emails, and phone numbers |
| title_exact_search | Cleaning data in Excel fixing names, addresses, emails, and phone numbers |
| title_full | Cleaning data in Excel fixing names, addresses, emails, and phone numbers |
| title_fullStr | Cleaning data in Excel fixing names, addresses, emails, and phone numbers |
| title_full_unstemmed | Cleaning data in Excel fixing names, addresses, emails, and phone numbers |
| title_short | Cleaning data in Excel |
| title_sort | cleaning data in excel fixing names addresses emails and phone numbers |
| title_sub | fixing names, addresses, emails, and phone numbers |
| topic | Microsoft Excel (Computer file) Instructional films Nonfiction films Internet videos Films de formation Films autres que de fiction Vidéos sur Internet |
| topic_facet | Microsoft Excel (Computer file) Instructional films Nonfiction films Internet videos Films de formation Films autres que de fiction Vidéos sur Internet |
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