Data Governance: Understanding Data Quality and Normalization
Offered By: Pluralsight
Course Description
Overview
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.
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.
In this course, you will gain an understanding of data quality dimensions, roles and responsibilities, and best practices for ensuring data quality.
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.
Syllabus
- Course Overview 1min
- Evaluate Data Quality Best Practices 24mins
- Evaluate Normalization Best Practices 33mins
Taught by
Pluralsight
Related Courses
Data Base Management SystemIndian Institute of Technology, Kharagpur via Swayam Healthcare Data Models
University of California, Davis via Coursera Image Data Augmentation with Keras
Coursera Project Network via Coursera Compare time series predictions of COVID-19 deaths
Coursera Project Network via Coursera Practicing Machine Learning Interview Questions in R
DataCamp