Foundations
Data Structures & Algorithms
Data structures and algorithms are how you organize information and reason about performance — the difference between code that works on your laptop and code that survives a million real users. This is the most-interviewed-on skill set in the entire industry, and the foundation every later module in this program builds on.
Topic Map
Everything in this module, at a glance
In Depth
Every topic, explained — with real-world industry context
Linear Data Structures
The simplest way to organize a collection of values, where each element sits in a sequence — the structures you will reach for constantly, often without even thinking about it.
- Arrays — contiguous memory, indexing & fixed vs. dynamic sizing
- Strings — as arrays of characters, and common string algorithms
- Linked lists — singly & doubly linked, and when they beat arrays
- Stacks (LIFO) & queues (FIFO) — and the problems each shape naturally solves
Real-World Industry Use Case
Your browser's "back button" history is a stack. A print queue or a customer-support ticket queue is, literally, a queue. Undo/redo in tools like Photoshop and VS Code is implemented with a stack of past actions.
Non-Linear Data Structures
Structures that model relationships and hierarchy rather than a simple sequence — this is where data starts looking like the real world: organizations, file systems, maps, and networks.
- Trees & binary search trees — hierarchical data & ordered lookups
- Heaps — priority queues, and keeping the "most important" item on top
- Tries — prefix trees for fast string lookups
- Graphs — nodes & edges, directed vs. undirected, weighted vs. unweighted
Real-World Industry Use Case
Autocomplete and spell-check in search engines are built on tries. Google Maps' route-finding and every "shortest path" feature runs on graph algorithms. Your file system — folders inside folders — is a tree. Task schedulers use heaps to always run the highest-priority job next.
Sorting & Searching
Two of the most common operations in all of computing — putting data in order, and finding a specific piece of it quickly — done wrong, they are the easiest way to make an app feel slow.
- Comparison sorts — quicksort, mergesort, and why "just use .sort()" still requires understanding what's underneath
- Binary search — searching sorted data in O(log n) instead of O(n)
- Hashing & hash tables — near-instant lookups by key
- Trade-offs: stability, in-place sorting, and worst-case behavior
Real-World Industry Use Case
Every database index is fundamentally a sorted structure enabling fast search. Hash tables power caching layers (like Redis) used by nearly every high-traffic app to avoid re-computing or re-fetching the same data repeatedly.
Algorithmic Techniques
A handful of reusable problem-solving strategies that show up again and again, in interviews and in production code alike — this is where "thinking like an engineer" really clicks.
- Recursion — a function that calls itself, and how to trust the recursive leap of faith
- Dynamic programming — solving a big problem by remembering solutions to smaller overlapping ones
- Greedy algorithms — making the locally-best choice at each step
- Backtracking — trying a path, and undoing it cleanly when it fails
Real-World Industry Use Case
Dynamic programming on trees underlies real optimization problems — from network routing to compiler register allocation. Backtracking is how Sudoku solvers and constraint-satisfaction tools work. GPS navigation apps use greedy and DP-based algorithms together to balance "fastest" vs. "shortest" routes.
Complexity Analysis
Big-O analysis goes from "intuition" (module one) to a real, applied skill here — the language engineers use to reason about and compare solutions before writing a single line of code.
- Big-O, Big-Theta & Big-Omega notation
- Time complexity vs. space complexity trade-offs
- Best-case, average-case & worst-case analysis
- Reading a problem and estimating the "right" complexity target
Real-World Industry Use Case
This is the single most consistently-tested skill in technical interviews at every major tech company — not memorized answers, but the ability to analyze a new problem on the spot. It is also exactly the skill engineers at companies like the ones covered in "The Pragmatic Engineer" report actually using day-to-day, not just in interviews.
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