Training Course · Indore

Data Structures & Algorithms

Learn Data Structures and Algorithms (DSA) at Placement Adda, Indore. Master arrays, linked lists, trees, graphs, sorting and dynamic programming with coding...

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Data Structures & Algorithms training course at Placements Adda Indore
Data Structures & AlgorithmsJob oriented training
Placement SupportInterview ready
Foundation of Software Engineering

Data Structures and Algorithms (DSA) Training in Indore

DSAData Structures and Algorithms (DSA) form the foundational pillar of computer science and software development. Data structures provide specialized techniques for organizing, managing, and storing information in memory efficiently, while algorithms provide step-by-step logic to process data and solve complex computational challenges.

At Placement Adda Bhawarkua Indore, we deliver an intensive, mentor-guided Data Structures and Algorithms course in Indore. Students gain comprehensive mastery over time-space complexity, recursive problem solving, dynamic programming, and placement interview coding rounds with practical implementations in Java, C++, and Python.

Why Master DSA at Placement Adda?

Cracking Coding Rounds − Master the exact problem-solving patterns tested by top product and IT recruiters including TCS, Infosys, Impetus, Cognizant, and high-growth startups.

250+ Curated Problems − Solve hand-picked coding questions benchmarked against top LeetCode, HackerRank, and GeeksforGeeks interview tracks.

Multi-Language Freedom − Write and optimize algorithms in your preferred programming language: Java Collections, C++ STL, or Python.

1:1 Whiteboard Mentoring − Participate in regular classroom problem-solving discussions and mock technical interview rounds in Indore.

Data Structures vs Algorithms

Data Structure: Represents the architectural blueprint of how data is stored, indexed, and navigated in computer memory (e.g. Arrays, Linked Lists, Trees, Graphs).
Algorithm: The step-by-step computational sequence executed on that data to achieve an optimal output (e.g. Binary Search, Merge Sort, Dijkstra's Shortest Path, Dynamic Programming).

Who Should Learn DSA?

Engineering Students (B.Tech / BE in CS, IT, EC) − Essential for 2nd, 3rd, and final-year campus placement rounds.

Graduates (MCA, BCA, B.Sc CS) − Build solid coding confidence to compete for lucrative software engineering roles.

Working Developers − Upgrade from service-oriented bug fixing to high-paying product company architectures.

Complexity Analysis

Master Big O, Omega, and Theta notations to write optimized code that passes tight judge time limits.

Multi-Language STL

Deep-dive into Java Collections Framework, C++ STL, and Python built-in data types.

Live Mock Coding

Whiteboard deconstruction, algorithmic trace trees, edge-case identification, and complexity proofs.

Placement Support

Company-wise questions from past campus recruitment drives with personalized mentor feedback.

Data Structures and Algorithms Course in Indore at Placement Adda
NEW BATCHES FORMING

DSA & Placement Accelerator

Bhawarkua, Indore | Offline Classroom & Coding Lab

Course Duration 3 to 4 Months (80-100 Hrs)
Implementation Tracks Java / C++ / Python
Batch Timings Regular Weekday & Weekend
Lab Practice 250+ Coding Problems
Doubt Resolution Daily 1:1 In-Person Mentoring
Credential Industry Course Certificate
Supported Coding Tracks

Learn conceptual algorithms and implement them in the language of your choice:

