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