Crack placements with Data Structures & Algorithms
A placement-focused path through C++ and the STL, arrays through graphs and dynamic programming, with every topic tied to the patterns that actually show up in interview rounds — then five extra weeks of advanced graphs, advanced strings, design problems, bitmask/digit DP and range-query structures, ending in a capstone project that goes beyond solving isolated problems.
Prerequisites & tooling
What you should already know
- Basic programming in any language — variables, loops, functions
- No prior C++ or DSA experience required — Week 1 starts from the syntax up
- Comfortable installing a compiler and using a code editor
- A C++17-capable compiler (g++ or clang++) and VS Code or an online judge
Stack you'll work with
The same language and library placement interviews at product and service companies are conducted in.
Your 20-week learning path
Seventeen modules, from your first vector through advanced graphs, strings, design problems and bitmask DP, to a capstone project that puts every data structure to work.
C++ & Complexity Foundations
The C++ and Big-O fluency every later module assumes.
- C++ syntax essentials: references, pointers & pass-by-reference
- Time & space complexity — reading and deriving Big-O
- STL essentials:
vector,pair& the<algorithm>header
Arrays, Two Pointers & Sliding Window
The patterns behind most "easy" and "medium" array problems.
- Prefix sums & in-place array manipulation
- The two-pointer technique (opposite ends & fast/slow)
- Fixed and variable-size sliding windows
Binary Search & Sorting
Search and order a dataset in better than linear time.
- Binary search on arrays and on an answer space
- Merge sort & quicksort, implemented from scratch
std::sortwith custom comparators
Strings & Bit Manipulation
Text-processing patterns and the bitwise tricks interviewers love to probe.
- String hashing, palindromes & pattern matching
- Bitwise operators, masks & the XOR trick family
- Counting set bits & subset enumeration via bitmasking
Linked Lists, Stacks & Queues
The pointer-based structures behind a huge share of interview questions.
- Singly/doubly linked lists, reversal & cycle detection
- Stacks: balanced parentheses & the monotonic stack pattern
- Queues & deques with
std::deque
Recursion & Backtracking
Think in terms of state, choices and undoing a choice.
- The recursion tree & base-case discipline
- Backtracking: subsets, permutations & N-Queens
- Pruning search spaces for performance
Trees & Binary Search Trees
Hierarchical data and the traversals every tree question builds on.
- Binary tree traversals: inorder, preorder, postorder & level-order
- BST properties, insertion, deletion & validation
- Lowest common ancestor & diameter-style tree problems
Heaps, Greedy Algorithms & Tries
Priority-driven structures and the "make the locally best choice" mindset.
priority_queue& the top-K family of problems- Greedy proofs: interval scheduling & Huffman-style problems
- Tries for prefix search & autocomplete
Graphs: Traversal, Shortest Paths & Union-Find
Model relationships as a graph and traverse them correctly.
- BFS/DFS on adjacency lists & connected components
- Dijkstra's algorithm & topological sort
- Union-Find (disjoint set) with path compression
Dynamic Programming I: Foundations
Turn exponential recursion into a polynomial-time solution.
- Memoization vs. tabulation, and spotting overlapping subproblems
- 1D DP: climbing stairs, house robber & longest increasing subsequence
- 2D DP: 0/1 knapsack & edit distance
Dynamic Programming II: Grids, Trees & Interval DP
Harder DP shapes, including interval DP over ranges instead of prefixes.
- DP on trees & DP on grids with obstacles
- Interval DP: matrix chain multiplication
- Palindrome partitioning with minimum cuts
Advanced Graphs: MST & Shortest Paths II
Minimum spanning trees and shortest paths beyond Dijkstra's assumptions.
- Minimum spanning trees: Kruskal's & Prim's algorithms
- Bellman-Ford for negative edge weights
- Floyd-Warshall for all-pairs shortest paths
Advanced Strings
Linear-time algorithms interviewers specifically look for by name.
- The Z-algorithm for linear-time pattern matching
- Manacher's algorithm: longest palindromic substring in O(n)
- Rabin-Karp for multi-pattern search
Design Problems
Build custom data structures from a set of required operations, not an algorithm name.
- LRU cache with O(1) get/put
- Min stack & a hash map built from scratch
- Design Twitter: merging feeds with a heap
Advanced Dynamic Programming
State that's an entire subset, and counting problems over huge numeric ranges.
- Bitmask DP: the Traveling Salesman Problem
- Bitmask DP: the assignment problem
- Digit DP for counting numbers with a property
Advanced Data Structures
Range queries fast, whichever way the underlying array does or doesn't change.
- Segment trees with lazy propagation for range updates
- Fenwick trees (Binary Indexed Trees)
- Sparse tables for O(1) static range-minimum queries
Capstone: Build a Mini Expression Interpreter
Apply stacks, recursion and trees to a project instead of an isolated problem.
- Tokenizer, an expression tree & a recursive evaluator
- Operator precedence handled with a stack-based parser
- Portfolio-ready project you can show employers