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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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
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What's inside
Syllabus
Course Overview
Introducing Data Discovery and Cataloging
Evaluating Data Quality and Profiling
Tracking Data Lineage and Governance
Read more
Syllabus
Course Overview
Introducing Data Discovery and Cataloging
Evaluating Data Quality and Profiling
Tracking Data Lineage and Governance
Read more
Exploring Best Practices for Metadata Management
Good to know
Good to know
Know what's good ,
what to watch for , 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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Save Principles for Data Quality Measures to your list so you can find it easily later:
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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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Compile and review foundational data concepts
Show steps
Reinforce your understanding of basic data concepts to establish a strong foundation for MLOps.
Show steps
Show steps
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
Show steps
Strengthen your ability to identify and correct data inconsistencies, a critical step in MLOps.
Browse courses on
Data Profiling
Show steps
Show steps
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
Show steps
Develop a personalized framework to assess and ensure data quality, a cornerstone of MLOps.
Show steps
Show steps
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
Show steps
Gain hands-on experience with the tools and technologies used in MLOps, enhancing your understanding of the practical aspects.
Show steps
Show steps
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
Show steps
Engage with fellow students to share insights and learn from diverse perspectives on effective data quality practices in MLOps.
Show steps
Show steps
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
Show steps
Design and implement a comprehensive data catalog to streamline data discovery and management, a crucial aspect of MLOps.
Show steps
Show steps
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
Show steps
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
Show steps
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 Scientist
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.
Data Engineer
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.
Data Quality Analyst
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.
Database Administrator
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.
Business Analyst
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.
Data Architect
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.
Data Analyst
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.
Quality Assurance 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.
Software Engineer
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.
Project Manager
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.
Compliance 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.
Information Security 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.
Risk 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.
Auditor
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.
Actuary
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.
For more career information including salaries, visit:
OpenCourser.com/course/kxm6n5/principles
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.
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.
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.
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.
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.
Provides a comprehensive overview of data science for business. It valuable resource for practitioners who want to understand data mining and data-analytic thinking.
Save
Provides a practical guide to data mining using R. It valuable resource for practitioners who want to learn data mining techniques.
Save
Provides a practical introduction to data visualization. It valuable resource for practitioners who want to learn how to visualize data.
Provides a practical introduction to machine learning in Python. It valuable resource for practitioners who want to learn machine learning techniques.
Provides a practical introduction to Python for data analysis. It valuable resource for practitioners who want to learn Python.
Provides a practical introduction to data science from scratch. It valuable resource for practitioners who want to learn data science.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/kxm6n5/principles
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Time
43 hours
Level
Advanced
Via
Pluralsight
Instructor
Niraj Joshi
Language
English
Good to know
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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