Study Plan

A 12-week plan built around the principle: learn deeply, not broadly. It is better to deeply understand 150 problems than to superficially graze 500. Each week introduces a topic, applies it to problems, and reviews the previous week through spaced repetition. By week 12 you will be ready for FAANG-level interviews.

Weeks 1–2: Arrays, Strings & Hashing

Foundations. Almost every other topic builds on array manipulation and HashMap usage.

Day	Topic	Problems to Solve
1	Arrays: two pointers, prefix sums	Two Sum, Best Time to Buy/Sell Stock, Max Subarray
2	Sliding window (fixed size)	Max Sum Subarray of Size K, Average of Subarrays
3	Sliding window (variable size)	Longest Substring Without Repeating, Fruit Into Baskets
4	HashMap / HashSet	Contains Duplicate, Group Anagrams, Valid Anagram
5	Prefix sums + difference arrays	Range Sum Query, Subarray Sum Equals K
6–7	Review + new strings	Valid Palindrome, Longest Common Prefix, Roman to Integer
8	String matching: KMP / Z-algo	Implement strStr(), Repeated Substring Pattern
9	2D arrays, matrix traversal	Spiral Matrix, Rotate Image, Set Matrix Zeroes
10	Sorting-based problems	3Sum, Sort Colors, Merge Intervals
11	Binary search fundamentals	Binary Search, Search in Rotated Array, Find Peak
12–14	Week review + spaced repetition	Re-solve Day 1–5 problems from scratch

Weeks 3–4: Linked Lists, Stacks & Queues

Day	Topic	Problems to Solve
15	Singly linked list: basics	Reverse Linked List, Middle of List, Merge Two Sorted
16	Linked list: fast & slow pointers	Linked List Cycle, Cycle Start, Kth Node From End
17	Doubly linked list + LRU Cache	LRU Cache (design from scratch)
18	Stack: basic applications	Valid Parentheses, Min Stack, Evaluate RPN
19	Monotonic stack	Daily Temperatures, Next Greater Element, Largest Rectangle
20	Queue and deque	Sliding Window Maximum, Design Circular Queue
21–22	Stack + queue design problems	Queue via Stacks, Stack via Queues, Min Stack
23	In-place list reversal	Reverse Nodes in K-Group, Reorder List, Palindrome List
24–28	Review weeks 1–4	Pick 2 random problems per day from previous topics

Weeks 5–6: Trees, Heaps & Trie

Day	Topic	Problems to Solve
29	Binary tree DFS	Max Depth, Invert Tree, Same Tree, Symmetric Tree
30	Tree paths and sums	Path Sum, Path Sum II, Sum Root to Leaf Numbers
31	Tree BFS (level order)	Level Order, Zigzag Level Order, Right Side View
32	BST properties	Validate BST, Kth Smallest in BST, BST Iterator
33	LCA and ancestors	LCA of BST, LCA of Binary Tree, All Nodes Distance K
34	Binary tree construction	Build Tree (pre+inorder), Serialize/Deserialize
35	Heap basics + top K	Kth Largest Element, K Most Frequent Elements, Top K
36	Heap: median stream, merge K	Find Median from Data Stream, Merge K Sorted Lists
37	Trie	Implement Trie, Word Search II, Replace Words
38–42	Review weeks 1–6	One problem per prior topic daily

Weeks 7–8: Graphs, Sorting & Searching

Day	Topic	Problems to Solve
43	Graph representations + BFS	Number of Islands, Rotting Oranges, 01 Matrix
44	DFS on graphs	Clone Graph, Path Exists, Surrounded Regions
45	Topological sort	Course Schedule I & II, Alien Dictionary
46	Union-Find	Number of Connected Components, Redundant Connection
47	Weighted graphs: Dijkstra	Network Delay Time, Path With Min Effort
48	Bellman-Ford + Floyd-Warshall	Cheapest Flights K Stops, Find City With Fewest
49	MST: Kruskal + Prim	Min Cost to Connect All Points, Optimize Water
50	Advanced graph: bipartite, SCC	Is Graph Bipartite, Critical Connections
51	Sorting algorithms (implement)	Code merge sort, quick sort, counting sort from scratch
52–56	Binary search advanced	Search in 2D Matrix II, Find K Closest, Koko Eating

