Software Interview Preparation Training

1. Hashmap

Learning Objectives:

Understand the concept and operations of a hashmap data structure, implement hashmaps using various programming languages, analyze the time complexity of hashmap operations, and comprehend their efficiency in average and worst-case scenarios. By the end of the module, you will be able to apply hashmaps to solve problems that necessitate fast key-value lookup or uniqueness, explore collision resolution techniques in hashmaps like chaining or open addressing, and understand the trade-offs between space complexity and hashmap performance.

Topics:

Design Hashmap
Missing Number
Binary Subarrays with Sum
Subarray Sums Divisible by K
Line Reflection
Binary Subarrays with Sum
Make Sum Divisible by P
Subarray Sum Equals K
Simplified Fractions
Count Number of Nice Subarrays
Insert Delete Getrandom 01
Count Triplets That Can Form Two Arrays of Equal XOR
Subarray Sums Divisible By K
Find Two Non-Overlapping Sub Arrays Each with Target Sum
Minimum Operations to Reduce X To Zero
Partition Labels
Task Scheduler
Longest Arithmetic Subsequence
Brick Wall
Flip String to Monotone Increasing
Product Of the Last K Numbers
Count Ways to Make Array with Product
Smallest Range Covering Elements from K Lists

2. Linked List

Learning Objectives:

Understand the concept and structure of linked lists, implement them in various programming languages, analyze the time and space complexity of linked list operations, and compare and contrast linked lists with other data structures. By the end of the module, you will be able to apply linked lists to solve algorithmic problems efficiently and explore advanced variations of linked lists, such as doubly linked lists and circular linked lists.

Topics:

Reverse Linked List
Linked List Cycle
Delete Node in Linked List
Palindrome Linkedlist
Intersection of Two Linked List
Linked List Cycle 2
Remove Nth Node from End of List
LRU Cache
Copy List with Random Pointer
Swap Nodes in Pairs
Linked List Random Node

3. Queue

Learning Objectives:

On completing the queue module, you will gain an understanding of the concept and operations of a queue data structure. You will be able to implement queues using various programming languages and analyze the time and space complexity of queue operations. Additionally, you will have the ability to apply queues to solve problems requiring a first-in, first-out (FIFO) approach, compare and contrast queues with other data structures, and explore advanced queue variations such as priority queues and circular queues.

Topics:

Binary Tree Right Side View
Jump Game IV
Maximum Sum Circular Subarray

4. Stack

Learning Objectives:

Gain a comprehensive understanding of the concept and operations of a stack data structure. You will be able to implement stacks using various programming languages and analyze the time and space complexity of stack operations. Acquire the skills to apply stacks to efficiently solve problems that follow a last-in, first-out (LIFO) approach, compare and contrast stacks with other data structures, and explore advanced stack variations, including the implementation of stack-based algorithms or stack-based memory management.

Topics:

Largest Rectangle in Histogram
Maximum Frequency Stack
Remove All Adjacent Duplicates in String II
Minimum Remove to Make Valid Parentheses
Next Greater Element II
Design a Stack with Increment Operation
Basic Calculator II
Score of Parentheses
Find the Most Competitive Subsequence

5. Array

Learning Objectives:

Understand arrays as efficient memory structures for storing elements, accessing and manipulating them through indexing and operations, and analyzing the time complexity of common operations like insertion, deletion, and searching. Apply arrays to solve data storage and retrieval problems, explore multidimensional arrays for problems with multiple dimensions, and recognize their limitations such as fixed size and memory allocation, considering alternative data structures when needed.

Topics:

Count Negative Numbers in a Sorted Matrix
Best Time to Buy and Sell Stock II
Squares of a Sorted Array
Sum of All Odd Length Subarrays
Missing Number
Search in Rotated Sorted Array
Minimum Jumps to Reach Home
Ways to Split Array into Three Subarrays
Number of Boomerangs
Find the Smallest Divisor Given a Threshold
Single Element in a Sorted Array
As Far from Land as Possible
Maximum Sum Circular Subarray
Maximum Product Subarray
Max Value of Equation

6. Strings

Learning Objectives:

Understand strings as character sequences, manipulate them through operations like concatenation, substring extraction, and character searching, and analyze the time complexity of string operations. Apply string algorithms to solve problems involving matching, parsing, or manipulation. Explore string manipulation libraries and functions in different programming languages, understand the significance of string encoding, and consider internationalization considerations for handling strings efficiently.

