Data Warehousing Essentials for Analytics and AI Support
Master data warehousing fundamentals: Learn key concepts and architectures for analytics and AI applications.
Master data warehousing fundamentals: Learn key concepts and architectures for analytics and AI applications.
This course covers essential topics in Data Engineering to support decision support systems, data analytics, data mining, machine learning, and artificial intelligence. Students will study on-premises data warehouse architecture and dimensional modeling of data warehouses. The curriculum begins with an introduction to data warehousing and business intelligence, emphasizing their role in enhancing organizational decision-making. It covers the transformation of raw data into actionable insights using processes like ETL (Extract, Transform, Load) and tools such as OLAP for querying and data mining. The course progresses through database concepts, including conceptual and logical database design, SQL basics, and normalization principles. It then delves into data warehouse concepts, exploring the multidimensional model, data cubes, and OLAP operations. By the end of the course, students will have a comprehensive understanding of data warehouse design, implementation, and its applications in analytics and AI support.
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What you'll learn
Understand the fundamentals of data warehousing and its role in business intelligence
Master conceptual and logical database design principles
Learn SQL basics for querying and managing databases
Understand the multidimensional data model and its representation as data cubes
Explore OLAP operations and their application in data analysis
Gain knowledge of data warehouse architectures and their implementation
Skills you'll gain
This course includes:
1 Hours PreRecorded video
5 assignments
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FullTime access
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There are 4 modules in this course
This course provides a comprehensive introduction to data warehousing essentials for analytics and AI support. It covers the entire process of data warehouse design and implementation, from basic database concepts to advanced data warehouse architectures. The curriculum is structured into four modules, each building upon the previous one. Students start with database fundamentals, including conceptual and logical database design, before moving on to data warehouse-specific concepts. They learn about multidimensional models, data cubes, and OLAP operations, which are crucial for understanding how data is structured and analyzed in data warehouses. The course emphasizes both theoretical knowledge and practical skills, with assignments and discussions that help reinforce learning. By the end of the course, students will be well-equipped to understand and work with data warehouse systems that support analytics and AI applications.
Database Concepts 1
Module 1 · 4 Hours to complete
Database Concepts 2
Module 2 · 4 Hours to complete
Data Warehouse Concepts 1
Module 3 · 4 Hours to complete
Data Warehouse Concepts 2
Module 4 · 3 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Professor at Northeastern University
Venkat Krishnamurthy is a professor at Northeastern University, where he teaches courses in the field of data management and analytics. He is involved in several key courses, including IE 6700 - Data Management for Analytics, IE 6750 - Data Warehousing and Integration, and IE 7374 - Machine Learning Operations. Dr. Krishnamurthy has a strong background in data analytics and engineering, contributing to the development of educational programs that prepare students for careers in these rapidly evolving fields. His expertise extends to online education, where he teaches courses such as "Data Warehousing Essentials for Analytics and AI" on platforms like Coursera. With a focus on integrating practical skills with theoretical knowledge, Dr. Krishnamurthy plays a vital role in shaping the next generation of data professionals at Northeastern University.
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