Overview
Business Intelligence, Predictive Analytics, BI, Artificial Intelligence and BI, Big Data Analytics. BI Tools, AI and ML
Syllabus
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- Introduction to Business Intelligence (BI)
-- Definition and Purpose of BI
-- Key Components of BI Systems
-- Role of BI in Organizations
- Introduction to Predictive Analytics
-- Understanding Predictive Analytics
-- Key Techniques and Algorithms
-- Applications in Various Industries
- Data Collection and Preparation
-- Data Sources for BI and Analytics
-- Data Cleaning and Preprocessing
-- Handling Big Data
- Business Intelligence Tools and Technologies
-- Overview of BI Tools (e.g., Tableau, Power BI)
-- Dashboards and Reporting
-- Data Visualization Techniques
- Predictive Modeling Process
-- Defining Business Problems and Objectives
-- Model Selection and Validation
-- Evaluation Metrics
- Statistical Methods for Predictive Analytics
-- Regression Analysis
-- Time Series Forecasting
-- Classification Techniques
- Machine Learning for Predictive Analytics
-- Supervised vs. Unsupervised Learning
-- Key Algorithms (e.g., Decision Trees, Neural Networks)
-- Model Training and Tuning
- Implementing BI and Predictive Analytics Solutions
-- Project Management and Deployment Strategies
-- Aligning Solutions with Business Goals
-- Monitoring and Maintaining BI Systems
- Ethical and Legal Considerations
-- Data Privacy and Security
-- Ethical Use of Predictive Models
-- Compliance and Regulatory Issues
- Case Studies and Real-World Applications
-- Analysis of Real BI and Predictive Projects
-- Success Stories and Challenges
-- Lessons Learned
- Future Trends in Business Intelligence and Analytics
-- Emerging Technologies and Innovations
-- The Role of Artificial Intelligence in BI
-- Industry 4.0 and Smart Analytics
- Conclusion and Course Recap
-- Key Takeaways
-- Additional Resources for Learning
-- Next Steps in BI and Analytics Journey
Taught by
Tags
On-Demand