Topics:

Count Binary Substrings
Add Strings
Permutation in String
Minimum Remove to Make Valid Parentheses
Zigzag Conversion
Basic Calculator II
Score of Parentheses
Reorganize String
Next Greater Element III
Partition Labels
Longest Valid Parentheses
Prefix and Suffix Search

7. Two Pointers

Learning Objectives:

Understand the principles of the Two Pointers technique, apply it to efficiently solve problems involving array or list traversal, and identify and implement the necessary pointers for effective position tracking and element manipulation. Analyze the time and space complexity of algorithms utilizing the Two Pointers approach, optimize algorithms by leveraging the Two Pointers technique to reduce time complexity, and effectively solve a variety of algorithmic problems by leveraging the Two Pointers technique.

Topics:

Permutation in String
Longest Repeating Character Replacement
Container with Most Water
Next Permutation
3 Sum
3 Sum Smaller
Number of Subsequences That Satisfy the Given Sum Condition
Ways To Split Array into Three Subarrays
Minimum Operations to Reduce X To Zero
Valid Triangle Number
3 Sum Closest
Subarrays With K Different Integers
Find Median from Data Stream
Trapping Rain Water

8. Binary Search

Learning Objectives:

Understand the principles of Binary Search, implement the Binary Search algorithm in multiple programming languages, analyze the time complexity of Binary Search and its advantages over linear search. Apply Binary Search to efficiently find elements in sorted arrays or lists, handle edge cases and understand the necessary conditions for Binary Search to function correctly, and explore variations of Binary Search.

Topics:

Count Negative Numbers in a Sorted Matrix
Binary Search
Search In Rotated Sorted Array
Unique Binary Search Trees
Number Of Subsequences That Satisfy the Given Sum Condition
Ways To Split Array into Three Subarrays
Find The Smallest Divisor Given a Threshold
Single Element in a Sorted Array
Count Complete Tree Nodes
Longest Increasing Subsequence
Longest Arithmetic Subsequence
Search a 2D Matrix
Search a 2D Matrix II

9. Recursion

Learning Objectives:

Understand recursion and its role in problem-solving, implement recursive algorithms in various domains, analyze the time and space complexity of recursive algorithms, and identify base cases and recursive cases when designing recursive solutions. Explore the relationship between recursion and mathematical induction, and apply recursive thinking to break down complex problems into smaller, manageable subproblems.

Topics:

Employee Importance
Minimum Distance Between BST Nodes
Nested List Weight Sum
Balanced Binary Tree
Pow X N
Kth Symbol in Grammar
Nested List Weight Sum II
Longest Univalue Path
Swap Nodes in Pairs
Predict the Winner
Minimum Cost Tree from Leaf Values
Expression Add Operators
Word Break II

10. Sorting and Searching

Learning Objectives:

Understand and compare different sorting algorithms (Bubble Sort, Insertion Sort, Merge Sort, Quick Sort) and their time complexities. Learn to implement efficient sorting algorithms for ordering elements in arrays or lists, explore searching algorithms (Linear Search, Binary Search) and their time complexities, apply searching algorithms to efficiently locate elements in collections (sorted or unsorted), analyze performance trade-offs between sorting and searching algorithms, and understand and implement advanced sorting techniques like Radix Sort or Heap Sort.

