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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-collection-frameworks-for-data-professionals/
课程评论：没有评论
课程名称：数据专业人士的数据收集框架 课程概述：本课程为希望深入了解和掌握先进数据分析及基础设施框架的专业人士提供全面指导。课程涵盖从网页和移动分析到实时数据流和可观测性等多个主题，为参与者设计和实施稳健的数据解决方案奠定了坚实基础。 课程结构： 模块 1：数据分析基础 - 介绍CRISP-DM和TDSP框架 - 理解数据生命周期和关键数据处理阶段 - 实际应用与案例研究 模块 2：网页和移动分析 - 深入探讨Google Analytics和Adobe Analytics - 用户行为跟踪和转化分析的实操练习 - 实施网页和移动应用的分析策略 模块 3：用户参与分析 - 利用Mixpanel和Amplitude进行用户参与分析 - A/B测试和群体分析技术 - 制定以数据为驱动的用户留存策略 模块 4：集中日志记录与监控 - 使用ELK Stack实施集中日志记录 - 使用Splunk进行实时日志分析 - 构建自定义仪表盘以实现有效监控 模块 5：实时数据流框架 - Apache Kafka在构建数据管道中的作用 - Apache Flink在流处理中的实际应用 - 设计可扩展且具有容错能力的流处理架构 模块 6：基于云的数据收集 - AWS Kinesis和Google Cloud Pub/Sub用于云基础的数据流 - 云数据解决方案中的可扩展性考虑 - 与其他云服务的集成以支持端到端数据处理 模块 7：可观测性框架 - 介绍Prometheus用于监控和报警 - 使用Grafana创建交互式仪表盘 - 实现全面系统可观测性的最佳实践 通过本课程，参与者将获得必要的知识和技能，以应用最新的数据分析技术并提升其在数据领域的职业能力。
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 AnalyticsOverview of CRISP-DM and TDSP frameworksUnderstanding the data lifecycle and key data processing stagesPractical applications and case studiesModule 2: Web and Mobile AnalyticsIn-depth exploration of Google Analytics and Adobe AnalyticsHands-on exercises for user behavior tracking and conversion analysisImplementing analytics strategies for web and mobile applicationsModule 3: User Engagement AnalyticsUtilizing Mixpanel and Amplitude for user engagement analysisA/B testing and cohort analysis techniquesDeveloping data-driven strategies for user retentionModule 4: Centralized Logging and MonitoringImplementation of ELK Stack for centralized loggingReal-time log analysis using SplunkBuilding custom dashboards for effective monitoringModule 5: Real-Time Data Streaming FrameworksApache Kafka and its role in building data pipelinesReal-world applications of Apache Flink in stream processingDesigning scalable and fault-tolerant streaming architecturesModule 6: Cloud-Based Data CollectionAWS Kinesis and Google Cloud Pub/Sub for cloud-based data streamingScalability considerations in cloud-based data solutionsIntegration with other cloud services for end-to-end data processingModule 7: Observability FrameworksIntroduction to Prometheus for monitoring and alertingCreating interactive dashboards with GrafanaBest practices for achieving comprehensive system observability
