COMPSCI 532: Systems for Data Science (Grade: A)

Course Overview:
COMPSCI 532: Systems for Data Science is a 3-credit course designed to provide students with a deep understanding of the systems and infrastructures that underpin large-scale data science. The course focuses on the principles and challenges associated with scaling computational processes both vertically (to many processors) and horizontally (to many nodes), enabling efficient and fast analyses of large datasets.

Instructor: Peter F. Klemperer, pklemperer@umass.edu
Lectures: TBA, on Zoom
Course Web Page: Moodle (Learning Management System)
Office Hours: By appointment on Zoom

The course content is broad and touches on various critical aspects of system design and performance in the context of data science, including:

• Locality and Data Representation: Understanding how data is stored and accessed efficiently in distributed systems.
• Concurrency: Managing multiple simultaneous computations without conflicts.
• Distributed Databases and Systems: Exploring the architecture and operation of databases that span multiple machines.
• Performance Analysis and Optimization: Learning how to measure, understand, and improve the performance of large-scale data systems.

Throughout the course, students engage with both theoretical concepts and practical applications, using industry-standard platforms such as MapReduce-Hadoop and Apache Spark. These platforms are essential tools for processing vast amounts of data in parallel, and the course provides hands-on experience in working with them.

Key Topics Covered

• Scaling Up and Scaling Out: The course begins by exploring the fundamentals of scaling in data science, covering the challenges and strategies for expanding computational power across processors and nodes.

• Locality and Data Representation: Delving into how data is represented and stored across distributed systems, emphasizing the importance of locality in data processing.

• Concurrency: Focusing on how to manage multiple processes running concurrently, ensuring that they do not interfere with each other and maintain data integrity.

• Distributed Databases and Systems: A deep dive into the architecture of distributed databases, including how they manage data across different machines and ensure consistency and availability.

• Performance Analysis and Optimization: Students learn how to assess the performance of data systems and implement optimizations to improve efficiency and speed.

• Data Science Platforms: The course provides hands-on experience with leading data science platforms, such as MapReduce-Hadoop and Apache Spark. Students learn to leverage these tools to process and analyze large datasets effectively.

Prerequisites

The course requires a solid foundation in computer science, with prerequisites including COMPSCI 377 (Operating Systems) and COMPSCI 445 (Database Design and Implementation). These courses provide the necessary background in system architecture and database management, enabling students to fully engage with the material in COMPSCI 532.

Personal Achievement

I completed COMPSCI 532: Systems for Data Science with an A Grade (4.0/4.0), reflecting my strong grasp of the concepts and practical skills required to work with large-scale data systems. This course significantly enhanced my understanding of the infrastructure that supports big data processing and prepared me to tackle complex data science challenges in a professional setting.

Final Thoughts

COMPSCI 532 was an eye-opening course that deepened my knowledge of the systems and technologies that make large-scale data analysis possible. The combination of theoretical foundations and practical experience with tools like Hadoop and Spark provided me with a robust skill set that I am eager to apply in my future data science endeavors.