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所在平台: Udemy |
课程主页: https://www.udemy.com/course/analytics-engineer-interview-guide-500-important-questions/
课程评论：没有评论
课程名称：分析工程师面试指南：500+ 重要问题 课程概述： 您是否在为分析工程师的面试做准备，并寻找一个全面的资源以增强信心并掌握关键概念？本课程“分析工程师面试指南：500+ 重要问题 - 6 个实践考试”是您有效准备的终极解决方案，通过场景问题和概念问题的结合，提供真实世界的实践测试和结构化的主题准备。该课程专为有志于成为分析工程师、数据专业人士和商业智能专家设计，涵盖成功面试所需的所有知识，深入关注理论与实际应用。 课程大纲亮点： 1. **分析工程的介绍** - 理解分析工程师的角色和职责。 - 学习与数据工程师和数据分析师的关键区别，重点关注数据转换和可用性。 2. **数据建模** - 深入理解数据仓库概念，包括维度建模、星型和雪花模式。 - 探索数据保管库和Kimball方法论以及模式设计最佳实践。 3. **数据转换和ELT** - 学习ETL与ELT工作流程的区别。 - 通过实际操作工具，如dbt、Apache Spark和Talend，掌握增量模型、测试策略和转换文档。 4. **SQL与数据查询** - 掌握高级SQL技术，如窗口函数、CTEs和子查询。 - 优化查询性能，有效聚合大数据集。 5. **数据仓库和数据湖** - 了解现代数据仓库平台，如Snowflake、BigQuery和Redshift。 - 学习数据湖与数据仓库的区别及混合集成模型。 6. **数据质量与测试** - 确保数据准确性，通过单元测试和模式验证。 - 获取数据可观察性和使用dbt测试和Great Expectations进行自动化测试的见解。 7. **商业智能(BI)工具** - 探索顶级BI工具，如Looker、Tableau和Power BI。 - 学习如何创建仪表板、启用自助分析并将分析嵌入工作流程中。 8. **分析工程最佳实践** - 应用数据版本控制，使用Git。 - 维护清晰的文档，与利益相关者及其他数据专业人士高效协作。 9. **云平台与基础设施** - 使用基于云的数据仓库工具，如AWS Redshift、Google BigQuery和Azure Synapse。 - 了解无服务器数据处理和云成本优化策略。 10. **数据治理与安全** - 实施基于角色的访问控制 (RBAC)。 - 确保遵守GDPR和CCPA等数据隐私法规，管理元数据并维护数据血缘。 11. **与数据团队的协作** - 有效协调数据工程师和数据科学家。 - 学习将业务需求转化为技术实施。 12. **高级分析和机器学习** - 介绍预测分析，包括回归和分类技术。 - 为机器学习准备数据，确保特征工程和缺失值处理恰当。 - 理解如何将机器学习模型与结构化、转换后的数据集成。 13. **持续集成和部署 (CI/CD)** - 自动化分析工作流的部署管道。 - 使用版本控制和CI/CD工具，如Jenkins和GitHub Actions，实现监控和记录以确保管道性能的可靠性。 您将获得的内容： - 500+ 面试问题和答案，包括概念性和场景性挑战。 - 6个真实的实践考试，涵盖所有核心主题并提供详细解释。 - 主题重点，促进坚实的基础理解和就业准备。 - 实用技术与真实世界的工具、工作流程和最佳实践对齐。 无论您是分析工程新手还是希望提升技能以备采访，这门课程将一步一步引导您走向成功和掌握。
Are you preparing for an Analytics Engineer interview and looking for a comprehensive resource to build your confidence and master key concepts? This course - "Analytics Engineer Interview Guide: 500+ Important Questions - 6 Practice Exams" - is your one-stop solution to prepare effectively through a combination of scenario-based and conceptual questions, real-world practice tests, and structured topic-wise preparation.Designed for aspiring analytics engineers, data professionals, and BI specialists, this course covers everything you need to know to succeed in interviews, with in-depth focus on both theory and practical applications.Course Syllabus Highlights:Introduction to Analytics EngineeringUnderstand the role and responsibilities of an analytics engineer.Learn the key differences from data engineers and data analysts, focusing on data transformation and usability.Data ModelingDive into data warehousing concepts including dimensional modeling, star and snowflake schemas.Explore Data Vault and Kimball methodologies along with schema design best practices.Data Transformation and ELTLearn the differences between ETL and ELT workflows.Get hands-on with tools like dbt, Apache Spark, and Talend.Understand incremental models, testing strategies, and transformation documentation.SQL and Data QueryingMaster advanced SQL techniques such as window functions, CTEs, and subqueries.Optimize query performance and aggregate large datasets effectively.Data Warehousing and Data LakesUnderstand modern data warehouse platforms including Snowflake, BigQuery, and Redshift.Learn the distinction between data lakes and warehouses and explore hybrid integration models.Data Quality and TestingEnsure data accuracy with unit testing and schema validation.Gain insights into data observability and automated testing using dbt tests and Great Expectations.Business Intelligence (BI) ToolsExplore top BI tools like Looker, Tableau, and Power BI.Learn how to create dashboards, enable self-service analytics, and embed analytics into workflows.Analytics Engineering Best PracticesApply data version control using Git.Maintain clear documentation and collaborate efficiently with stakeholders and other data professionals.Cloud Platforms and InfrastructureWork with cloud-based data warehousing tools such as AWS Redshift, Google BigQuery, and Azure Synapse.Understand serverless data processing and strategies for cloud cost optimization.Data Governance and SecurityImplement role-based access control (RBAC).Ensure compliance with data privacy regulations like GDPR and CCPA.Manage metadata and maintain data lineage.Collaboration with Data TeamsCoordinate effectively with data engineers and data scientists.Learn to translate business needs into technical implementations.Advanced Analytics and Machine LearningGet introduced to predictive analytics including regression and classification techniques.Prepare data for ML with proper feature engineering and handling of missing values.Understand how to integrate ML models with structured, transformed data.Continuous Integration and Deployment (CI/CD)Automate deployment pipelines for analytics workflows.Use version control and CI/CD tools like Jenkins and GitHub Actions.Implement monitoring and logging for robust pipeline performance.What You'll Get:500+ Interview Questions and Answers - Including conceptual and scenario-based challenges.6 Realistic Practice Exams - Covering all core topics with detailed explanations.Topic-wise Focus - Enabling strong foundational understanding and job readiness.Practical Techniques - Aligned with real-world tools, workflows, and best practices.Whether you're new to analytics engineering or looking to brush up your skills for an upcoming interview, this course will guide you step-by-step toward mastery and interview success.
