Structured Learning for Real Algorithm Mastery
We built this program after seeing too many developers struggle with scattered tutorials and fragmented knowledge. It's a cohesive path from fundamentals through advanced techniques, with genuine peer support along the way.
Request Program DetailsFour Phases That Actually Build On Each Other
Most programs throw content at you without structure. We've organized everything so each concept connects to the next. You won't jump from arrays to graph theory without understanding what comes between.
Each phase takes about ten weeks if you're working through it steadily — maybe faster if you've got prior experience, maybe slower if life gets busy. The timeline adapts to how you learn.
Foundation Building
Start with basic data structures and time complexity. We cover arrays, linked lists, stacks, and queues with practical examples you'll actually encounter.
Pattern Recognition
Learn to spot common algorithmic patterns. Sliding windows, two pointers, recursion fundamentals — the techniques that show up repeatedly in real coding scenarios.
Complex Structures
Trees, graphs, heaps, and hash tables. This is where things get interesting and you start seeing how different structures solve different problems.
Advanced Techniques
Dynamic programming, greedy algorithms, and graph algorithms. The challenging stuff that separates competent developers from strong ones.
What Each Module Actually Covers
We break down complex topics into digestible chunks. Each module includes problem sets, code reviews, and practical exercises that mirror real development challenges.
Array Manipulation & Strings
Master the basics that appear in every coding interview and real project. String parsing, array transformations, and efficiency optimization.
- Two-pointer techniques
- Sliding window patterns
- In-place modifications
- String algorithms
Recursion & Backtracking
Build intuition for recursive thinking. We start simple and gradually increase complexity until backtracking feels natural.
- Base cases and termination
- State space exploration
- Pruning strategies
- Memoization basics
Trees & Binary Search
From simple BST operations to complex tree traversals. You'll understand when and why to use each tree structure.
- Tree traversal methods
- Binary search trees
- Balanced tree concepts
- Tree construction problems
Graph Algorithms
Navigate the world of graph theory with BFS, DFS, and shortest path algorithms. Real applications from social networks to route planning.
- Graph representations
- Traversal algorithms
- Shortest path problems
- Topological sorting
Dynamic Programming
The module everyone finds difficult at first. We break it down methodically until the patterns click and you start recognizing DP opportunities.
- Overlapping subproblems
- Optimal substructure
- Tabulation vs memoization
- Common DP patterns
System Design Fundamentals
Apply your algorithm knowledge to larger system contexts. Understand trade-offs between different data structure choices in production environments.
- Scalability considerations
- Data structure selection
- Performance trade-offs
- Real-world constraints
Learn Together, Not Alone
Algorithms can feel isolating when you're stuck on a problem at midnight. That's why we built collaboration into the program structure — not as an afterthought, but as a core component.
You'll work in small study groups that rotate throughout the program. Sometimes you're explaining concepts to others, sometimes you're the one asking questions. Both experiences help solidify understanding.
Weekly Group Sessions
Structured problem-solving sessions where you work through challenges with peers. Different perspectives often unlock solutions you wouldn't find alone.
Code Review Practice
Review others' solutions and have yours reviewed. You'll learn different approaches and develop critical thinking about code efficiency.
Shared Progress Tracking
See how your cohort progresses through modules. It's motivating to know others are working through the same challenges.
Peer Teaching Opportunities
Once you grasp a concept, teaching it to someone else cements your understanding. We facilitate these exchanges throughout the program.
Who You'll Learn From
Our instructors have spent years working with algorithms in production environments. They've debugged performance issues at scale and understand what actually matters beyond textbook theory.
Dmitri Kovalenko
Algorithm Design Lead
Spent eight years optimizing search algorithms at a major tech company. Now focuses on teaching pattern recognition and problem decomposition strategies.
Linnea Bergström
Data Structures Specialist
Background in database internals and distributed systems. Explains complex concepts through practical examples that stick with students.
Siobhan MacLeod
Dynamic Programming Coach
Previously worked on optimization problems for logistics software. Known for making DP concepts accessible through incremental learning approaches.