Scalable Data Science and Large-Scale Data Processing

As datasets grow exponentially, traditional data analysis methods quickly reach their limits. To extract value from massive data, you must understand how to design algorithms and systems that scale horizontally across clusters. This course provides a solid foundation in the mathematical and practical principles of scalable data science, teaching you how to think about big data problems systematically. You will start with core concepts of computational complexity, memory bottlenecks, and distributed architectures before moving into practical algorithmic strategies. By reading through structured explanations and analyzing code examples, you will learn how to design data pipelines that remain fast and cost-effective at scale. What you will learn: Understand the core trade-offs of distributed systems, including storage, memory, and network bottlenecks; Apply randomized algorithms, hashing techniques, and sketching for streaming data; Master the principles of parallel processing and map-reduce execution flows; Configure vector databases and modern retrieval-augmented generation patterns for large-scale AI applications; Design scalable machine learning pipelines that handle high-dimensional feature spaces. The course begins with essential theoretical foundations and fundamental definitions of scalability, then transitions into algorithmic techniques, and concludes with modern distributed system architectures. This course is designed for beginner data scientists, software engineers, and analytical professionals who want to transition from local data processing to large-scale distributed environments. No prior experience with big data frameworks is required. Start reading today to build data systems that scale effortlessly.