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Introduction to Data Analysis

$79
Self-Paced5 Course Hours | 12 Weeks
View Course
Data Analytics
Introduction to Data Analysis

Description

Whatever your profession. Whatever your field. As a professional, and certainly as a leader, you will be asked to make a decision based on data. This course will introduce the different types of decisions made in an organizational setting, why quantitative analytics is important, and how data quality can affect decision making. Since quantitative analytics is used in various settings, this introductory-level course also offers insight into how research is used in different sectors. From a management perspective, the course highlights appropriate quantitative methods and ways to ensure quality and accuracy through research design.

credits

  • PMI PDUs: 5
    • Leadership PDUs: 2
      • Strategic & Business Management PDUs: 1
        • Technical PM PDUs: 2
          • IACET CEUs: 0.5
            • HRCI Credits: 5
              • SHRM PDCs: 5

                features

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

                learning Outcomes

                • Explain the value of big data and analytics
                • Discuss the types of decisions that can be made analytically in an organizational setting
                • Describe different decision making models and tools
                • Identify the fundamental concepts of measurement including levels of measurement, reliability and validity, errors, measurement and information bias
                • Distinguish between independent and dependent variables
                • Describe methods of ensuring the quality of data
                • Explain data management techniques including transforming data, recoding data, and handling missing data
                • Identify some biases and errors data collection may be subject to
                • Distinguish between correlation and causation
                • Apply appropriate decision making techniques to a specific case

                related courses

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