Analytics
Exploratory Data Analysis
Data Governance: Understanding Data Quality and Normalization
Data normalization best practices are essential for organizing and standardizing data effectively. In this course, you will gain an understanding of data quality dimensions, roles and responsibilities, and best practices for ensuring data quality.
Pluralsight Teams₹4,166.75 per user/month, billed annually
₹50,004
CompleteBilled Monthly
₹2,370/mo
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Course Insight
Suitable for beginner learners. This course serves as an entry point into Data Science, building foundational knowledge before moving on to advanced frameworks or specialized paths.
Beginner FriendlySelf-Paced Learning
SKILLS TO
MASTER
ModelingTrending
Predictive Machine LearningSQL Querying
Relational Data ManagementCareers:Relevant for professionals pursuing roles within Data Science.
💡This course fits perfectly into our comprehensiveData Science Learning Path. Explore the ecosystem to see how it compares to other foundational skills.
Quick Facts
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What You’ll Learn
- Course Overview : 1min.
- Evaluate Data Quality Best Practices : 24mins.
- Evaluate Normalization Best Practices : 33mins.
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Description
Enterprises struggle with data quality issues and seek data normalization for more effective data management.
In this course, Data Governance: Understanding Data Quality and Normalization, you'll gain the ability to understand and apply best practices for ensuring data quality and normalization.
First, you'll explore data quality dimensions, including key concepts, principles, and terminology related to data quality. Then, you'll learn about the impact of poor data quality and the roles and responsibilities of data governance.
Next, you'll explore data normalization importance and best practices, the process of standardizing, and how to organize data effectively.
Finally, you'll briefly see the implementation of normalization techniques.
When you're finished with this course, you'll have the skills and knowledge of data quality and normalization best practices needed to identify, monitor, and resolve data quality issues, as well as to apply data normalization best practices to your work.
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