Data-Driven Decision Making

Data-Driven Decision Making

Data-Driven Decision Making provides an overview of topics in statistics and their applications in a variety of fields. This course begins with the basics of quantitative analysis and its increasing use in the professional landscape of today. Learners are exposed to quantitative decision making tools and techniques, which tie into real-world case studies. This course utilizes games, videos, interactive exercises, quizzes, and other engaging content.

MindEdge’s Data-Driven Decision Making content enhances skills in applying analytics in decision making, distinguishing good data from bad data, evaluating research techniques to yield the most accurate results, utilizing descriptive statistics in a variety of settings, creating a graphical representation of descriptive statistics, employing forecasting techniques, performing a regression analysis, and making recommendations based on analytics. 

This course uses interactive material to enable student engagement. There are spreadsheet programs for applying and practicing concepts presented throughout the course. When performing calculations, students receive real time feedback on their answers. Frequent checkpoints allow students to measure their comprehension of the material.

 

Module 1: The Case for Quantitative Analysis

  • Explain why quantitative analysis and analytics is important in decision making
  • Explain 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
  • Explain how quality data affects decision making (GIGO principle)
  • Describe methods of ensuring the quality of data
  • Evaluate techniques for ensuring accurate research design
  • Describe how research is used in different settings: business, education, health care, the military, government, nonprofits
  • Explain data management techniques including transforming data, recoding data, and handling missing data
  • Apply appropriate decision making techniques to a specific case

Module 2: Statistics as a Managerial Tool

  • Describe how statistics are used in different settings
  • Describe common problems with and misuse of statistics
  • Identify criteria for evaluating statistics
  • Explain the key fundamentals of probability and their real-world application
  • Identify the fundamental concepts of descriptive statistics (populations and samples, measures of central tendency, measures of variability, measures of distribution) and their real-world application
  • Select appropriate graphic methods for displaying descriptive statistics
  • Explain the fundamental concepts of inferential statistics and their real-world application
  • Evaluate a scenario in order to determine the appropriate statistic to use
  • Apply fundamental statistics to a real-world situation
  • Evaluate the appropriateness of statistics used
  • Use statistics to identify the most appropriate decision alternative
  • Translate statistical data into a graphical presentation based on a brief case study

Module 3: Quantitative Decision Tools

  • Evaluate the usefulness of different statistical techniques and their real-world application
  • Describe the various forecasting techniques and the benefits and limitations
  • Describe the various types of regression analysis and their real-world application
  • Analyze the results of a regression analysis
  • Describe common problems with multiple regression
  • Describe other statistical techniques and their real-world application
  • Explain the advantages and disadvantages of various statistical techniques
  • Choose a statistical technique based on a brief case study

Module 4: Quality Management Basics (Statistical Process Control)

  • Describe principles that help guide quality management activities
  • Use the Plan-Do-Check-Act cycle to coordinate work and implement change
  • Explain the differences between quality control and quality assurance
  • Create a SIPOC diagram to help visualize work as a process
  • Explain the role that metrics and statistics play in measuring and controlling work processes
  • Apply analysis and planning approaches to quality
  • Explain how the Seven Basic Quality Tools are used to monitor and control quality processes
  • Use the Seven Basic Quality Tools to process and sort non-numerical data
  • Use the Seven Basic Quality Tools in combination to create powerful plans and solutions to quality problems
  • Describe various quality management programs
  • Employ quality management tools based on a brief case study

Module 5: Real World Data-Driven Decisions

  • Explain the management implications of the use of business intelligence and knowledge management systems
  • Define Big Data and describe its current uses for analysis and future potential and its implications
  • Explain common analytics for business and quality improvement
  • Recommend manufacturing business decisions based on data analytics
  • Explain common analytics used in health care
  • Recommend health care decisions based on data analytics
  • Explain common analytics used in education
  • Recommend educational decisions based in data analytics
  • Explain common analytics used in government
  • Recommend governmental decisions based on data analytics

Module 6: Improving Organizational Performance (Performance Measures)

  • Explain how performance measures are used in different settings
  • Differentiate among various organizational performance measurements
  • Describe the advantages and disadvantages of KPIs
  • Describe the advantages and disadvantages of the Balanced Scorecard
  • Describe the advantages and disadvantages of a Net Promoter Score
  • Explain the relationship between performance assessment and organizational tactics and strategy
  • Assess the validity of performance measures for an organization based on a brief case study

Module 7: Performance Assessment

  • Capstone case studies