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- Flow of the program
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- Time & Space Complexity
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3. Basics of Java
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3. [Basics of Java](https://www.javatpoint.com/java-tutorial)
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- Array
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- Introduction
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- Memory management
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- Subset Questions
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- Space and Time Complexity Analysis
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- Introduction
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- Comparisons of various cases
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- Solving Linear Recurrence Relations
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- Solving Divide and Conquer Recurrence Relations
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- Big-O, Big-Omega, Big-Theta Notations
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- Get equation of any relation easily - best and easiest approach
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- Complexity discussion of all the problems we do
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- Space Complexity
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- Memory Allocation of various languages
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- NP-Completeness and Hardness
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- Object Oriented Programming
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- Introduction
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- Classes & its instances
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- this keyword in Java
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- Properties
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- Inheritance
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- Abstraction
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- Polymorphism
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- Encapsulation
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- Overloading & Overriding
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- Static & Non-Static
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- Access Control
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- Interfaces
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- Abstract Classes
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- Singleton Class
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- final, finalize, finally
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- Exception Handling
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- Stacks & Queues
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- Introduction
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- Interview problems
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- Push efficient
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- Pop efficient
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- Queue using Stack and Vice versa
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- Circular Queue
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- Linked List
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- Introduction
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- Fast and slow pointer
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- Cycle Detection
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- Single and Doubly LinkedList
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- Reversal of LinkedList
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- Dynamic Programming
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- Introduction
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- Recursion + Recursion DP + Iteration + Iteration Space Optimized
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- Complexity Analysis
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- 0/1 Knapsack
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- Subset Questions
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- Unbounded Knapsack
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- Subsequence questions
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- String DP
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Introduction
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Comparisons of various cases
90-
Solving Linear Recurrence Relations
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Solving Divide and Conquer Recurrence Relations
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Big-O, Big-Omega, Big-Theta Notations
93-
Get equation of any relation easily - best and easiest approach
94-
Complexity discussion of all the problems we do
95-
Space Complexity
96-
Memory Allocation of various languages
97-
NP-Completeness and Hardness
98-
Object Oriented Programming
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100-
Introduction
101-
Classes & its instances
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this keyword in Java
103-
Properties
104-
Inheritance
105-
Abstraction
106-
Polymorphism
107-
Encapsulation
108-
Overloading & Overriding
109-
Static & Non-Static
110-
Access Control
111-
Interfaces
112-
Abstract Classes
113-
Singleton Class
114-
final, finalize, finally
115-
Exception Handling
116-
Stacks & Queues
117-
118-
Introduction
119-
Interview problems
120-
Push efficient
121-
Pop efficient
122-
Queue using Stack and Vice versa
123-
Circular Queue
124-
Linked List
125-
126-
Introduction
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Fast and slow pointer
128-
Cycle Detection
129-
Single and Doubly LinkedList
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Reversal of LinkedList
131-
Dynamic Programming
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Introduction
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Recursion + Recursion DP + Iteration + Iteration Space Optimized
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Complexity Analysis
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0/1 Knapsack
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Subset Questions
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Unbounded Knapsack
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Subsequence questions
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String DP
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Trees
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Introduction
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Binary Trees
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Binary Search Trees
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DFS
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BFS
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AVL Trees
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Segment Tree
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Fenwick Tree / Binary Indexed Tree
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Square Root Decomposition
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Heaps
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Introduction
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Theory
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Priority Queue
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Two Heaps Method
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k-way merge
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top k elements
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interval problems
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HashMap
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Introduction
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Theory - how it works
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Comparisons of various forms
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Limitations and how to solve
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Map using LinkedList
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Map using Hash
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Chaining
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Probing
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Huffman-Encoder
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Tries
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Graphs
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Introduction
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BFS
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DFS
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Working with graph components
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Minimum Spanning Trees
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Kruskal Algorithm
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Prims Algorithm
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Dijkstra’s shortest path algorithm
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Topological Sort
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Bellman ford
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A* pathfinding Algorithm
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- Trees
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- Introduction
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- Binary Trees
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- Binary Search Trees
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- DFS
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- BFS
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- AVL Trees
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- Segment Tree
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- Fenwick Tree / Binary Indexed Tree
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- Square Root Decomposition
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- Heaps
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- Introduction
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- Theory
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- Priority Queue
156+
- Two Heaps Method
157+
- k-way merge
158+
- top k elements
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- interval problems
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161+
- HashMap
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- Introduction
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- Theory - how it works
164+
- Comparisons of various forms
165+
- Limitations and how to solve
166+
- Map using LinkedList
167+
- Map using Hash
168+
- Chaining
169+
- Probing
170+
- Huffman-Encoder
171+
- Tries
172+
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- Graphs
174+
- Introduction
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- BFS
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- DFS
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- Working with graph components
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- Minimum Spanning Trees
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- Kruskal Algorithm
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- Prims Algorithm
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- Dijkstra’s shortest path algorithm
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- Topological Sort
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- Bellman ford
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- A* pathfinding Algorithm
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### What basic data structures and algorithms should one learn before starting competitive programming?
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1. Basic data sturctures (arrays, queues, linked lists, etc.).
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2. Bit manipulation.
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2. [Bit manipulation.](https://www.youtube.com/watch?v=5rtVTYAk9KQ&t)
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3. Advanced data structures:
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- Union-Find Disjoint Sets.
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- Segment Tree.

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