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Casey Ayers

Two things are true in project leadership: no plan is ever perfect, and all projects come to an end. In this course, you’ll learn how to guide your team through inevitable change, maximize the value your team offers, and close out work effectively.

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Two things are true in project leadership: no plan is ever perfect, and all projects come to an end. In this course, you’ll learn how to guide your team through inevitable change, maximize the value your team offers, and close out work effectively.

Two things are true in project leadership: no plan is ever perfect, and all projects come to an end. In this course, you’ll learn how to guide your team through inevitable change, maximize the value your team offers, and close out work effectively. First, you’ll learn why change control is essential to project success, and how to manage changes effectively. Then, you’ll learn how to evaluate the impact of changes on your project’s progress and how to continue refining the value your team can offer. After that, you’ll learn how to validate project work, ensuring your team has been successful in achieving its goals. Finally, you’ll learn how to conclude work on projects, and capture lessons that can help you and your team succeed in future endeavors. By the end of this course, you’ll understand how to manage project changes and closure effectively.

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What's inside

Syllabus

Course Overview
Managing Changes to Project Plans
Evaluating Project Changes
Validating Project Work
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Closing Out and Looking Back

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Helps students manage changes and closure in project leadership
Useful for project leaders as it delves into project management topics, such as change control and value evaluation
Provides practical guidance on validating project work and concluding work on projects
Taught by industry experts Casey Ayers
Helps students develop essential project management skills

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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 Designing and Implementing Solutions Using Google Machine Learning APIs with these activities:
Review Core Project Management Concepts
Strengthen foundational understanding of project management principles before diving into course content.
Browse courses on Project Planning
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  • Revisit textbooks, online resources, or previous course materials to refresh your memory on core concepts.
  • Focus on fundamental topics such as project scope, timelines, and stakeholder management.
  • Consider taking practice quizzes or assessments to test your understanding.
  • Identify areas where you need additional reinforcement and seek out supplementary resources.
Review the basics of project management
Refresh your understanding of the fundamental principles of project management to enhance your grasp of the course material.
Browse courses on Project Management
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  • Read through notes or textbooks on project management
  • Complete practice questions or exercises
Sharpen Communication and Collaboration Skills
Enhance your ability to effectively communicate and collaborate with team members and stakeholders.
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  • Identify areas where you need to improve your communication skills, such as active listening or public speaking.
  • Practice active listening techniques and provide constructive feedback to others.
  • Engage in role-playing exercises to simulate project team interactions and stakeholder meetings.
  • Seek opportunities to lead or participate in team projects that require collaboration and communication.
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Organize and review course materials
Consolidate and review course materials, including notes, assignments, quizzes, and exams, to reinforce your understanding and enhance retention.
Show steps
  • Gather and organize materials from different sources
  • Review materials regularly
  • Create summaries or outlines of key concepts
Attend Project Management Industry Meetups
Connect with professionals in the field and gain insights from their experiences.
Show steps
  • Identify and attend industry meetups or conferences focused on project management.
