Data Manipulation at Scale: Systems and Algorithms

Data Manipulation at Scale: Systems and Algorithms is an in-depth course offered by the University of Washington, focusing on the challenges and methodologies of data science at realistic scales. This course provides a comprehensive understanding of the landscape of relevant systems, their principles, tradeoffs, and evaluation against specific requirements. By the end of the course, participants will have the ability to:

• Describe common patterns, challenges, and approaches associated with data science projects
• Identify and use programming models for scalable data manipulation, including relational algebra, mapreduce, and other data flow models
• Evaluate key-value stores and NoSQL systems, understanding their tradeoffs and future trends
• Write programs in Spark and understand the associated ecosystem of algorithms, extensions, and languages
• Describe the landscape of specialized Big Data systems for graphs, arrays, and streams

This course delves into a wide range of topics, including data science context, relational databases and the relational algebra, MapReduce and parallel dataflow programming, NoSQL systems and concepts, and graph analytics. It is designed to equip learners with the necessary skills and knowledge to effectively work with large, heterogeneous, and noisy datasets, providing a solid foundation for data science at realistic scales.