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Principles for Data Quality Measures

Niraj Joshi

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.

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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.

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.

This course is no longer available. Find something similar by browsing:
Data Quality Data Cataloging Metadata Management MLOps Data Lineage Machine Learning Data Governance

What's inside

Syllabus

Course Overview
Introducing Data Discovery and Cataloging
Evaluating Data Quality and Profiling
Tracking Data Lineage and Governance
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Explores data lineage and governance, which are fundamental in building robust and maintainable ML pipelines
Introduces concepts of data cataloging and metadata management, which are core principles in data engineering
Taught by recognized instructors in the field of MLOps
Covers quality measures, which are essential for data-driven organizations
Examines best practices in metadata management, which enhances the understanding and utilization of data assets
Requires learners to come in with some prior knowledge of data science and machine learning

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Reviews summary

Foundational principles for data quality in mlops

According to students, this course provides a solid foundational understanding essential for anyone in MLOps or data roles. Learners praise its well-structured modules that cover data discovery, quality profiling, lineage, and metadata management. They found the content highly relevant and immediately applicable to professional scenarios, especially for establishing robust data governance. While the course excels in conceptual principles, some learners noted it is less focused on hands-on tool implementation, suggesting it serves as an excellent theoretical primer that may require further practical exploration.
Logically organized topics, easy to follow the progression.
"I appreciated the clear progression from data discovery to governance and metadata."
"The topics are presented in an easy-to-digest, step-by-step manner, which was very helpful."
"The course overview matched the content delivered, making it easy to track my learning."
Focuses on fundamental principles, providing theoretical depth.
"The course provides an excellent conceptual understanding of data quality measures."
"I appreciate the focus on 'principles' rather than just tools, which makes it timeless."
"It helped me understand the 'why' behind data quality and governance practices."
Highly relevant for MLOps, data science, and data engineering.
"The concepts discussed are highly practical for my work in data governance and ML pipelines."
"I can immediately apply the principles of metadata management to our existing systems."
"This course fills a crucial gap in understanding data lifecycle for ML models."
Offers a robust understanding of data quality for MLOps.
"I found this course invaluable for understanding core data quality measures in ML workflows."
"It really helped me grasp data discovery, profiling, and governance best practices."
"I now have a solid framework for approaching data quality challenges in my ML projects."
Primarily theoretical with fewer practical tool examples or labs.
"I was hoping for more practical examples using specific data quality tools or platforms."
"While the concepts are strong, it would benefit from more hands-on labs or demos."
"The course lays a great foundation, but I'll need to explore tool implementation separately."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Principles for Data Quality Measures with these activities:
Compile and review foundational data concepts
Reinforce your understanding of basic data concepts to establish a strong foundation for MLOps.
Show steps
  • Review introductory books or articles on data concepts
  • Summarize key points and definitions in your own words
Practice data profiling and cleaning exercises
Strengthen your ability to identify and correct data inconsistencies, a critical step in MLOps.
Browse courses on Data Profiling
Show steps
  • Download practice datasets
  • Use data profiling tools or techniques to analyze and identify data issues
  • Apply data cleaning methods to rectify errors and prepare the data for modeling
Create a data quality checklist
Develop a personalized framework to assess and ensure data quality, a cornerstone of MLOps.
Show steps
  • Research different data quality dimensions and metrics
  • Identify relevant metrics for your specific data and use case
  • Create a comprehensive checklist based on your findings
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore MLOps platforms and tools
Gain hands-on experience with the tools and technologies used in MLOps, enhancing your understanding of the practical aspects.
Show steps
  • Identify industry-leading MLOps platforms
  • Explore their features and capabilities
  • Complete guided tutorials or documentation to understand their usage
Participate in peer discussions on data quality best practices
Engage with fellow students to share insights and learn from diverse perspectives on effective data quality practices in MLOps.
Show steps
  • Join online forums or communities focused on MLOps
  • Participate in discussions and share your experiences
  • Seek feedback from peers on your data quality approaches
Develop a data cataloging framework
Design and implement a comprehensive data catalog to streamline data discovery and management, a crucial aspect of MLOps.
Show steps
  • Define data cataloging principles and standards
  • Identify data sources and collect metadata
  • Design and implement a data catalog using appropriate tools or platforms
Mentor a junior or inexperienced peer in MLOps
Consolidate your understanding of MLOps by sharing your knowledge and skills with others, while also supporting their growth in the field.
Browse courses on Knowledge Sharing
Show steps
  • Identify opportunities to mentor or guide others interested in MLOps
  • Share your expertise and experiences
  • Provide constructive feedback and support

