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Home > Computing and Information Technology > Databases > Data capture and analysis > Data Warehousing for Data Scientists: Building a Foundation for Advanced Analytics, Machine Learning, and Data Mining
Data Warehousing for Data Scientists: Building a Foundation for Advanced Analytics, Machine Learning, and Data Mining

Data Warehousing for Data Scientists: Building a Foundation for Advanced Analytics, Machine Learning, and Data Mining


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About the Book

Unlock the power of your data with Data Warehousing for Data Scientists: Building a Foundation for Advanced Analytics, Machine Learning, and Data Mining.This comprehensive guide provides a practical, hands-on approach to designing, building, and leveraging data warehouses for enhanced business intelligence, cutting-edge machine learning, and insightful data mining. It equips you with the knowledge and skills to transform raw data into actionable insights, driving better decision-making and innovation. The journey begins with a solid foundation in data warehousing principles, exploring core concepts, terminology, and architectural blueprints. From understanding fundamental building blocks like facts, dimensions, and cubes, to grasping the intricacies of star and snowflake schemas, you'll gain a clear understanding of the role data warehousing plays in the broader analytics landscape. Master the art of data modeling with a deep dive into dimensional modeling techniques, enabling you to design schemas optimized for querying, reporting, and analysis. Learn how to structure data for optimal performance and scalability, ensuring your data warehouse can handle the demands of your organization's growing data needs. This book demystifies the crucial Extract, Transform, Load (ETL) process. You'll explore a range of data extraction techniques from diverse sources, mastering methods for transforming raw data into a consistent and usable format for your data warehouse. Efficient data loading strategies are explored, along with best practices for ETL orchestration and automation. Navigate the complexities of data warehouse implementation, weighing the advantages of cloud-based solutions against traditional on-premise deployments. Gain practical guidance on selecting the right technologies for your specific business needs and constraints, ensuring a cost-effective and efficient implementation. Data quality and governance are paramount. This book equips you with the tools and strategies to ensure data reliability and trustworthiness. Learn about data quality management techniques, establish robust data governance frameworks, and delve into the critical role of metadata management. Data security best practices and compliance with relevant regulations are also emphasized. Unleash the power of your data warehouse for advanced analytics. Explore the intricacies of Online Analytical Processing (OLAP) and data mining, uncovering hidden patterns and insights. Effective data visualization techniques are also covered, empowering you to communicate complex findings clearly and concisely. Bridge the gap between data warehousing and machine learning. Learn how to engineer powerful features from your warehouse data to train robust machine learning models. Explore model deployment strategies, the integration of MLOps with data warehouses, and gain insights into real-world applications of this powerful combination. Future trends in data warehousing and machine learning are also discussed, preparing you for the evolving data landscape. From foundational concepts to advanced applications, Data Warehousing for Data Scientists provides a complete roadmap for harnessing the power of data. Equip yourself with the knowledge and skills to transform data into actionable insights and drive impactful decisions. Architect your data future. Get your insights flowing!


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Product Details
  • ISBN-13: 9798348478889
  • Publisher: Ellis Meadowlight
  • Publisher Imprint: Ellis Meadowlight
  • Height: 229 mm
  • No of Pages: 118
  • Sub Title: Building a Foundation for Advanced Analytics, Machine Learning, and Data Mining
  • Width: 152 mm
  • ISBN-10: 8348478884
  • Publisher Date: 22 Jan 2025
  • Binding: Paperback
  • Language: English
  • Spine Width: 6 mm
  • Weight: 222 gr


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