DSA Notes
HubAlgorithm vs Data Structure — Key Differences, Definitions, and How They Work TogetherApplications of DSA — How Data Structures and Algorithms Power Real-World SoftwareTime and Space Complexity — A Complete BeginnerWhat is DSA? — A Complete BeginnerWhy Learn DSA? — Career Benefits, Real-World Impact, and Long-Term ValueAsymptotic Analysis — Complete Framework with Recurrence Relations and Master TheoremBest, Average, and Worst Case Analysis — A Practical Guide for Every AlgorithmBig O Notation — Complete Guide with Formal Definition, Rules, and ExamplesBig Omega Notation — Lower Bounds, Best Case, and Formal DefinitionBig Theta (Θ) Notation — Tight Bounds and Exact Growth RatesSpace Complexity — Memory Analysis, Auxiliary Space, and Time-Space TradeoffsTime Complexity Examples — 25 Worked Problems from Basic to AdvancedArray Interview Problems — 15 Classic Problems with Complete SolutionsArray Operations — Every Operation Explained with Code, Complexity, and Edge CasesArray Rotation — Algorithms, Techniques, and Interview ProblemsArray Searching — Linear Search, Binary Search, and Advanced TechniquesArrays — The Complete Introduction to the Most Fundamental Data StructureDynamic Arrays — How Python Lists and ArrayLists Work InternallyMultidimensional Arrays — 2D Arrays, Matrices, and Memory LayoutSliding Window Technique — Complete Guide with 10 Classic ProblemsAnagram Problems — Detection, Grouping, and Sliding Window VariationsPalindrome Problems — Check, Longest, Count, and Expand Around CenterPattern Searching — KMP, Rabin-Karp, and Boyer-Moore AlgorithmsString Interview Problems — 10 Classic Problems with Complete SolutionsString Matching — Longest Common Subsequence, Edit Distance, and Z-AlgorithmString Operations — Complete Reference with Complexity and ImplementationStrings — Complete Introduction to String Data Structure and OperationsCircular Linked List — Structure, Operations, and ApplicationsDoubly Linked List — Complete Implementation with Bidirectional OperationsLinked List Interview Problems — 12 Classic Problems with Complete SolutionsLinked Lists — A Complete Introduction with Memory Model and TypesLinked List Operations — Every Technique with Code and ComplexityReverse Linked List — All Approaches from Basic to AdvancedSingly Linked List — Complete Implementation with All OperationsBalanced Parentheses — Stack-Based Validation with All VariantsInfix, Prefix, and Postfix Expressions — Stack-Based Conversion and EvaluationStack Implementation — Three Complete Implementations with Edge CasesStack Interview Problems — 10 Classic Problems with Complete SolutionsStack — Complete Introduction to LIFO Data StructureStack Using Arrays — Memory Layout, Capacity Management, and PerformanceStack Using Linked List — Implementation, Analysis, and When to Use ItCircular Queue — The Ring Buffer Explained with ImplementationDeque — Double-Ended Queue with Complete Implementation and ApplicationsPriority Queue — Complete Guide from Concept to ImplementationQueue Applications — BFS, Level-Order Traversal, 01-BFS, and MoreQueue Implementation — Array, Linked List, and Two-Stack ApproachesQueue Interview Problems — 10 Classic Problems with Complete SolutionsQueue — Complete Introduction to the FIFO Data StructureAVL Tree - Self-Balancing Binary Search TreeBinary Search Tree (BST)Binary Tree - Structure and OperationsFenwick Tree (Binary Indexed Tree)Red-Black Tree - Another Balanced BSTSegment Tree - Range Query Data StructureTree Interview Problems CollectionTree Data Structure FundamentalsTree Traversals - Inorder, Preorder, Postorder, Level OrderHeap — Complete Introduction to the Priority-Based Tree Data StructureHeap Operations — Complete Reference with Complexity AnalysisHeap Problems — 8 Classic Interview Problems with Complete SolutionsMax Heap — Complete