  • Network with other project managers, share knowledge, and learn from their best practices.
  • Explore potential collaborations or opportunities for professional growth.
Join a study group or discussion forum
Engage with peers in a study group or discussion forum to exchange knowledge, collaborate on projects, and reinforce your understanding of course concepts.
Show steps
  • Join an existing study group or create your own
  • Participate in regular group meetings or online discussions
  • Collaborate on assignments or projects
Explore Case Study Examples
Supplement course materials with real-world examples of project management challenges and solutions.
Browse courses on Project Management
Show steps
  • Identify reputable sources for case studies, such as industry publications or research journals.
  • Select case studies relevant to the course topics, such as Change Control or Project Closure.
  • Read and analyze the case studies, focusing on the project management principles and techniques used.
  • Summarize the key takeaways from the case studies and reflect on how they apply to your own project management experience.
Curate a Collection of Project Management Tools
Expand your toolkit by compiling a list of useful project management tools and resources.
Show steps
  • Research and identify a range of project management tools, including software, apps, and templates.
  • Categorize and organize the tools based on their functionality and purpose.
  • Create a curated document or online repository where you can share the collection with others.
  • Update and maintain the compilation as you discover new and valuable tools.
Explore online tutorials on project management tools
Enhance your skills by exploring online tutorials that provide practical demonstrations and guidance on project management software and techniques.
Show steps
  • Identify relevant tutorials based on your learning goals
  • Follow tutorials to learn specific tools or techniques
  • Apply your learnings to course projects or assignments
Simulate Project Change Scenarios
Develop practical skills in managing project changes and mitigating risks.
Browse courses on Change Management
Show steps
  • Create hypothetical project scenarios involving unexpected changes or challenges.
  • Apply the principles and techniques learned in the course to analyze and respond to the scenarios.
  • Discuss and evaluate different change management strategies and their potential impact on project outcomes.
  • Repeat the simulation with different scenarios to enhance your understanding and decision-making.
Develop a Project Change Management Plan
Apply course concepts to create a comprehensive plan for managing project changes effectively.
Show steps
  • Identify potential sources of change and establish a process for reviewing and approving change requests.
  • Define clear roles and responsibilities for stakeholders involved in change management.
  • Develop a communication strategy to ensure timely and effective communication of changes to all affected parties.
  • Outline a process for tracking, monitoring, and evaluating the impact of changes on project objectives.
  • Create a detailed document outlining the project change management plan and share it with stakeholders for review and feedback.
Develop a project plan for a real-world scenario
Apply your knowledge by creating a comprehensive project plan based on a real-world scenario, demonstrating your understanding of project management principles and practices.
Show steps
  • Define the project scope and objectives
  • Identify project stakeholders and their roles
  • Develop a detailed work breakdown structure
  • Estimate project costs, timelines, and resources
  • Create a risk management plan
Develop a Project Management Blog or Vlog
Share your knowledge and insights on project management to engage with a wider audience.
Browse courses on Case Studies
Show steps
  • Choose a platform for your blog or vlog, such as a website, YouTube, or LinkedIn.
  • Develop a content calendar and plan topics that align with the course material and industry best practices.
  • Research and write (or record) informative and engaging content that provides value to your audience.
  • Promote your content through social media and other channels to attract followers and subscribers.
  • Interact with your audience, respond to comments, and continue to create valuable content that meets their needs.