Topics:

Count Negative Numbers in a Sorted Matrix
Squares of a Sorted Array
Wiggle Sort
Sort Colors
Distant Barcodes
H Index
H Index II
Leftmost Column With At Least a One
Sell Diminishing Valued Colored Balls
3 Sum
3 Sum Smaller
Number of Subsequences That Satisfy the Given Sum Condition
Maximum Gap
Find Median from Data Stream
Maximum Performance of a Team
Minimum Cost to Hire K Workers
Russian Doll Envelopes
Maximum Profit in Job Scheduling
Least Number of Unique Integers After K Removals
Reorganize String

11. Trees

Learning Objectives:

Understand trees as a fundamental data structure and their properties, implement tree data structures and traversal algorithms in programming languages, and analyze the time complexity of tree operations (insertion, deletion, searching). Apply trees to efficiently solve problems with hierarchical relationships or organized data, explore various types of trees (binary trees, binary search trees, balanced trees), and understand advanced tree concepts including tree balancing techniques such as AVL trees or Red-Black trees.

Topics:

Diameter of Binary Tree
Balanced Binary Tree
Second Minimum Node in a Binary Tree
Sum of All Odd Length Subarrays
Symmetric Tree
Complete Binary Tree Inserter
Implement Trie (Prefix Tree)
Unique Binary Search Trees
Binary Tree Vertical Order Traversal
Maximum Difference Between Node and Ancestor
Count Complete Tree Nodes
Binary Tree Right Side View
Word Search II

12. Tree Breadth-First Search (BFS)

Learning Objectives:

Understand the concept and mechanics of Breadth-First Search (BFS) in trees, implement a specialized BFS algorithm for tree traversal, and analyze the time and space complexity of Tree BFS. Apply Tree BFS to efficiently traverse and explore tree structures, utilize Tree BFS to solve problems like finding level order traversal or determining the width of a tree, and understand the differences and similarities between Tree BFS and Graph BFS algorithms.

Topics:

Cut Off Trees for Golf Event
Closest Leaf in a Binary Tree

13. Priority Queue

Learning Objectives:

Understand the concept and operations of a priority queue data structure, implement a priority queue in various programming languages, analyze the time complexity of priority queue operations and their efficiency across different implementations, and apply priority queues to solve problems involving prioritization of elements. Explore various implementations of priority queues such as binary heaps or Fibonacci heaps and their trade-offs, and understand the relationship between priority queues and other data structures like heapsort or graph algorithms such as Dijkstra's algorithm.

Topics

Last Stone Weight
Find K Pairs with Smallest Sums
Top K Frequent Elements
Swap Adjacent in Lr String
Kth Smallest Element in a Sorted Matrix
Top K Frequent Words
Minimum Cost to Connect Sticks
Split Array into Consecutive Subsequences
Kth Largest Element in an Array
K Closest Points to Origin
Kth Smallest Number in Multiplication Table
Trapping Rain Water
Merge K Sorted Lists
Kth Smallest Prime Fraction
Cheapest Flights Within K Stops
Reorganize String
Network Delay Time
Jump Game VI
Path With Maximum Probability

14. Sliding Window

Learning Objectives:

Understand the concept and mechanics of the Sliding Window technique, apply it to efficiently solve problems involving subarray or substring operations, and identify and implement the necessary data structures such as arrays or queues to implement the Sliding Window technique. Analyze the time and space complexity of algorithms, optimize the algorithms to reduce redundant computations, and effectively solve a variety of algorithmic problems by leveraging the Sliding Window technique.

Topics

Maximum Average Subarray
Permutation in String
Maximum Points You Can Obtain from Cards
Binary Subarrays with Sum
Number of Sub Arrays of Size K and Average Greater Than or Equal to Threshold
Number of Substrings Containing All Three Characters
Replace the Substring for Balanced String
Fruit into Baskets
Sliding Window Maximum
Minimum Swaps to Group All 1's Together
Longest Repeating Character Replacement
Longest Continuous Subarray with Absolute Diff Less Than or Equal to Limit
Minimum Size Subarray Sum
Max Consecutive Ones III
Longest Substring with at Most K Distinct Characters
Maximum Average Subarray I
Longest Turbulent Subarray
Subarrays with K Different Integers
Arithmetic Slices
Jump Game VI
Longest Substring with at Most Two Distinct Characters
Shortest Subarray with Sum At Least K
Minimum Window Substring
Max Value of Equation
Subarrays With K Different Integers

15. Graphs

Learning Objectives:

Understand and implement graph data structures and traversal algorithms, analyze their time and space complexity, and apply them to solve problems related to connectivity, shortest path, and traversal. Explore different graph representations and their trade-offs, and apply graph algorithms to real-world scenarios like network routing or social network analysis.