Implementation and ApplicationsMin Heap — Complete Implementation with All OperationsPriority Queue Using Heap — Implementation, Applications, and PatternsBellman-Ford Algorithm — Shortest Path with Negative Weights and Cycle DetectionBFS Algorithm — Breadth-First Search with All Applications and VariantsDFS Algorithm — Depth-First Search with Cycle Detection and All ApplicationsDijkstraFloyd-Warshall Algorithm — All-Pairs Shortest Paths with Complete AnalysisGraph Interview Problems — 12 Classic Problems with Complete SolutionsGraph — Complete Introduction to the Most Powerful Data StructureGraph Representation — Adjacency List, Matrix, Edge List with Complete AnalysisKruskalMinimum Spanning Tree — Definition, Properties, and ApplicationsPrimShortest Path Algorithms — Complete Comparison and Selection GuideTopological Sorting — DFS and BFS (KahnUnion-Find (DSU) in Graphs — Connected Components and Dynamic ConnectivitySeparate Chaining — Complete Implementation and AnalysisCollision Handling — Chaining vs Open Addressing Complete ComparisonHash Functions — Design, Properties, and Common ImplementationsHashing — Complete Introduction to Hash Tables and Hash FunctionsHashing Problems — 10 Classic Interview Problems with Complete SolutionsOpen Addressing — Linear Probing, Quadratic Probing, and Double HashingBubble Sort — Complete Analysis with Optimizations and When to Use ItCounting Sort — O(n+k) Sorting That Breaks the Comparison Lower BoundHeap Sort — Guaranteed O(n log n) with O(1) SpaceInsertion Sort — The O(n) Best Case Sort That Powers PythonMerge Sort — Complete Guide to the Guaranteed O(n log n) Stable SortQuick Sort — The Fastest Practical Sorting Algorithm with Complete AnalysisRadix Sort — O(nk) Digit-by-Digit Sorting for Large Integer RangesSelection Sort — Complete Analysis with Swaps Minimization PropertySorting Algorithm Comparison — The Complete Decision GuideSorting — Complete Introduction to Sorting AlgorithmsBinary Search — Complete Guide with All Variants and Proof of CorrectnessInterpolation Search — O(log log n) Search for Uniformly Distributed DataLinear Search — Complete Guide with All Variants and ApplicationsSearch Algorithm Comparison — Choosing the Right Search for Every SituationSearching Interview Problems — 10 Classic Problems with Complete SolutionsTernary Search — Finding Maximum/Minimum of Unimodal FunctionsBacktracking Introduction — The Recursive Exploration with UndoRecursion Basics — The Complete BeginnerRecursion Problems — 12 Classic Problems from Basic to AdvancedRecursion Tree — Visualizing Recursion for Complexity AnalysisRecursion vs Iteration — When to Use Each and How to ConvertTail Recursion — Definition, Optimization, and Converting to IterationDP Interview Problems — 12 Classic Problems with Pattern Recognition GuideDynamic Programming — Complete Introduction with Properties and Problem IdentificationFibonacci DP — From Exponential to Linear with Memoization and Matrix ExponentiationKnapsack Problem — 0/1, Unbounded, and Fractional with Complete AnalysisLongest Common Subsequence — Complete Guide with DP Table and String VariantsLongest Increasing Subsequence — O(n log n) with Patience SortingMatrix Chain Multiplication — Interval DP with Complete AnalysisMemoization — Top-Down Dynamic Programming with CachingTabulation — Bottom-Up Dynamic Programming with Complete Table BuildingActivity Selection Problem — Greedy Scheduling with Proof of CorrectnessFractional Knapsack — Greedy by Value Density with ProofGreedy Interview Problems — 10 Classic Problems with Complete SolutionsGreedy Algorithms — Complete Introduction with Proof TechniquesHuffman Coding — Optimal Prefix-Free Data CompressionJob Sequencing Problem — Greedy Scheduling with DeadlinesBacktracking — The Complete Guide to Recursive Exploration with UndoBacktracking Problems — 12 