Learn an overview of MLOps and how to apply best practices for your team.
Analytics and AI teams look to automate their data pipelines to scale. DataOps is the set of best practices and standards to automate, improve data quality, and govern data processing. This course provides an overview of DataOps and how to leverage best practices to continuously improve data processing automation and quality.
This course introduces the key concepts and best practices to gather critical requirements about data & AI products upfront, determine appropriate quality based on usage patterns, and ensure continuous value to end users or systems over time.
This course covers how to align and deliver analytics and A.I. solutions across the portfolio, program, and project levels. Take this course to learn the best ways to do work break down, capacity planning, and Agile for analytics efforts.
This course helps teams understand and apply Agile best practices to increase the alignment, impact and velocity of analytic solutions. Students learn the pros & cons of Agile, along with the mechanics, work breakdown and team roles involved to successfully manage analytics projects at scale.
This course covers why an organization may benefit from data governance, how it is implemented, and why it is essential in compliance and risk reduction efforts. Students learn how to define and implement operational governance and compliance standards for AI Models throughout an organization.
This course teaches the foundational data stewardship, quality and governance capabilities needed to enable mass adoption of data, analytics, and AI throughout the organization. Students learn how to build in context, structure, and quality from data capture through curation.
This course teaches the methods for driving decisions and communicating insights to different audiences in an organization. Go beyond typical reporting when identifying an insight, designing how it’s displayed, building context and conveying benefits/risks to stakeholders.
This course teaches the foundations for data access, data quality, visualization and deployment. Students learn to apply consistent processes for accessing and analyzing data sets in order to extract meaningful information from them. They will also be taught basic elements of design, and to use visualization tools to graphically display potentially complex relationships in a comprehensible way.
This course introduces the concepts and terminology of AI and analytics. Overview of the benefits of tying analytics to clear business objectives, prioritization of analytics initiatives, intentional user-centric design when creating analytics products, and an overview of the analytics life cycle along with roles and responsibilities.
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