Topics

Find the Town Judge
Network Delay Time
House Robber
Walls and Gates
Number of Distinct Islands
Detect Cycles In 2d Grid
Count Servers that Communicate
House Robber III
Open the Lock
Shortest Path with Alternating Colors
Shortest Bridge
Number of Closed Islands
Find Eventual Safe States
Tree Diameter
01 Matrix
Cheapest Flights Within K Stops
Find The Celebrity
Minimum Number of Vertices to Reach All Nodes
Is Graph Bipartite?
Network Delay Time
Number Of Operations to Make Network Connected
Minimum Height Trees
Path With Maximum Probability
Longest Consecutive Sequence
Alien Dictionary
Sort Items by Groups Respecting Dependencies

16. Depth-First Search (DFS)

Learning Objectives:

Understand and implement Depth-First Search (DFS) in multiple programming languages, analyze its time and space complexity, and apply DFS to traverse and explore graphs and trees. Identify and solve problems suited for DFS, and comprehend its relationship with other graph traversal algorithms like Breadth-First Search (BFS).

Topics

Flood Fill
Surrounded Regions
Pacific Atlantic Water Flow
Is Graph Bipartite
Cheapest Flights Within K Stops
Binary Tree Right Side View
House Robber III
Shortest Bridge
Number of Provinces
Network Delay Time
Jump Game III
Jump Game
Maximum Difference Between Node and Ancestor
Number Of Operations to Make Network Connected
Minimum Height Trees
Balanced Binary Tree
Symmetric Tree

17. Breadth-First Search (BFS)

Learning Objectives:

Implement Breadth-First Search (BFS) in multiple programming languages, analyze its time and space complexity, and apply BFS to traverse and explore graphs and trees. Identify and solve problems well-suited for BFS, and understand its relationship with other graph traversal algorithms like Depth-First Search (DFS).

Topics

Word Ladder
Perfect Squares
As Far from Land as Possible
Rotten Oranges

18. Graph Breadth-First Search

Learning Objectives:

Understand the concept and mechanics of Breadth-First Search (BFS) in graphs, implement BFS algorithms in multiple programming languages, analyze the time and space complexity of BFS, and apply BFS to efficiently traverse and explore graphs. Utilize BFS to solve problems like finding the shortest path or detecting connected components in a graph, and gain an understanding of the differences and similarities between BFS and other graph traversal algorithms, such as Depth-First Search (DFS).

Topics

Cheapest Flights Within K Stops
Binary Tree Right Side View
Surrounded Regions
Pacific Atlantic Water Flow
Perfect Squares
Rotting Oranges
Shortest Bridge
Is Graph Bipartite?
Network Delay Time
Jump Game III
Minimum Jumps to Reach Home
Number Of Operations to Make Network Connected
Minimum Height Trees
As Far from Land as Possible
Hard Breadth First Search
Sort Items by Groups Respecting Dependencies
Word Ladder

19. Graph Topological Sort

Learning Objectives:

Understand the concept and significance of Topological Sort in directed acyclic graphs (DAGs), implement algorithms like Depth-First Search (DFS) or Breadth-First Search (BFS) to perform Topological Sort, and identify the applications of Topological Sort in task scheduling or determining dependencies in a project, analyze the time and space complexity of Topological Sort algorithms. Apply Topological Sort to solve problems involving ordering or sequencing constraints, and gain an understanding of the relationship between Topological Sort and other graph algorithms, such as detecting cycles or finding shortest paths.

Topics

Minimum Height Trees
Sort Items by Groups Respecting Dependencies
Alien Dictionary

20. Divide and Conquer

Learning Objectives:

Understand the concept and principles of the Divide and Conquer algorithmic paradigm, apply it to efficiently solve problems by breaking them into smaller subproblems, analyze the time complexity of Divide and Conquer algorithms using recurrence relations, and identify and implement an appropriate divide and combine steps for different problem domains. Utilize recursion and merge techniques in implementing Divide and Conquer algorithms, and solve a variety of algorithmic problems effectively by leveraging the Divide and Conquer approach.

