Data Analytics for Effective Managerial Decision-Making

Introduction:

This Data Analytics for Effective Managerial Decision-Making course is designed for corporate executives, emphasizing the value of data analytics as a tool to support decision-making in management. It will demonstrate how data analytics can be leveraged to strengthen strategic initiatives, guide policy formulation, and influence operational choices.

The course highlights the practicality of data analytics in management, validating analytic outcomes, and explaining the contribution of quantitative reasoning to decision-making. By engaging with data analytics, participants will gain greater confidence in their ability to make evidence-based decisions.

Defining Managerial Decision-Making:

The managerial decision-making process empowers managers to identify problems and make informed choices. This involves defining the problem, identifying alternatives, evaluating these choices, and selecting one for implementation. Each step of this process is illuminated through the effective use of data analytics.

This course helps participants understand how data analytics informs managerial decisions at every stage, enabling them to refine techniques and achieve better results.

Objectives:

Upon completion of this Data Analytics for Effective Managerial Decision-Making course, participants will be able to:

• Recognize the value of using data to support decisions.
• Comprehend the scope and structure of analytical work.
• Apply various functional techniques for effective data analysis.
• Interpret and critically assess statistical evidence.
• Apply data analytics in real-world scenarios.

Training Methodology:

• Group Discussions
• Case Studies
• Interactive Workshops
• Hands-On Data Exercises
• Simulation-Based Learning
• Peer Learning
• Simulated Activities
• Response Sessions

Course Outline:

Unit 1: Setting the Statistical Scene in Management

• Understanding management's quantitative reality
• Application of statistics to management (key performance indicators)
• Integrating data analytics into management practices
• Preparing data for analysis: types, quality, and exploratory techniques (e.g., pivot tables)
• Creating summary tables and visual displays

Unit 2: Evidence-Based Observational Decision-Making

• Profiling numeric sample data using descriptive statistics
• Understanding measures of central tendency and variability
• Analyzing distributions: skewness, bimodal distributions, etc.
• Examining relationships between numerical descriptors
• Performing breakdown analysis for numeric measures

Unit 3: Statistical Decision-Making - Drawing Inferences from Sample Data

• Introduction to inferential statistics for drawing conclusions from sample data
• Interval estimation and its application in estimating population parameters
• Understanding sampling error and its impact on generalization, reliability, and validity

Unit 4: Statistical Decision-Making - Drawing Inferences from Hypothesis Testing

• Fundamentals of hypothesis testing: accepting or rejecting null hypotheses
• Analyzing causes of error terms and their impact on models
• Applying one-sample and two-sample t-tests to evaluate hypotheses
• Testing hypotheses involving paired data

Unit 5: Predictive Decision-Making - Statistical Modeling and Data Mining

• Using statistical models to establish systematic relationships for prediction
• Introduction to regression analysis: building and evaluating regression models
• Overview of data mining and its applications in various fields, including management
• Examples of descriptive and predictive data mining techniques (e.g., clustering, decision tree construction)

Managerial Decision-Making Models:

One of the most critical decisions a leader must make is how to reach a decision. Various models offer different methodologies for analyzing and choosing among alternatives. This course examines different leadership models, their implications for the managerial decision-making process, and how integrating data analytics can influence this process.

Conclusion:

This course is designed as an introductory course in statistical concepts for managers. It emphasizes the importance of learning from failure in business management processes, such as strategic planning and team building.

The Data Analytics for Managerial Decision-Making training program aims to enhance participants' ability to apply their theoretical knowledge and understanding to practical business applications.