Data Collection Frameworks for Data Professionals

This comprehensive course is designed for professionals seeking advanced knowledge and practical skills in leveraging cutting-edge data analytics and infrastructure frameworks. The course covers a range of topics, from web and mobile analytics to real-time data streaming and observability, providing participants with a solid foundation for designing and implementing robust data solutions.

Course Structure:

1. Module 1: Foundations of Data Analytics
   • Overview of CRISP-DM and TDSP frameworks
   • Understanding the data lifecycle and key data processing stages
   • Practical applications and case studies
2. Module 2: Web and Mobile Analytics
   • In-depth exploration of Google Analytics and Adobe Analytics
   • Hands-on exercises for user behavior tracking and conversion analysis
   • Implementing analytics strategies for web and mobile applications
3. Module 3: User Engagement Analytics
   • Utilizing Mixpanel and Amplitude for user engagement analysis
   • A/B testing and cohort analysis techniques
   • Developing data-driven strategies for user retention
4. Module 4: Centralized Logging and Monitoring
   • Implementation of ELK Stack for centralized logging
   • Real-time log analysis using Splunk
   • Building custom dashboards for effective monitoring
5. Module 5: Real-Time Data Streaming Frameworks
   • Apache Kafka and its role in building data pipelines
   • Real-world applications of Apache Flink in stream processing
   • Designing scalable and fault-tolerant streaming architectures
6. Module 6: Cloud-Based Data Collection
   • AWS Kinesis and Google Cloud Pub/Sub for cloud-based data streaming
   • Scalability considerations in cloud-based data solutions
   • Integration with other cloud services for end-to-end data processing
7. Module 7: Observability Frameworks
   • Introduction to Prometheus for monitoring and alerting
   • Creating interactive dashboards with Grafana
   • Best practices for achieving comprehensive system observability