From Data to Smart Decision-Making and Predictive Insights Powered by AI

• Course Description:

• This program serves as a practical bridge connecting traditional data analytics with advanced Artificial Intelligence and Machine Learning techniques. 
• Designed specifically for business analysts, managers, and decision-makers, this course empowers you to transform complex, unstructured data into actionable strategies for marketing, finance, supply chain, and customer relationship management. You will gain hands-on experience building predictive models, segmenting target audiences, analyzing text and social networks, leveraging Automated Machine Learning (AutoML), and practicing responsible AI-driven analytics
•  

• Course Main Points:

• Foundations of Business Analytics & Machine Learning:
• Understand the end-to-end data modeling lifecycle—from defining objectives to data cleaning, preprocessing, and deployment
• Data Visualization & Dimensionality Reduction:
• Explore multidimensional data structures and reduce feature dimensions using techniques like Principal Component Analysis (PCA)
• Supervised Predictive Modeling:
• – Master prediction and classification using Multiple Linear & Logistic Regression.  
• – Implement rule-based algorithms (Decision Trees, Naive Bayes, k-NN)
• – Harness Neural Networks and Deep Learning concepts for complex decision-making
• Unsupervised Learning & Customer Segmentation:
• – Apply Cluster Analysis (K-means & Hierarchical) to identify distinct customer profiles and market segments.  
• – Discover purchase patterns with Association Rules and Market Basket Analysis
• Time Series & Trend Forecasting:
• Predict future sales and operational metrics using regression and smoothing-based forecasting methods
• Text Mining & Social Network Analytics:
• Extract unstructured data from digital channels, perform text processing, and analyze social network graphs
• Interventions, AutoML & AI Optimization:
• Automate pipeline selection with AutoML, evaluate interventions through A/B Testing, and optimize targeting using Uplift Modeling
• Responsible Data Science (RDS):
• Ensure fairness, audit model bias, and implement ethical governance frameworks in AI deployment
•  

• Course Duration:

• 40 Hrs