Career center

Learners who complete Principles for Data Quality Measures will develop knowledge and skills that may be useful to these careers:
Data Quality Analyst
A Data Quality Analyst analyzes raw data and ensures that it meets certain quality standards. They may work with data from a variety of sources, including databases, spreadsheets, and web logs. A course like Principles for Data Quality Measures may be useful for someone in this role in that it can help them understand the importance of data quality and provide them with the tools and techniques needed to assess data quality. This course can also help learners to understand how to build data pipelines that ensure that data is clean and consistent.
See salaries and explore the career path for Data Quality Analyst
Data Scientist
The job of a Data Scientist is to use data to solve business problems. They may work with data from a variety of sources, including databases, spreadsheets, and web logs. A course like Principles for Data Quality Measures may be useful for someone in this role in that it can help them understand the importance of data quality and provide them with the tools and techniques to assess data quality. This course can also help learners build data pipelines that ensure that data is clean and consistent.
See salaries and explore the career path for Data Scientist
Data Engineer
The job of a Data Engineer is to design, build, and maintain data pipelines. They work with data from a variety of sources, including databases, spreadsheets, and web logs. A course like Principles for Data Quality Measures may be useful for someone in this role in that it can help them understand the importance of data quality and provide them with the tools and techniques needed to assess data quality. This course can also help learners understand how to build data pipelines that ensure that data is clean and consistent.
See salaries and explore the career path for Data Engineer
Data Architect
The job of a Data Architect is to design and manage data systems. They work with data from a variety of sources, including databases, spreadsheets, and web logs. A course like Principles for Data Quality Measures may be useful for someone in this role in that it can help them understand the importance of data quality and provide them with the tools and techniques needed to assess data quality. This course can also help learners understand how to build data pipelines that ensure that data is dean and consistent.
See salaries and explore the career path for Data Architect
Database Administrator
The job of a Database Administrator is to manage and maintain databases. They work with data from a variety of sources, including databases, spreadsheets, and web logs. A course like Principles for Data Quality Measures may be useful for someone in this role in that it can help them understand the importance of data quality and provide them with the tools and techniques needed to assess data quality. This course can also help learners understand how to build data pipelines that ensure that data is clean and consistent.
See salaries and explore the career path for Database Administrator
Business Analyst
The job of a Business Analyst is to analyze business needs and requirements. They work with data from a variety of sources, including databases, spreadsheets, and web logs. A course like Principles for Data Quality Measures may be useful for someone in this role in that it can help them understand the importance of data quality and provide them with the tools and techniques needed to assess data quality. This course can also help learners understand how to build data pipelines that ensure that data is clean and consistent.
See salaries and explore the career path for Business Analyst
Project Manager
The job of a Project Manager is to plan, execute, and track projects. They work with data from a variety of sources, including databases, spreadsheets, and web logs. A course like Principles for Data Quality Measures may be useful for someone in this role in that it can help them understand the importance of data quality and provide them with the tools and techniques needed to assess data quality. This course can also help learners understand how to build data pipelines that ensure that data is dean and consistent.
See salaries and explore the career path for Project Manager
Software Engineer
The job of a Software Engineer is to design, develop, and maintain software applications. They work with data from a variety of sources, including databases, spreadsheets, and web logs. A course like Principles for Data Quality Measures may be useful for someone in this role in that it can help them understand the importance of data quality and provide them with the tools and techniques needed to assess data quality. This course can also help learners understand how to build data pipelines that ensure that data is clean and consistent.
See salaries and explore the career path for Software Engineer
Data Analyst
The job of a Data Analyst is to analyze data and create reports and visualizations. They work with data from a variety of sources, including databases, spreadsheets, and web logs. A course like Principles for Data Quality Measures may be useful for someone in this role in that it can help them understand the importance of data quality and provide them with the tools and techniques needed to assess data quality. This course can also help learners understand how to build data pipelines that ensure that data is clean and consistent.
See salaries and explore the career path for Data Analyst
Quality Assurance Analyst
The job of a Quality Assurance Analyst is to test and evaluate software applications. They work with data from a variety of sources, including databases, spreadsheets, and web logs. A course like Principles for Data Quality Measures may be useful for someone in this role in that it can help them understand the importance of data quality and provide them with the tools and techniques needed to assess data quality. This course can also help learners understand how to build data pipelines that ensure that data is clean and consistent.
See salaries and explore the career path for Quality Assurance Analyst
Information Security Analyst
The job of an Information Security Analyst is to protect data from unauthorized access, use, disclosure, disruption, modification, or destruction. They work with data from a variety of sources, including databases, spreadsheets, and web logs. A course like Principles for Data Quality Measures may be useful for someone in this role in that it can help them understand the importance of data quality and provide them with the tools and techniques needed to assess data quality. This course can also help learners understand how to build data pipelines that ensure that data is clean and consistent.
See salaries and explore the career path for Information Security Analyst
Risk Analyst
The job of a Risk Analyst is to identify, assess, and manage risks. They work with data from a variety of sources, including databases, spreadsheets, and web logs. A course like Principles for Data Quality Measures may be useful for someone in this role in that it can help them understand the importance of data quality and provide them with the tools and techniques needed to assess data quality. This course can also help learners understand how to build data pipelines that ensure that data is clean and consistent.
See salaries and explore the career path for Risk Analyst
Compliance Analyst
The job of a Compliance Analyst is to ensure that an organization complies with all applicable laws and regulations. They work with data from a variety of sources, including databases, spreadsheets, and web logs. A course like Principles for Data Quality Measures may be useful for someone in this role in that it can help them understand the importance of data quality and provide them with the tools and techniques needed to assess data quality. This course can also help learners understand how to build data pipelines that ensure that data is clean and consistent.
See salaries and explore the career path for Compliance Analyst
Auditor
The job of an Auditor is to examine and evaluate financial records and other data to ensure accuracy and compliance with laws and regulations. They work with data from a variety of sources, including databases, spreadsheets, and web logs. A course like Principles for Data Quality Measures may be useful for someone in this role in that it can help them understand the importance of data quality and provide them with the tools and techniques needed to assess data quality. This course can also help learners understand how to build data pipelines that ensure that data is clean and consistent.
See salaries and explore the career path for Auditor
Actuary
The job of an Actuary is to use mathematical and statistical methods to assess risk and uncertainty. They work with data from a variety of sources, including databases, spreadsheets, and web logs. A course like Principles for Data Quality Measures may be useful for someone in this role in that it can help them understand the importance of data quality and provide them with the tools and techniques needed to assess data quality. This course can also help learners understand how to build data pipelines that ensure that data is clean and consistent.
See salaries and explore the career path for Actuary

