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 — 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 15-week learning path
Twelve modules, from your first vector 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 & Advanced Structures
Harder DP shapes and the structures behind range-query problems.
- DP on trees & DP on grids with obstacles
- Segment trees & Fenwick trees (Binary Indexed Trees)
- Choosing the right structure under interview time pressure
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