Introduction

Artificial Intelligence (AI) and machine learning (ML) are transforming the way businesses leverage data. By incorporating AI-driven predictive models into Business Intelligence (BI), organizations can uncover hidden patterns, make precise forecasts, and optimize decision-making. This course focuses on equipping participants with the skills needed to build, evaluate, and deploy AI-powered predictive models that enhance BI capabilities, enabling smarter and more proactive business strategies.

Objectives

By the end of this course, participants will be able to:

• Understand the fundamental concepts of AI and machine learning in the context of Business Intelligence.
• Apply supervised and unsupervised learning techniques to build predictive models for business problems.
• Implement regression, classification, and clustering models to solve BI challenges.
• Use time series forecasting techniques to predict future trends.
• Integrate AI-driven models with existing BI tools (e.g., Tableau, Power BI) for seamless reporting and decision-making.
• Evaluate and fine-tune predictive models for improved accuracy and performance.
• Apply deep learning and ensemble methods for more complex predictive modeling tasks.

Who Should Attend?

This course is ideal for:

• Data scientists and BI professionals looking to incorporate AI into their workflows
• Business analysts and data analysts interested in predictive analytics
• IT professionals supporting AI-powered BI solutions
• Business leaders and decision-makers who want to understand the impact of AI in BI
• Anyone interested in advancing their skills in predictive modeling and AI for business applications

Course Outline

Day 1: Introduction to AI and Predictive Analytics in BI

• Overview of AI and Machine Learning: Definitions, applications, and business relevance
• Types of AI Models: Supervised learning, unsupervised learning, and reinforcement learning
• The Role of Predictive Analytics in Business Intelligence: How predictive models enhance BI tools
• Business Use Cases for AI-Driven Predictive Models: Forecasting sales, customer churn prediction, demand forecasting
• Data Preprocessing for AI Models: Feature engineering, data cleaning, and transformation
• Exploratory Data Analysis (EDA): Understanding data characteristics before applying predictive models
• Case Study: How AI-Driven Predictive Models Improve Demand Forecasting
• Hands-on Session: Setting up a Predictive Analytics Environment Using Python/R

Day 2: Supervised Learning for Predictive Models

• Regression Analysis: Using linear and logistic regression for predictive modeling
• Classification Models: Applying decision trees, random forests, and support vector machines (SVM) for classification tasks
• Model Evaluation: Performance metrics for regression (MSE, RMSE) and classification (accuracy, precision, recall, F1 score)
• Hyperparameter Tuning: Optimizing models for better performance (grid search, cross-validation)
• Feature Selection and Engineering: Identifying the most relevant features for model performance
• Use Cases in BI: Predicting customer churn, sales forecasting, fraud detection
• Case Study: Building a Classification Model to Predict Customer Churn
• Hands-on Session: Building a Predictive Regression Model Using Random Forest in Python

Day 3: Unsupervised Learning and Clustering in BI

• Introduction to Unsupervised Learning: Key differences between supervised and unsupervised learning
• Clustering Algorithms: K-means, hierarchical clustering, DBSCAN for customer segmentation, market analysis
• Dimensionality Reduction: Using PCA (Principal Component Analysis) to simplify complex data
• Association Rule Learning: Finding patterns in large datasets (e.g., market basket analysis)
• Business Applications: Customer segmentation, anomaly detection, recommendation systems
• Model Evaluation: Assessing clustering models using silhouette scores and inertia
• Case Study: Using K-means Clustering for Customer Segmentation in Retail
• Hands-on Session: Applying PCA and K-means for Data Clustering Using Python

Day 4: Time Series Forecasting and AI-Driven Predictions

• Time Series Analysis for Forecasting: Key components (trend, seasonality, noise) and forecasting models
• ARIMA and SARIMA Models: Advanced techniques for time series forecasting
• Deep Learning for Time Series: Using LSTM (Long Short-Term Memory) networks for sequential data
• Predicting Trends: Sales forecasting, stock price predictions, demand forecasting
• Evaluating Time Series Models: Performance metrics (MAPE, MAE, RMSE) for time series forecasting
• Incorporating Time Series Predictions into BI Dashboards: Using forecasting models in Power BI and Tableau
• Case Study: Predicting Monthly Sales Using ARIMA and Deep Learning Models
• Hands-on Session: Building a Time Series Forecast Using ARIMA and LSTM in Python

Day 5: Model Deployment, Fine-Tuning, and Real-World BI Applications

• Model Deployment: How to integrate predictive models into BI systems and business workflows
• Monitoring Model Performance: Tracking and improving model accuracy over time
• Ensemble Methods: Combining multiple models (e.g., boosting, bagging) for improved prediction accuracy
• AI in BI Dashboards: Integrating AI-powered models into Power BI, Tableau, or other BI tools for actionable insights
• Business Applications: Predictive maintenance, fraud detection, dynamic pricing, personalized marketing
• Ethical Considerations in AI for BI: Ensuring transparency, fairness, and privacy in predictive models
• Case Study: Implementing Predictive Models for Dynamic Pricing in E-commerce
• Final Project: Building and Deploying a Predictive Model in a BI Tool for Business Decision-Making