Business Intelligence (BI) and Predictive Analytics 101

Overview

Course Description:

Business Intelligence and Predictive Analytics 101

Syllabus

• 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

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Taught by

Dr. José Prabhu J

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Subjects

Business