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Principles for Data Quality Measures.
Cover image
Getting in Front on Data
Save
Provides a comprehensive guide to data quality best practices and techniques. It valuable resource for practitioners who want to improve the quality of their data.
Getting in Front on Data: Who Does What
Paperback
$$
Cover image
People and Data
Save
Shares real-world experiences and case studies of data quality initiatives. It provides practical advice on how to overcome common challenges and build a successful data quality program.
People and Data
Paperback
Check
People and Data
Kindle Edition
Check
Enterprise Knowledge Management
Save
Provides a comprehensive guide to measuring and improving data quality. It covers topics such as data quality assessment, data quality improvement, and data quality management.
Enterprise Knowledge Management: The Data Quality...
Paperback
$$$
Cover image
Big Data Analytics
Save
Provides a foundation in data quality, covering concepts, techniques, and best practices. It explains the importance of data quality for organizations and presents methods for assessing and improving data quality.
Big Data Analytics: Turning Big Data into Big Money
Audible Audiobook
$$
Big Data Analytics: Turning Big Data into Big Money...
Kindle Edition
$$
Cover image
Data Science for Business
Save
Provides a comprehensive overview of data science for business. It valuable resource for practitioners who want to understand data mining and data-analytic thinking.
Data Science for Business: What You Need to Know...
Paperback
$$
Data Science for Business: What You Need to Know...
Kindle Edition
$$
Cover image
Data Mining with R
Save
Provides a practical guide to data mining using R. It valuable resource for practitioners who want to learn data mining techniques.
Data Mining with R: Learning with Case Studies,...
Hardcover
$$$
Data Mining with R: Learning with Case Studies...
Hardcover
$$
Data Mining with R: Learning with Case Studies,...
Kindle Edition
$$
Cover image
Data Visualization
Save
Provides a practical introduction to data visualization. It valuable resource for practitioners who want to learn how to visualize data.
Data Visualization: A Successful Design Process: A...
Paperback
$
Data Visualization: Representing Information on...
Kindle Edition
$$$
Data Visualization Design and Utilization (Korean...
Paperback
$$$
Cover image
Machine Learning with Spark and Python
Save
Provides a practical introduction to machine learning in Python. It valuable resource for practitioners who want to learn machine learning techniques.
Machine Learning with Spark and Python: Essential...
Paperback
$$
(中文) Spark and Python Machine Learning in Action: Core...
Paperback
$$
Machine Learning with Spark and Python: Essential...
Kindle Edition
$$
Cover image
Python for Data Analysis
Save
Provides a practical introduction to Python for data analysis. It valuable resource for practitioners who want to learn Python.
Python for Data Analysis
Paperback
Check
Python for Data Analysis
Kindle Edition
Check
Cover image
Data Science from Scratch
Save
Provides a practical introduction to data science from scratch. It valuable resource for practitioners who want to learn data science.
Data Science from Scratch: First Principles with...
Paperback
$$
Data Science from Scratch: First Principles with...
Kindle Edition
$$
Data Science from Scratch: First Principles with...
Paperback
$$

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Course image
Time
43 hours
Level
Advanced
Via
Pluralsight
Instructor
Niraj Joshi
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Explores data lineage and governance, which are fundamental in building robust and maintainable ML pipelines
Introduces concepts of data cataloging and metadata management, which are core principles in data engineering
Taught by recognized instructors in the field of MLOps
Covers quality measures, which are essential for data-driven organizations
Examines best practices in metadata management, which enhances the understanding and utilization of data assets
Requires learners to come in with some prior knowledge of data science and machine learning
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