Descriptions
Data Cleaning Fundamentals: Preparing Data for Analytics and AI, Poor data quietly undermines reporting, decision-making, and business performance, and its impact becomes even more critical as organizations invest in analytics and AI. In this course, you’ll learn why data quality matters, how to recognize common types of dirty data across sales, marketing, finance, and procurement, and what to do about them. You’ll be introduced to the COAT framework (Consistent, Organized, Accurate, Trustworthy) and learn how to apply it to assess and improve the data you rely on every day.
Through practical Excel techniques, you’ll learn to identify, clean, standardize, and structure messy datasets so they become usable and reliable. The course also explores how to build a repeatable, AI-ready data-cleaning process, decide which tasks to automate and where human judgment is needed, use Excel formulas and functions, and use Copilot in Excel to identify data-quality issues. A final project brings these skills together to prepare an AI-ready dataset, supporting better reporting, stronger decisions, and more reliable use of analytics and AI.
What you’ll learn
- Explain why data quality matters for reporting, decision-making, business performance, analytics, and AI.
- Recognize common dirty-data problems in areas such as sales, marketing, finance, and procurement.
- Apply the COAT framework—Consistent, Organized, Accurate, and Trustworthy—to assess data quality.
- Standardize and structure data fields such as names, dates, addresses, phone numbers, and product descriptions.
- Identify, clean, and reduce errors and inconsistencies in messy datasets using practical Excel techniques.
- Choose a data-cleaning approach appropriate to dataset scale and prioritize high-impact data.
- Decide which data-cleaning tasks are suitable for automation and where human judgment and spot checks are needed.
- Use Excel formulas, functions, and Copilot to help identify and address data-quality issues.
- Build repeatable data-cleaning habits, including normalization, classification and address spot checks, and master lists.
- Prepare an AI-ready dataset and assess whether data is reliable and fit for its intended purpose.
Who this course is for
- Beginners who want to understand data quality and the risks of dirty data in analytics and AI.
- Business professionals working with sales, marketing, finance, procurement, reporting, or operational data.
- Analysts and data professionals who need practical methods to assess, clean, standardize, and structure datasets.
- Excel users who want to improve data-cleaning workflows and explore Copilot for identifying data-quality issues.
Specificatoin of Data Cleaning Fundamentals: Preparing Data for Analytics and AI
- Publisher : Linkedin
- Teacher : Susan Walsh
- Language : English
- Level : All Levels
- Number of Course :
- Duration : 1 hours and 43 minutes
Content of Data Cleaning Fundamentals: Preparing Data for Analytics and AI
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Sample Clip
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Subtitle : English
Quality: 720p
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Password file(s): www.downloadly.ir
File size
349 MB