Classic Interview Problems with Complete SolutionsN-Queens Problem — Complete Solution with Backtracking and OptimizationRat in a Maze — Backtracking Path Finding with All SolutionsSubset Generation — All Subsets, Combinations, and Partitions with BacktrackingSudoku Solver — Backtracking with Constraint PropagationCodeforces Guide — Complete Strategy to Reach 1800+ RatingCommon CP Techniques — Essential Algorithms Every Competitive Programmer Must KnowContest Strategies — How to Perform Your Best in Competitive Programming ContestsCompetitive Programming — Complete Introduction for BeginnersFast Input/Output — Why I/O Speed Matters and Complete Templates for C++ and PythonLeetCode Guide — Complete Strategy to Crack FAANG InterviewsBit Manipulation — Complete Guide with All Tricks and Interview ProblemsBloom Filter — Space-Efficient Probabilistic Membership TestingComputational Geometry — Points, Lines, Convex Hull, and Geometric AlgorithmsDisjoint Set Union (DSU) — Union-Find with Path Compression and Union by RankRandomized Algorithms — Las Vegas, Monte Carlo, and Probabilistic TechniquesSkip List — The Probabilistic Data Structure that Competes with Balanced BSTsSuffix Array — Construction, LCP Array, and String Search ApplicationsTrie Data Structure — Complete Guide with Implementation and ApplicationsAutocomplete System Using TrieLibrary Management System Using DSARoute Finder Using GraphsMini Search Engine ProjectSocial Network Analysis Using GraphsEssential Coding Patterns for InterviewsDSA Cheatsheet: Quick ReferenceEssential DSA Interview QuestionsFAANG DSA Sheet: Company-Specific Patterns and ProblemsProblem Solving Strategies for Coding InterviewsComplexity CheatsheetImportant Algorithms SummaryBest Practice Platforms for DSARecommended Books for DSA
The definitive LeetCode guide — how to use LeetCode effectively, topic-wise problem lists, the 150-problem study plan, how to approach medium and hard problems, common patterns to master, time management in interviews, and which problems to prioritize by company.
What Is LeetCode?
LeetCode is the world's most popular platform for technical interview preparation. It hosts 3000+ algorithmic problems used by top tech companies including Google, Amazon, Meta, Microsoft, Apple, Bloomberg, Uber, LinkedIn, and hundreds more.
Unlike competitive programming platforms (Codeforces, AtCoder) which focus on algorithm knowledge, LeetCode focuses specifically on the type of problems that appear in software engineering interviews.
The LeetCode Grind — How to Actually Use It
Wrong Approach (What Most People Do)
Example
1. Open LeetCode, pick a random problem
2. Spend 2 hours on a medium problem, give up
3. Look at solution, understand it partially
4. Forget it by next week
5. Repeat, make no real progress
Right Approach (Topic-Based Learning)
Example
1. Learn an algorithm/data structure (from this course)
2. Immediately solve 5-10 LeetCode problems on that topic
3. If stuck > 20 minutes, look at hint (not full solution)
4. After solving, read top solutions for new perspectives
5. After 3 days, resolve the same problem without looking
6. Move to next topic only after mastering current one
The Essential 150 — Problems You Must Know
Organized by topic in learning order:
Arrays and Hashing (10 problems)
| # | Problem | Difficulty | Pattern |
|---|---|---|---|
| 1 | Contains Duplicate | Easy | Hash set |
| 2 | Valid Anagram | Easy | Char count |
| 3 | Two Sum | Easy | Hash map |
| 4 | Group Anagrams | Medium | Key = sorted string |
| 5 | Top K Frequent Elements | Medium | Bucket sort / heap |
| 6 | Product of Array Except Self | Medium | Prefix/suffix |
| 7 | Valid Sudoku | Medium | Set per row/col/box |
| 8 | Encode/Decode Strings | Medium | Delimiter encoding |
| 9 | Longest Consecutive Sequence | Medium | Hash set |
| 10 | Sort Colors | Medium | Dutch National Flag |
