Data Collection Frameworks for Data Professionals
Description:
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:
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
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
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
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
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
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
Module 7: Observability Frameworks
Introduction to Prometheus for monitoring and alerting
Creating interactive dashboards with Grafana
Best practices for achieving comprehensive system observability