DataOps Methodology
Master DataOps principles to improve data management, integration, and automation across your organization.
Master DataOps principles to improve data management, integration, and automation across your organization.
This course introduces the DataOps methodology, a collaborative data management practice that enhances communication, integration, and automation of data flows. Students will learn to establish a repeatable process for building and deploying analytics and data pipelines, following data governance and model management practices. The curriculum covers the entire DataOps lifecycle, from establishing teams and toolchains to iterating on data discovery, quality, and usage. Learners will gain practical skills in articulating business value, prioritizing data tasks, and implementing data movement and integration projects.
4.7
(42 ratings)
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What you'll learn
Understand the fundamentals of DataOps and its role in data management
Establish effective DataOps teams and toolchains
Implement data discovery and classification techniques
Develop strategies for ensuring and improving data quality
Learn data preparation and transformation methods
Understand data movement and integration considerations
Skills you'll gain
This course includes:
2.96 Hours PreRecorded video
14 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 7 modules in this course
This course provides a comprehensive introduction to the DataOps methodology, designed to enable organizations to utilize a repeatable process for building and deploying analytics and data pipelines. Students will learn about the fundamental principles of DataOps, including team structures, toolchains, and the iterative process of knowing, trusting, and using data. The curriculum covers key aspects such as data discovery, classification, quality management, and data preparation. Learners will gain practical skills in establishing DataOps processes, measuring business value, and implementing data movement and integration projects. By the end of the course, students will be equipped to apply DataOps practices to drive value through high-quality, trusted data across their organizations.
Establish DataOps - Prepare for operation
Module 1 · 1 Hours to complete
Establish DataOps – Optimize for operation
Module 2 · 2 Hours to complete
Iterate DataOps - Know your data
Module 3 · 1 Hours to complete
Iterate DataOps – Trust your data
Module 4 · 1 Hours to complete
Iterate DataOps – Use your data
Module 5 · 2 Hours to complete
Improve DataOps
Module 6 · 39 Minutes to complete
Summary & Final Exam
Module 7 · 35 Minutes to complete
Fee Structure
Payment options
Financial Aid
Instructor
IBM WW Lead for DataOps Center of Excellence, Expert in Data Management and Governance
Elaine Hanley is the IBM Worldwide Lead for the DataOps Center of Excellence, where she helps organizations globally unlock the value of their data to drive analytics-based decision-making. Her role involves guiding diverse organizations to align their resources toward achieving focused business objectives. Elaine collaborates with IBM engineering teams to define and execute product strategy, integrating multiple aspects of information management. With over 25 years of experience, her expertise spans coding, product management, and data governance. She holds a BAI in Software Engineering from Trinity College, Dublin, and an M.Sc. in Computer Applications from Dublin City University.
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4.7 course rating
42 ratings
Frequently asked questions
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