Topics

Majority Element
Best Time to Buy and Sell Stock
Reverse Pairs
The Skyline Problem
Count Of Smaller Numbers After Self
Best Time to Buy and Sell Stock III
Median Of Two Sorted Arrays
Beautiful Array
Different Ways to Add Parentheses
Best Time to Buy and Sell Stock II

21. Backtracking

Learning Objectives:

Understand the concept and principles of backtracking as a problem-solving technique, apply it to solve problems involving exhaustive search and constraint satisfaction, and implement necessary strategies such as pruning and optimization to improve backtracking efficiency. Analyze the time complexity of backtracking algorithms and identify applicable cases, utilize recursion and backtracking to explore and generate all possible solutions to a problem, and effectively solve a variety of algorithmic problems by leveraging the backtracking approach.

Topics

Subsets
Combination Sum III
Subsets II
Factor Combinations
Combination Sum II
Letter Combinations of a Phone Number
Generate Parentheses
Combination Sum
Combinations
Word Search II

22. Connected Components

Learning Objectives:

Understand the concept and importance of connected components in graph theory, identify and classify connected components in a given graph, and implement algorithms like Depth-First Search (DFS) or Breadth-First Search (BFS) to find connected components. Analyze the time and space complexity of connected component algorithms, apply connected component analysis to solve problems related to graph connectivity, and explore the relationship between connected components and other graph properties such as strongly connected components or bipartite graphs.

Topics

Number Of Provinces
Number Of Operations to Make Network Connected
Smallest String with Swaps.
Regions Cut by Slashes

23. Bit Manipulation

Learning Objectives:

Understand the concept and importance of connected components in graph theory, identify and classify connected components in a given graph, and implement algorithms like Depth-First Search (DFS) or Breadth-First Search (BFS) to find connected components. Analyze the time and space complexity of connected component algorithms, apply connected component analysis to solve problems related to graph connectivity, and explore the relationship between connected components and other graph properties such as strongly connected components or bipartite graphs.

Topics

Number Of Provinces
Number Of Operations to Make Network Connected
Smallest String with Swaps.
Regions Cut by Slashes

24. Bit Manipulation

Learning Objectives:

Understand binary representation and bitwise operations, perform basic bit manipulation operations like bitwise AND, OR, XOR, and bit shifting, utilize bit manipulation techniques for code optimization and problem-solving involving bitmasking and bit masking. Apply bit manipulation for arithmetic operations using bitwise operations, explore advanced bit manipulation techniques such as bit counting and reversing bits, analyze the time and space complexity of bit manipulation operations, and understand the practical applications of bit manipulation in cryptography and low-level programming.

Topics

Number of 1 Bits
Counting Bits
Count Triplets That Can Form Two Arrays of Equal XOR

25. Dynamic Programming

Learning Objectives:

Understand Dynamic Programming as an algorithmic paradigm, apply it to efficiently solve problems by breaking them into overlapping subproblems, and identify and define recurrence relations or state transitions for Dynamic Programming problems. Utilize memorization or tabulation techniques to optimize subproblem computation, analyze the time and space complexity of Dynamic Programming algorithms, and effectively solve a variety of algorithmic problems using the Dynamic Programming approach.

Topics

Cheapest Flights Within K Stops
Target Sum
House Robber III
Perfect Squares
Longest Happy String
Predict the Winner
Unique Binary Search Trees
Jump Game II
Jump Game VI
Minimum Jumps to Reach Home
Minimum Sideway Jumps
Maximum Absolute Sum of Any Subarray
Find Two Non-Overlapping Sub Arrays Each with Target Sum
As Far from Land as Possible
Maximum Sum Circular Subarray
Maximum Product Subarray
Minimum Path Sum
Longest Common Subsequence
Minimum Number of Refueling Stops
Best Time to Buy and Sell Stock III
Stone Game III
Russian Doll Envelopes
Edit Distance

26. Geometry

Learning Objectives:

Understand geometry's fundamental concepts, including points, lines, angles, and shapes, and apply geometric formulas and theorems to solve problems related to areas, perimeters, and angles of various figures. Analyze geometric properties and relationships between shapes, utilize coordinate geometry to describe and manipulate points and shapes on a coordinate plane, explore geometric transformations, and apply geometric concepts to solve real-world problems.

