Advanced Algorithms for Real-World Applications

Who is this course for?
This course is specifically designed for intermediate software engineers, backend developers, and data engineers who have general programming experience but have not yet explored advanced scalable algorithms such as Consistent Hashing, Bloom Filters, and HyperLogLog. If you have ever wondered how distributed systems, large-scale databases, or web analytics platforms handle billions of requests, scale horizontally, or efficiently estimate unique users, this course is for you. You don’t need prior knowledge of these algorithms, but you should be comfortable reading and writing code in a mainstream language (Python, Java, C++, Go, JavaScript, etc.).

Why take this course?
Modern software runs at massive scale. Traditional data structures and approaches—like hash maps, sets, or simple counters—often become bottlenecks or impractical when distributed across many machines or when data grows exponentially. This course closes the gap between classic programming knowledge and the scalable, probabilistic, and distributed techniques used in real-world systems at companies like Google, Amazon, and Facebook. You will not just learn the ‘how’, but also the ‘why’—the practical motivations and trade-offs behind each algorithm.

What will you learn?

• The core motivation for scalable, distributed, and probabilistic algorithms
• How Consistent Hashing solves rebalancing and partitioning in distributed caches and databases—using virtual nodes and hash rings
• Bloom Filters: how they enable lightning-fast set membership queries with limited memory, and why they allow false positives but never false negatives
• HyperLogLog: how it’s possible to estimate the cardinality (number of unique elements) in massive data streams using just kilobytes of memory, and how this is leveraged in web analytics and data platforms
• Key differences between these algorithms and traditional approaches, including performance trade-offs, limitations, and best-fit use cases
• Step-by-step implementation in pseudocode or Python, always explained by intuition and analogy, so you can easily port to any language
• Real-world scenarios: caching, load balancing, log analysis, deduplication, analytics, and more
• A practical mindset: each topic is introduced by the real-world problem it solves, follows with intuitive visuals, hands-on coding, and is concluded with a recap that connects it to other topics
• How other probabilistic structures like Count-Min Sketch compare, and pointers for deeper study

What will you gain?

• The confidence to design and reason about scalable backend and data processing systems
• The ability to evaluate when and why to use advanced algorithms instead of traditional data structures
• Hands-on experience translating algorithmic concepts into code and real-world solutions
• A practical foundation for learning even more advanced techniques in distributed systems and big data

This course is intentionally direct and practical—no academic jargon, no unnecessary theory. Every lesson is focused on intuition, analogy, and real code. Prepare to level up your engineering toolkit for the challenges of modern large-scale systems!