Business Analytics & Predictive Modeling

This course is designed for business professionals, data analysts, and managers who wish to gain a deep understanding of business analytics and predictive modeling. It is ideal for individuals in fields such as marketing, finance, operations, and strategy who are responsible for decision-making and performance optimization. The course is also suitable for professionals seeking to enhance their analytical skills to drive business insights and improve their organization’s forecasting and strategic planning. A basic understanding of data analysis and statistics would be beneficial, but not required.

Day 1: Introduction to Business Analytics and Data Analysis

• What is business analytics?
• Types of analytics: Descriptive, diagnostic, predictive, and prescriptive
• The role of business analytics in decision-making
• Data types and data sources for analytics
• Data cleaning and preparation: Handling missing data and outliers
• Introduction to analytical tools (Excel, R, Python)
• Practical exercise: Preparing a dataset for analysis
• Case study: How businesses use analytics to gain competitive advantage

Day 2: Understanding Predictive Modeling

• Introduction to predictive modeling and its applications
• Key concepts in predictive modeling: Variables, features, and outcomes
• Overview of common predictive modeling techniques
• Simple linear regression and multiple regression models
• Evaluation metrics for predictive models: R-squared, RMSE, MAE
• Practical exercise: Building a basic regression model using sample data
• Case study: Predicting sales trends using regression analysis

Day 3: Advanced Predictive Modeling Techniques

• Decision trees and classification models
• Support vector machines (SVM) and k-nearest neighbors (KNN)
• Random forests and ensemble methods
• Time series forecasting for business trends
• Model selection: Choosing the right model for the business problem
• Practical exercise: Implementing a decision tree and classification model
• Case study: Predicting customer churn using decision trees

Day 4: Data Visualization and Communicating Insights

• The importance of data visualization in business analytics
• Types of data visualizations: Charts, graphs, and dashboards
• Using visualization tools (Excel, Tableau, Power BI) to present results
• Communicating insights to stakeholders: Translating complex data into actionable decisions
• Creating dashboards for monitoring key business metrics
• Practical exercise: Building a dashboard to visualize business data
• Case study: How data visualization led to actionable business decisions

Day 5: Applying Predictive Modeling to Real-World Business Problems

• Customer segmentation and targeting using clustering techniques
• Forecasting demand, sales, and financial performance
• Predictive analytics for risk management and fraud detection
• Evaluating and validating the predictive model performance
• Ethical considerations in data analysis and predictive modeling
• Practical exercise: Building a predictive model for sales forecasting
• Final assessment: Analyzing a business scenario and presenting the model results