Product Management: Data Science and Agile Systems
Master modern product management with Agile, DevOps, and data science. Learn to build flexible systems and use data for risk management.
Master modern product management with Agile, DevOps, and data science. Learn to build flexible systems and use data for risk management.
This course focuses on modern product management techniques incorporating Agile methodologies, DevOps principles, and data science. Students learn to design agile systems that outpace competition, using Lean and DevOps principles to streamline value streams. The curriculum covers the evolution of product management from traditional methods to current business agility practices. It emphasizes the importance of modular, open-set architectures and flexible data management paradigms. The course delves into data science and analytics for risk management, teaching students how to collect, integrate, and analyze data at scale. Participants learn to apply these concepts in product strategy, team alignment, and backlog optimization.
Instructors:
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What you'll learn
Understand the evolution and principles of Agile systems engineering
Apply DevOps principles to enhance business agility and product innovation
Utilize data science techniques for effective product risk management
Implement data-driven controls for technology and team management
Develop strategies for aligning product teams and optimizing product backlogs
Understand and apply lean startup methodologies in product development
Skills you'll gain
This course includes:
6.7 Hours PreRecorded video
26 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This course provides a comprehensive exploration of modern product management, focusing on the integration of Agile methodologies, DevOps principles, and data science. It is structured into five modules, each addressing crucial aspects of contemporary product management. The first module introduces Agile systems engineering, covering the evolution of product management practices from the 1970s to the present day. The second module delves into DevOps principles for business agility, exploring lean startup methodologies and system optimization. The third module focuses on data science for product risk management, covering the data science pipeline, prototyping, and various data analytics techniques. The fourth module addresses data-driven controls, discussing OKRs, product strategies, and team alignment processes. The final module serves as a comprehensive assessment of the learned concepts. Throughout the course, students engage with real-world examples and practical exercises to develop their skills in managing modern, data-driven, and agile product development processes.
Week 1: Agile Systems Engineering
Module 1 · 3 Hours to complete
Week 2: DevOps Principles for Business Agility
Module 2 · 2 Hours to complete
Week 3: Data Science for Product Risk Management
Module 3 · 2 Hours to complete
Week 4: Data-Driven Controls with Technology and Teams
Module 4 · 2 Hours to complete
Final Exam
Module 5 · 53 Minutes to complete
Fee Structure
Payment options
Financial Aid
Instructor
University of Maryland Offers Professional Certificate in Agile Project Management to Enhance Skills in Modern Project Delivery.
The University of Maryland, College Park, offers a variety of courses that focus on Agile methodologies and project management, designed to equip students with essential skills for navigating modern challenges across various fields. Key offerings include "Agile Innovation and Problem Solving Skills," "Agile Leadership Principles and Practices," and "Applied Scrum for Agile Project Management," among others. These courses emphasize practical applications of Agile frameworks, leadership strategies, and stakeholder engagement in project management contexts. Additionally, the curriculum prepares students for roles in both public and private sectors by fostering skills critical for effective teamwork and project execution in dynamic environments. For more information about the faculty and their specific expertise, you can refer to the University of Maryland's academic catalog or their faculty directory.
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