Course Modules

Module 1: Telecom Big Data Landscape and Digital Transformation

• Evolution of telecom data ecosystems
• Big Data challenges in telecommunications
• Network-generated data
• Customer-generated data
• IoT and connected device data
• Opportunities from telecom analytics
• Data-driven telecom business models
• Case Study & Practical Application: Assessing the Big Data maturity of a telecommunications organization.

Module 2: Telecom Data Architecture and Engineering Foundations

• Telecom data architectures
• Data lakes and data warehouses
• ETL and ELT processes
• Data pipelines
• Data integration strategies
• Cloud-based analytics environments
• Case Study & Practical Application: Designing a scalable telecom data architecture.

Module 3: Real-Time Data Processing and Streaming Analytics

• Real-time telecom analytics requirements
• Streaming data architectures
• Event-driven analytics
• Network monitoring analytics
• Real-time customer intelligence
• Operational dashboards
• Case Study & Practical Application: Developing a real-time network monitoring analytics framework.

Module 4: Data Governance, Quality and Security Management

• Telecom data governance frameworks
• Data quality management
• Master data management
• Metadata management
• Data privacy regulations
• Secure data handling
• Case Study & Practical Application: Developing a telecom data governance framework.

Module 5: Advanced Business Intelligence and Executive Decision Support

• Business Intelligence (BI) frameworks for telecommunications
• Enterprise reporting and decision-support systems
• Telecom Key Performance Indicators (KPIs)
• Operational dashboards for Network Operations Centres (NOCs)
• Executive scorecards and performance management
• Interactive data visualization and storytelling
• Self-service analytics and business intelligence platforms
• Case Study & Practical Application: Designing an executive telecom business intelligence dashboard integrating network performance, customer experience, financial performance, and operational metrics for strategic decision-making.

Module 6: Network Analytics and Operational Intelligence

• Radio Access Network (RAN) analytics
• Core network performance analytics
• Traffic engineering and congestion analysis
• Service Quality (QoS) and Quality of Experience (QoE) analytics
• Capacity planning and forecasting
• Root cause analysis using network analytics
• Intelligent performance optimization
• Case Study & Practical Application: Analyzing multi-source network data to identify performance bottlenecks and develop an optimization strategy for a nationwide mobile operator.

Module 7: Customer Intelligence, Revenue Analytics and Market Insights

• Customer analytics frameworks
• Subscriber behaviour modelling
• Customer segmentation techniques
• Customer churn prediction
• Customer Lifetime Value (CLV) analysis
• Revenue assurance analytics
• Product and service adoption analytics
• Personalized marketing through analytics
• Case Study & Practical Application: Developing a customer intelligence framework to reduce subscriber churn and improve customer retention through predictive analytics.

Module 8: Artificial Intelligence and Predictive Analytics for Telecommunications

• AI-driven telecom analytics
• Predictive modelling techniques
• Demand forecasting
• Revenue forecasting
• Network fault prediction
• AI-enabled decision support
• Prescriptive analytics for telecom operations
• Intelligent forecasting models
• Case Study & Practical Application: Developing predictive analytics models for network traffic forecasting and service demand planning.

Module 9: Data Monetization, Digital Innovation and Emerging Technologies

• Data monetization strategies
• Telecom data marketplaces
• AI-driven digital services
• Edge analytics
• Cloud-native analytics platforms
• Digital twins and simulation
• Data fabric architecture
• Future analytics ecosystems supporting 6G
• Case Study & Practical Application: Designing a data monetization strategy that transforms telecom data assets into new digital revenue streams while maintaining regulatory compliance.

Module 10: Enterprise Data Strategy, Governance and Future Telecom Analytics

• Enterprise analytics strategy development
• Data governance maturity assessment
• Responsible AI and analytics ethics
• Cybersecurity considerations in analytics
• Digital transformation roadmaps
• Future of AI-enabled telecom organizations
• Building a data-driven culture
• Change management for enterprise analytics adoption
• Case Study & Practical Application: Developing a comprehensive enterprise data and analytics strategy aligned with business objectives, AI adoption, digital transformation, and future telecommunications technologies.