Topics

Island Perimeter
Surface Area of 3D Shapes
Rectangle Area
Minimum Area Rectangle II
Minimum Area Rectangle
Valid Square
Rectangle Overlap
Valid Boomerang
Largest Perimeter Triangle
Max Points on a Line
Line Reflection
Circle And Rectangle Overlapping
Maximum Number of Visible Points
Erect the Fence
Erect the Fence II
Rectangle Area II
Perfect Rectangle
Maximum Number of Darts Inside of a Circular Dartboard

27. Math

Learning Objectives: Understand and apply fundamental mathematical concepts, including arithmetic, algebra, geometry, and calculus, and analyze and interpret mathematical data. Learn to utilize mathematical reasoning and critical thinking skills to solve problems, explore mathematical modeling and its real-world applications, and develop computational and problem-solving skills through mathematical exercises and practice.

Topics

Missing Number
Excel Sheet Column Number
Palindrome Number
Reverse Integer
Prime Arrangements
Factorial Trailing Zeroes
Global and Local Inversions
Check If Array Pairs are Divisible by K
Count Numbers with Unique Digits
Reach a Number
Rotate Image
Count Primes
Sparse Matrix Multiplic
Fraction Addition and Subtraction
Minimum Factorization
Multiply Strings
Rectangle Area
Perfect Squares
Maximum Swap
Predict the Winner
Next Permutation
Unique Binary Search Trees
Count Triplets That Can Form Two Arrays of Equal XOR
Integer Break
Prime Arrangements
Permutation Sequence
Remove 9
Consecutive Numbers Sum
Number of Digit One
Orderly Queue
Stone Game III
Three Equal Parts

28. Union Find

Learning Objectives:

Understand the concept and operations of the Union-Find data structure, implement it using various techniques, and analyze its time complexity. Apply it to solve connectivity and disjoint set problems, explore optimization techniques for improved performance, and understand its relationship with other graph algorithms such as Kruskal's algorithm for minimum spanning trees.

Topics

Redundant Connection
Surrounded Regions
Number of Provinces
Smallest String with Swaps
Number of Islands
Friend Circles
Evaluate Division
Accounts Merge
Sentence Similarity II
Longest Consecutive Sequence
Making a Large Island
Shortest Path Visiting All Nodes
Swim in Rising Water

29. Greedy

Learning Objectives:

Understand the concept and principles of the Greedy algorithmic paradigm, apply it to solve optimization problems, and analyze problem properties suitable for Greedy algorithms. Identify and implement strategies for designing and optimizing Greedy algorithms, explore their applications in different problem domains, and understand their trade-offs and limitations compared to other approaches.

Topics

Best Time to Buy and Sell Stock II
Sell Diminishing Valued Colored Balls
Car Pooling
Reorganize String
Longest Happy String
Gas Station
Monotone Increasing Digits
Jump Game
Jump Game II
Find The Most Competitive Subsequence
Minimum Sideway Jumps
Task Scheduler

30. Design and Coding

Learning Objectives:

Understand software design principles and apply them to create modular and reusable code, analyze requirements and use design patterns to design solutions. Implement clean and maintainable code, test and debug for correctness and robustness, and collaborate with team members to create scalable and efficient software solutions.

Topics

Design a Stack with Increment Operation
Product of the Last K Numbers
All O`One Data Structure
Implement Trie (Prefix Tree)
My Calendar I
Largest Rectangle in Histogram
Stone Game III
Three Equal Parts
Making a Large Island
Maximum Performance of a Team
Minimum Cost to Hire K Workers
Russian Doll Envelopes
Maximum Profit in Job Scheduling

31. Segment Tree

Learning Objectives:

Understand segment trees as a data structure for efficient interval querying and updating, and implement them in various programming languages. Learn to analyze their time complexity, apply them to solve interval-based query problems, explore advanced techniques like lazy propagation, and understand their relationship with other data structures such as binary indexed trees or range minimum query structures.