Two Pointers (5 problems)
| # | Problem | Difficulty |
|---|---|---|
| 11 | Valid Palindrome | Easy |
| 12 | Two Sum II | Medium |
| 13 | 3Sum | Medium |
| 14 | Container With Most Water | Medium |
| 15 | Trapping Rain Water | Hard |
Sliding Window (6 problems)
| # | Problem | Difficulty |
|---|---|---|
| 16 | Best Time to Buy and Sell Stock | Easy |
| 17 | Longest Substring Without Repeating | Medium |
| 18 | Longest Repeating Character Replacement | Medium |
| 19 | Permutation In String | Medium |
| 20 | Minimum Window Substring | Hard |
| 21 | Sliding Window Maximum | Hard |
Binary Search (7 problems)
| # | Problem | Difficulty |
|---|---|---|
| 22 | Binary Search | Easy |
| 23 | Search a 2D Matrix | Medium |
| 24 | Koko Eating Bananas | Medium |
| 25 | Find Minimum in Rotated Sorted Array | Medium |
| 26 | Search in Rotated Sorted Array | Medium |
| 27 | Time Based Key-Value Store | Medium |
| 28 | Median of Two Sorted Arrays | Hard |
Trees (15 problems)
| # | Problem | Difficulty |
|---|---|---|
| 29 | Invert Binary Tree | Easy |
| 30 | Maximum Depth of Binary Tree | Easy |
| 31 | Diameter of Binary Tree | Easy |
| 32 | Balanced Binary Tree | Easy |
| 33 | Same Tree | Easy |
| 34 | Subtree of Another Tree | Easy |
| 35 | LCA of Binary Tree | Medium |
| 36 | Level Order Traversal | Medium |
| 37 | Right Side View | Medium |
| 38 | Count Good Nodes | Medium |
| 39 | Validate BST | Medium |
| 40 | Kth Smallest in BST | Medium |
| 41 | Construct Tree from Preorder+Inorder | Medium |
| 42 | Binary Tree Max Path Sum | Hard |
| 43 | Serialize/Deserialize Binary Tree | Hard |
The 14 Coding Patterns
Most LeetCode problems fall into 14 core patterns. Recognizing which pattern applies is the key skill:
Pattern 1 — Sliding Window
Trigger: "Contiguous subarray/substring with a condition"
Example
def sliding_window(arr, k):
window_sum = sum(arr[:k])
max_sum = window_sum
for i in range(k, len(arr)):
window_sum += arr[i] - arr[i-k]
max_sum = max(max_sum, window_sum)
return max_sum
Problems: Longest substring without repeat, minimum window substring, max sum subarray
Pattern 2 — Two Pointers
Trigger: "Sorted array, find pair/triplet with condition"
Example
def two_sum_sorted(arr, target):
l, r = 0, len(arr)-1
while l < r:
s = arr[l] + arr[r]
if s == target: return [l,r]
elif s < target: l += 1
else: r -= 1
return []
Pattern 3 — Fast/Slow Pointers
Trigger: "Linked list cycle detection, middle of list"
python example
def has_cycle(head):
slow = fast = head
while fast and fast.next:
slow = slow.next
fast = fast.next.next
if slow == fast: return True
return FalsePattern 4 — Merge Intervals
Trigger: "Overlapping intervals, scheduling problems"
Example
def merge(intervals):
intervals.sort(key=lambda x: x[0])
merged = [intervals[0]]
for start, end in intervals[1:]:
if start <= merged[-1][1]:
merged[-1][1] = max(merged[-1][1], end)
else:
merged.append([start, end])
return merged
Pattern 5 — Binary Search on Answer
Trigger: "Find minimum/maximum feasible value"
python example
def binary_search_answer(feasible_range, check_function):
lo, hi = feasible_range
while lo < hi:
mid = (lo + hi) // 2
if check_function(mid):
hi = mid # Try to do better
else:
lo = mid + 1
return loPattern 6 — BFS for Shortest Path
Trigger: "Minimum steps/moves in unweighted graph/grid"
Example
from collections import deque
def bfs_shortest(grid, start, end):
queue = deque([(start, 0)])
visited = {start}
while queue:
pos, dist = queue.popleft()
if pos == end: return dist
for next_pos in neighbors(pos):
if next_pos not in visited:
visited.add(next_pos)
queue.append((next_pos, dist+1))
return -1
Pattern 7 — DFS / Backtracking
Trigger: "All possible solutions, paths through a tree"
python example
def dfs_backtrack(choices, current, result):
if is_complete(current):
result.append(list(current))
return
for choice in choices:
current.append(choice)
dfs_backtrack(remaining(choices, choice), current, result)
current.pop()Pattern 8 — Dynamic Programming (1D)
Trigger: "Optimal value at each position depends on previous"
Example
def dp_1d(nums):
dp = [0] * len(nums)
dp[0] = nums[0]
for i in range(1, len(nums)):
dp[i] = max(nums[i], dp[i-1] + nums[i]) # Kadane's
return max(dp)
Pattern 9 — Dynamic Programming (2D)
Trigger: "Paths in grid, string comparison problems"
Example
def dp_2d(s1, s2):
m, n = len(s1), len(s2)
dp = [[0]*(n+1) for _ in range(m+1)]
for i in range(1, m+1):
for j in range(1, n+1):
if s1[i-1] == s2[j-1]:
dp[i][j] = dp[i-1][j-1] + 1
else:
dp[i][j] = max(dp[i-1][j], dp[i][j-1])
return dp[m][n]
Pattern 10 — Topological Sort
Trigger: "Course prerequisites, task scheduling with dependencies"
Example
from collections import deque
def topo_sort(n, edges):
in_degree = [0]*n
adj = [[] for _ in range(n)]
for u, v in edges:
adj[u].append(v); in_degree[v] += 1
queue = deque([i for i in range(n) if in_degree[i]==0])
order = []
while queue:
node = queue.popleft(); order.append(node)
for nei in adj[node]:
in_degree[nei] -= 1
if in_degree[nei] == 0: queue.append(nei)
return order if len(order) == n else []
Pattern 11 — Union Find (DSU)
Trigger: "Connected components, cycle detection in undirected graph"
Pattern 12 — Trie
Trigger: "Prefix search, autocomplete, word existence queries"
Pattern 13 — Monotonic Stack
Trigger: "Next greater/smaller element, histogram problems"
Pattern 14 — Heap / Priority Queue
Trigger: "Kth largest/smallest, merge k sorted arrays"
Company-Specific Focus Areas
Favors: Graph algorithms, trees, system design, hard DP Must-know: BFS/DFS on grids, advanced binary search, segment trees
Amazon
Favors: Practical problems, OOP design, leadership principle stories Must-know: Trees (especially BST), graphs, DP, sliding window
Meta (Facebook)
Favors: Arrays, strings, trees, graphs Must-know: BFS, DFS, sliding window, two pointers, dynamic programming
Microsoft
Favors: Classic DS problems, clean code Must-know: Full breadth of easy-medium problems
Bloomberg
Favors: Real-world financial data structures, system design Must-know: Heaps, trees, graph algorithms
Time Management in Interviews
| 5 min | Understand the problem, ask clarifying questions |
| 5 min | Discuss approach, verify with interviewer |
| 25 min | Implement the solution |
| 5 min | Test on examples, find bugs |
| 5 min | Discuss optimizations, trade-offs |
Weekly Study Plan
| Monday | Arrays / Hash Maps (2 problems) |
| Tuesday | Trees (2 problems) |
| Wednesday | Graphs - BFS/DFS (2 problems) |
| Thursday | Dynamic Programming (2 problems) |
| Friday | Strings / Sliding Window (2 problems) |
| Saturday | Contest (LeetCode weekly contest) |
| Sunday | Review week's hard problems, read editorials |
Target: 14 problems/week = ~700 problems in a year
Summary
LeetCode mastery comes from:
- Topic-based learning — learn the algorithm, then practice it
- Pattern recognition — 14 patterns cover 80% of all problems
- Spaced repetition — revisit problems after 3 days, 1 week, 1 month
- Contest participation — weekly contests simulate interview pressure
- Company-specific practice — focus on patterns your target company favors
The goal is not to memorize solutions but to recognize patterns instantly and implement them cleanly under pressure.
*Next Lesson: Codeforces Guide*
Exam Focus
Revise definitions, diagrams, examples, and short-answer points for LeetCode Guide — Complete Strategy to Crack FAANG Interviews.
Interview Use
Prepare one clear explanation, one practical example, and one common mistake for this Data Structures & Algorithms topic.
Search Terms
data-structures-algorithms, data structures & algorithms, data, structures, algorithms, competitive, programming, leetcode
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