Is Principles for Data Quality Measures beginner friendly?

No, this is an advanced level course. It is recommended for learners who already have foundational knowledge and practical experience in the domain.

How long does it take to complete Principles for Data Quality Measures?

The total duration of the course is approximately 43 minutes. Since it is generally self-paced, completion time depends entirely on your learning schedule and availability.

Is this course more theory-focused or project-focused?

This course provides a strong mix of conceptual theory and practical application to ensure a comprehensive understanding of the subject matter.

Data Science

Principles for Data Quality Measures

Name: Principles for Data Quality Measures
Availability: InStock
Rating: 3 (69 reviews)

This course will teach you the aspects to understand MLOps journey, end to end data quality checks and establish the mechanism of data cataloging, principles around metadata management and data governance.

3|Reviews (69)

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Course Insight

Designed for advanced/expert practitioners. Designed for experienced practitioners. We recommend having a solid grasp of Data Science fundamentals before starting this specialization.

Advanced LevelSelf-Paced Learning

SKILLS TO
MASTER

Analytics

Exploratory Data Analysis

Modeling

Predictive Machine Learning

SQL Querying

Relational Data Management

Pandas

Matplotlib

Statistics

Tableau

ETL

Careers: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

43 minutes

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What You’ll Learn

Data quality is an important prerequisite prior to machine learning modelling. It is of utmost importance to thoroughly assess data quality before model building. In this course, Principles for Data Quality Measures, you'll learn to build MLOps pipelinse and explore best practices for metadata management. First, you'll explore data discovery and cataloging. Next, you'll discover data profiling and quality checks. Finally, you'll learn to explore data lineage and the best metadata management practices and analyze the MLOps cycle. By the end of this course, you'll gain a better understanding of data discovery, profiling, and metadata management of the ML Model building process.

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