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PM Skills®: Analysis and Problem Solving

Self-Paced3 Course Hours | 12 Weeks
View Course
Project Management
PM Skills®: Analysis and Problem Solving


As a project leader, one of your main responsibilities will be to not only collect project data but to also analyze that data to uncover any patterns and trends in project outputs. You will then have to mesh those data analysis skills with creative problem-solving techniques to keep your project on pace and aligned to meet its goals. This self-paced, fully online course will help you develop the expertise you'll need to assess information, define problems, and uncover their root causes. You'll learn how to develop and implement solutions in ways that will resolve issues without creating new risks. And you'll begin to recognize the key role that analysis plays in your decision-making and problem-solving approaches, so you can efficiently guide projects to their successful conclusion.


  • PMI PDUs: 3
    • Leadership PDUs: 1.5
      • Strategic & Business Management PDUs: 0.25
        • Technical PM PDUs: 1.25
          • IACET CEUs: 0.3
            • HRCI Credits: 3
              • SHRM PDCs: 3


                • Mobile-friendly
                • Audio-enabled
                • Badge and credit-awarding
                • Real-world case studies
                • Fully accessible
                • Games & Flashcards
                • Expert-supported
                • Video content

                learning Outcomes

                • Explain why analysis is important for project management
                • Describe the general steps for problem solving
                • Summarize the Davenport-Kim three-stage model for analysis
                • Explain the five stages of results-based management
                • Identify what defines data as valid and reliable
                • Define the three main types of data analytics
                • Identify examples of bias in a data set
                • Explain the purpose of a balanced scorecard
                • Describe the best practices for presenting data

                related courses

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