Career center

Learners who complete Designing and Implementing Solutions Using Google Machine Learning APIs will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
A Machine Learning Engineer is responsible for designing and implementing machine learning solutions to solve complex business problems. This course provides a strong foundation in the Google Machine Learning APIs, which are essential tools for building and deploying machine learning models. The course covers topics such as model selection, data preparation, model training, and model evaluation. This knowledge will help Machine Learning Engineers to develop and deploy machine learning solutions that are accurate, efficient, and scalable.
Data Scientist
A Data Scientist is responsible for collecting, cleaning, and analyzing data to extract insights that can be used to improve business decisions. This course provides a strong foundation in the Google Machine Learning APIs, which can be used to automate many of the tasks involved in data science. The course covers topics such as data ingestion, data transformation, data visualization, and machine learning model building. This knowledge will help Data Scientists to work more efficiently and effectively.
Software Engineer
A Software Engineer is responsible for designing, developing, and maintaining software applications. This course provides a strong foundation in the Google Machine Learning APIs, which can be used to add machine learning capabilities to software applications. The course covers topics such as model integration, API development, and performance optimization. This knowledge will help Software Engineers to develop software applications that are more intelligent and efficient.
Product Manager
A Product Manager is responsible for defining, planning, and launching new products or features. This course provides a strong foundation in the Google Machine Learning APIs, which can be used to create machine learning-powered products and features. The course covers topics such as product strategy, user experience design, and data analysis. This knowledge will help Product Managers to create products and features that are innovative, user-friendly, and effective.
Business Analyst
A Business Analyst is responsible for understanding business needs and translating them into technical requirements. This course provides a strong foundation in the Google Machine Learning APIs, which can be used to solve a wide range of business problems. The course covers topics such as business process analysis, data modeling, and machine learning model selection. This knowledge will help Business Analysts to work more effectively with technical teams to develop solutions that meet business needs.
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data to extract insights that can be used to improve business decisions. This course provides a strong foundation in the Google Machine Learning APIs, which can be used to automate many of the tasks involved in data analysis. The course covers topics such as data ingestion, data transformation, data visualization, and machine learning model building. This knowledge will help Data Analysts to work more efficiently and effectively.
Quantitative Analyst
A Quantitative Analyst is responsible for developing and using mathematical and statistical models to solve business problems. This course provides a strong foundation in the Google Machine Learning APIs, which can be used to develop and deploy machine learning models. The course covers topics such as model selection, data preparation, model training, and model evaluation. This knowledge will help Quantitative Analysts to develop and deploy machine learning solutions that are accurate, efficient, and scalable.
Machine Learning Researcher
A Machine Learning Researcher is responsible for developing new machine learning algorithms and techniques. This course provides a strong foundation in the Google Machine Learning APIs, which can be used to implement and test new machine learning algorithms. The course covers topics such as model selection, data preparation, model training, and model evaluation. This knowledge will help Machine Learning Researchers to develop new machine learning algorithms that are accurate, efficient, and scalable.
Data Engineer
A Data Engineer is responsible for designing and building data pipelines that collect, clean, and store data. This course provides a strong foundation in the Google Machine Learning APIs, which can be used to automate many of the tasks involved in data engineering. The course covers topics such as data ingestion, data transformation, data storage, and data security. This knowledge will help Data Engineers to work more efficiently and effectively.
DevOps Engineer
A DevOps Engineer is responsible for bridging the gap between development and operations teams. This course provides a strong foundation in the Google Machine Learning APIs, which can be used to automate many of the tasks involved in DevOps. The course covers topics such as continuous integration, continuous delivery, and infrastructure management. This knowledge will help DevOps Engineers to work more efficiently and effectively.
Cloud Architect
A Cloud Architect is responsible for designing and managing cloud computing solutions. This course provides a strong foundation in the Google Machine Learning APIs, which can be used to deploy machine learning models in the cloud. The course covers topics such as cloud computing fundamentals, machine learning model deployment, and cloud security. This knowledge will help Cloud Architects to design and manage cloud computing solutions that are scalable, secure, and cost-effective.
Data Architect
A Data Architect is responsible for designing and managing data architectures. This course provides a strong foundation in the Google Machine Learning APIs, which can be used to integrate machine learning models into data architectures. The course covers topics such as data modeling, data integration, and data governance. This knowledge will help Data Architects to design and manage data architectures that are scalable, secure, and efficient.
Software Architect
A Software Architect is responsible for designing and managing software architectures. This course provides a strong foundation in the Google Machine Learning APIs, which can be used to integrate machine learning models into software architectures. The course covers topics such as software design principles, machine learning model integration, and software testing. This knowledge will help Software Architects to design and manage software architectures that are scalable, secure, and efficient.
System Administrator
A System Administrator is responsible for managing and maintaining computer systems and networks. This course provides a strong foundation in the Google Machine Learning APIs, which can be used to automate many of the tasks involved in system administration. The course covers topics such as system monitoring, performance tuning, and security management. This knowledge will help System Administrators to work more efficiently and effectively.
Network Administrator
A Network Administrator is responsible for managing and maintaining computer networks. This course provides a strong foundation in the Google Machine Learning APIs, which can be used to automate many of the tasks involved in network administration. The course covers topics such as network monitoring, performance tuning, and security management. This knowledge will help Network Administrators to work more efficiently and effectively.

Reading list

We've selected 12 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 Designing and Implementing Solutions Using Google Machine Learning APIs.
Contrasts various project management methodologies and provides guidance on choosing the right one for different projects
Dives deeper into Kanban, one of the topics in the course.
Is designed for individuals who are not project managers but need to manage projects.

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