Weeks 9–10: Greedy, Backtracking & Divide and Conquer

Day	Topic	Problems to Solve
57	Greedy fundamentals	Jump Game, Jump Game II, Gas Station
58	Greedy: intervals	Non-overlapping Intervals, Meeting Rooms II
59	Backtracking: subsets	Subsets, Subsets II, Combination Sum
60	Backtracking: permutations	Permutations, Permutations II, Next Permutation
61	Backtracking: N-Queens, Sudoku	N-Queens, Sudoku Solver, Word Search
62	Backtracking pruning techniques	Palindrome Partitioning, Restore IP Addresses
63	Divide and conquer	Merge Sort (implement), Quick Sort pivot strategies
64	D&C on arrays	Median of Two Sorted Arrays, Kth Largest in Two Arrays
65–70	Review full backtracking set	Re-solve all backtracking problems without notes

Weeks 11–12: Dynamic Programming + Review + Mock Interviews

Day	Topic	Problems to Solve
71	1D DP	Climbing Stairs, House Robber, Decode Ways
72	2D DP (grid)	Unique Paths, Min Path Sum, Dungeon Game
73	Sequence DP	Longest Increasing Subsequence, Russian Dolls
74	String DP	Edit Distance, Longest Common Subsequence, Interleaving
75	Knapsack variants	Partition Equal Subset, Target Sum, Coin Change
76	DP on trees	House Robber III, Diameter of Binary Tree
77	Interval DP	Burst Balloons, Matrix Chain, Strange Printer
78	Bitmask DP	Assign Work, Shortest Path Visiting All Nodes
79–80	Full mock interviews	3 problems, 45 min each; simulate real conditions
81–84	Weak area focus	Double down on your 3 weakest topics

Daily Routine

20 min: warm up with one Easy problem from a previous topic
40–60 min: new problem(s) for the current day's topic
15 min: review yesterday's solutions — could you make it cleaner?
10 min: update your pattern journal (what pattern did each problem use?)

Spaced Repetition System

Use a simple tagging system for every problem you solve:

Text

Tag system:
  [1] — solved on first try with no help
  [2] — needed a hint or minor struggle
  [3] — had to look at the solution

Review schedule:
  [1] problems → revisit in 7 days
  [2] problems → revisit in 3 days
  [3] problems → revisit next day

If you tag [1] again on the revisit → move to 2-week cycle.
If you tag [3] again → it goes back on the daily queue.

When to Move On

You can solve the problem clean without looking at previous solutions
You can explain the time and space complexity instantly
You can identify the pattern and explain why it applies
You can name at least 2 variant problems that use the same pattern

Warning

Do not skip weeks. The plan is cumulative — graph algorithms assume you know BFS/DFS well, which assumes you understood tree traversal, which assumes you are comfortable with recursion. Rushing ahead creates gaps that will hurt you in interviews on "simple" follow-up questions.

Recommended Resources

Resource	Best for	Free?
LeetCode	Problem practice with test cases	Partial (free has most problems)
NeetCode.io	Structured roadmap + video explanations	Free
AlgoExpert	Video walkthroughs, structured progression	Paid
CLRS (book)	Deep theoretical understanding	Paid
Grokking the Coding Interview	Pattern-based learning	Paid
Competitive Programmer's Handbook	Advanced topics, free PDF online	Free
Back to Back SWE (YouTube)	Clear visual explanations of hard problems	Free
Tech Interview Handbook	Behavioral + technical combined	Free (GitHub)

Tip

The biggest mistake candidates make is grinding hundreds of problems without retention. Slow down, solve fewer problems, and truly understand each one. Depth beats breadth in interviews because interviewers ask follow-ups and variants — which you can only handle if you understand the core idea, not just the solution code.