Business Decision-Making through Data Analysis

Introduction:

The interactive Data Analysis in Business Decision-Making course is a highly application-based program delivered over five days, focusing on the importance of data analytics as a tool for making informed management decisions. This course will demonstrate how data analytics can enhance strategic initiatives, assist in policy formulation, and guide operational decision-making.

Throughout the program, participants will learn to apply analytics practically in management, understand the critical importance of accurately interpreting findings, and incorporate quantitative reasoning into decision-making processes. Exposure to analytics will ultimately boost participants' confidence in using evidence-based information to support managerial decisions.

The Importance of Business Decision-Making through Data Analysis:

Understanding data analytics is essential for making effective business decisions in today's data-driven world. Integrating data analysis into decision-making processes enables leaders to develop sound strategies. This course will teach participants how to frame business decisions using data analytics, thereby enhancing their ability to apply these techniques and improve overall decision-making outcomes.

Objectives:

By the end of this Business Decision-Making through Data Analysis course, participants will be able to:

• Recognize the value of data analytics as a decision support tool.
• Understand the scope and structure of data analytics.
• Apply a range of practical data analytics techniques.
• Interpret and critically evaluate statistical evidence.
• Identify and implement relevant applications of data analytics in real-world scenarios.

Training Methodology:

• Group Discussions
• Case Studies
• Interactive Workshops
• Data Exercises
• Scenario-Based Learning
• Peer Instructional Activities
• Simulation Activities
• Feedback Sessions
• Self-Assessment Activities
• Action Planning Sessions

Course Outline:

Unit 1: Setting the Statistical Scene in Management

• The Quantitative Landscape
• Applications (identifying KPIs)
• Thinking Statistically
• Data Integration Elements
• Data: Types, Quality, and Preparation
• Exploratory Data Analysis using Excel (pivot tables)
• Summary Tables and Visual Displays for Sample Data Profiling

Unit 2: Evidence-Based Observational Decision-Making

• Numeric Descriptors for Profiling Numeric Sample Data
• Central and Non-Central Location Measures
• Quantifying Dispersion in Sample Data
• Exploring Numeric Measures Distribution (skewness and bimodal)
• Interrelationships Between Numeric Descriptors
• Breakdown Analysis of Numeric Measures

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

• The Basis for Statistical Inference
• Uncertainty Quantification in Data – Normal Probability Distribution
• The Significance of Sampling in Inferential Statistics
• Sampling Methods (random-based sampling techniques)
• Understanding Sampling Distribution
• Confidence Interval Estimation

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

• The Logic Behind Hypothesis Testing
• Hypothesis Testing Procedure and Types of Errors
• Single-Population Tests (tests for a single mean)
• Two-Independent Population Tests of Means
• Paired Samples Testing Situations
• Mean Comparison Across Multiple Populations

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

• Using Statistical Relationships to Build Predictive Models
• Model Building Using Regression Analysis
• The Rationale for Model Building and Evaluation of Regression Models
• Overview of Data Mining – Its Evolution
• Descriptive Data Mining Applications in Management