Topics

My Calendar I
My Calendar III

32. Ordered Set

Learning Objectives:

Understand and implement ordered sets, analyze their time complexity for operations like insertion, deletion, and searching, apply them to solve problems requiring sorted collections, and explore advanced variations like balanced binary search trees. Understand the trade-offs between ordered sets and other data structures such as arrays or hash-based sets.

Topics

My Calendar I
My Calendar III

33. Game Theory

Learning Objectives:

Understand and apply the principles of Game Theory to model and analyze strategic interactions in various types of games, and analyze strategies and equilibria using concepts like dominant strategies and Nash equilibrium. Explore solution concepts such as minimax and mixed strategies, apply game theory to real-world scenarios, and analyze the impact of factors like information asymmetry and repeated interactions on game outcomes.

Topics

Predict The Winner
Stone Game III

34. Monotonic Stack

Learning Objectives:

Understand the concept and properties of a monotonic stack data structure, implement it in different programming languages, analyze its time complexity, and apply it to solve problems involving the nearest greater/smaller elements or sliding windows. Explore advanced applications such as histograms or circular arrays, and understand their relationship with other data structures like monotonic queues or dynamic programming techniques.

Topics

Find The Most Competitive Subsequence

35. System Design - I

Learning Objectives:

Understand fundamental principles of system design, analyze requirements, apply architectural patterns, select technologies, and design reusable components. Learn to optimize performance, ensure fault tolerance and security, make informed decisions, and collaborate with stakeholders for successful system implementation.

Topics

9 Steps to System Design
High-Level System Design
Communication Channels
API Design Best Practices
Database Design Best Practices
DB Choices
Caching
Message Queue Design
Realtime Websockets, SSE etc.
Doubt Solving

36. Mastering System Design – II

Learning Objectives:

Learn resilient system design to withstand failures and protect data during disasters. Explore data sharding for efficient data storage distribution. Implement transactional systems for consistent and reliable inventory management, akin to Zulily. Design secure messaging apps like WhatsApp and develop DDOS prevention services. Enhance user experiences by creating features like pause in platforms such as Netflix. Gain expertise in building scalable platforms like Uber, YouTube, and Spotify to cater to diverse user needs.

Topics

HA and Disaster recovery
Sharding
Transaction Zulily Inventory Management System
Design Whatsapp
DDOS prevention service
Netflix pause feature
Design Uber
Design YouTube
Design Spotify

37. Mock Interviews - I

This session is designed to help you get prepared for technical interviews and increase your chances of securing job offers at top technology companies. Through comprehensive training, you will develop a strong foundation in technical concepts, sharpen your problem-solving skills, and gain the confidence needed to excel in challenging and competitive interviews. Benefit from effective interview strategies by experts who've been there and done that. By the end of this session, you will be equipped with the necessary skills, knowledge, and mindset to succeed in technical interviews and stand out as a top candidate in the job market.

Topics

Evaluation for System Design Module
Behavioural Learning
Resume Review
Behavioural Mock Interview - I
Technical Mock Interview – I

38. Mock Interviews – II

In this session, take your interview preparation to the next level and further enhance your skills for securing job offers at top technology companies. Building upon the foundation established in previous sessions, this session will provide you with more challenging interview scenarios, simulate real-world interview conditions, and focus on refining your problem-solving techniques.

Through intensive practice, personalized feedback, and targeted coaching, you will gain the ability to tackle complex technical problems with ease, effectively communicate your thought process, and demonstrate your expertise to potential employers. By participating in these Mock Interviews with industry experts who know what it takes to get into top tech companies, you will develop the resilience, adaptability, and confidence necessary to excel in even the most demanding technical interviews and position yourself as a standout candidate in the competitive job market.

Topics

Technical Mock Interview - II
Behavioural Mock Interview - II
Technical Mock Interview - III
Technical Mock Interview - IV

Software Interview Preparation Training