Java (Collections)
C++ (STL)
Python

Curriculum & Course Modules

view_list Linear Structures account_tree Trees & Graphs functions Algorithms & DP laptop_mac Java, C++ & Python video_label Mock Interviews & Lab
  • keyboard_arrow_rightWorst, Average & Best Case Analysis
  • keyboard_arrow_rightBig O, Big Omega & Big Theta Notations
  • keyboard_arrow_rightTime Complexity of Loops & Nested Structures
  • keyboard_arrow_rightAuxiliary Space vs Input Space Complexity
  • keyboard_arrow_rightMaster Theorem & Recurrence Relations
  • keyboard_arrow_rightMemory Layout & Dynamic Array Sizing
  • keyboard_arrow_rightTwo Pointer Technique & Reversal
  • keyboard_arrow_rightSliding Window Patterns (Fixed & Dynamic)
  • keyboard_arrow_rightKadane's Algorithm for Maximum Subarray
  • keyboard_arrow_rightDutch National Flag 3-Way Partitioning
  • keyboard_arrow_rightString Matching, Anagrams & Palindromes
  • keyboard_arrow_rightSingly, Doubly & Circular Linked Lists
  • keyboard_arrow_rightNode Insertion, Deletion & Traversal
  • keyboard_arrow_rightIterative & Recursive List Reversal
  • keyboard_arrow_rightFloyd's Tortoise & Hare Loop Detection
  • keyboard_arrow_rightFinding Middle Element & Intersection
  • keyboard_arrow_rightMerge K Sorted Linked Lists
  • keyboard_arrow_rightLIFO Stack Implementation & Evaluation
  • keyboard_arrow_rightMonotonic Stack for Next Greater Element
  • keyboard_arrow_rightFIFO Circular Queue & Deque Operations
  • keyboard_arrow_rightHash Functions & Collision Resolution
  • keyboard_arrow_rightLRU Cache Design with HashMap + DLL
  • keyboard_arrow_rightInorder, Preorder, Postorder Traversals
  • keyboard_arrow_rightBFS Level-Order Traversal with Queue
  • keyboard_arrow_rightTree Height, Depth & Diameter Calculation
  • keyboard_arrow_rightLowest Common Ancestor (LCA) in Binary Tree
  • keyboard_arrow_rightCheck if Tree is Balanced (AVL condition)
  • keyboard_arrow_rightBST Properties & Search Optimization
  • keyboard_arrow_rightInsertion & Node Deletion in BST
  • keyboard_arrow_rightValidate if Binary Tree is Valid BST
  • keyboard_arrow_rightKth Smallest / Largest Element in BST
  • keyboard_arrow_rightConstruct BST from Preorder Traversal
  • keyboard_arrow_rightComplete Binary Tree Array Representation
  • keyboard_arrow_rightMin Heap, Max Heap & Heapify Operation
  • keyboard_arrow_rightHeapSort In-Place Implementation
  • keyboard_arrow_rightTop K Frequent Elements in Array
  • keyboard_arrow_rightFind Median from Data Stream
  • keyboard_arrow_rightAdjacency Matrix & Adjacency List
  • keyboard_arrow_rightBreadth First Search (BFS) & Depth First Search (DFS)
  • keyboard_arrow_rightCycle Detection in Directed & Undirected Graphs
  • keyboard_arrow_rightTopological Sort (Kahn's Algorithm)
  • keyboard_arrow_rightDijkstra's Shortest Path & DSU Union-Find
  • keyboard_arrow_rightTrie Data Structure for Autocomplete
  • keyboard_arrow_rightBinary Search on Monotonic Sequences
  • keyboard_arrow_rightSearch in Rotated Sorted Array
  • keyboard_arrow_rightBinary Search on Answer (Book Allocation)
  • keyboard_arrow_rightMergeSort & Counting Inversions
  • keyboard_arrow_rightQuickSort with Lomuto & Hoare Partition
  • keyboard_arrow_rightRecursion Call Stack & Base Case Logic
  • keyboard_arrow_rightSubsets, Power Sets & Subsequences
  • keyboard_arrow_rightPermutations & Combinations Sum
  • keyboard_arrow_rightN-Queens Constraint Satisfaction
  • keyboard_arrow_rightSudoku Solver & Word Search on Matrix
  • keyboard_arrow_rightGreedy Choice Property & Proofs
  • keyboard_arrow_rightActivity Selection & Non-overlapping Intervals
  • keyboard_arrow_rightJob Sequencing with Deadlines
  • keyboard_arrow_rightFractional Knapsack Problem
  • keyboard_arrow_rightHuffman Data Compression Coding
  • keyboard_arrow_rightOverlapping Subproblems & Optimal Substructure
  • keyboard_arrow_rightMemoization (Top-Down) vs Tabulation (Bottom-Up)
  • keyboard_arrow_right1D DP: Climbing Stairs & House Robber
  • keyboard_arrow_right2D DP: 0/1 Knapsack & Coin Change
  • keyboard_arrow_rightLongest Common Subsequence (LCS)
  • keyboard_arrow_rightLongest Increasing Subsequence (LIS O(NlogN))
  • keyboard_arrow_rightArrayList & LinkedList Internals
  • keyboard_arrow_rightHashMap & TreeMap Collision Resolution
  • keyboard_arrow_rightPriorityQueue with Custom Comparators
  • keyboard_arrow_rightArrayDeque & Stack Implementation
  • keyboard_arrow_rightJava Generics & Memory Bounds
  • keyboard_arrow_rightstd::vector & Iterator Invalidation
  • keyboard_arrow_rightstd::unordered_map & Hash Functions
  • keyboard_arrow_rightstd::priority_queue & Greater Comparators
  • keyboard_arrow_rightPointers, References & Memory Footprint
  • keyboard_arrow_rightFast I/O & Competitive Programming Idioms
  • keyboard_arrow_rightPython Lists, Slicing & Amortized Cost
  • keyboard_arrow_rightcollections.deque for O(1) Double Queues
  • keyboard_arrow_rightheapq Module for Min Heap Operations
  • keyboard_arrow_rightDictionary Hashing & Set Intersections
  • keyboard_arrow_rightfunctools.lru_cache for DP Memoization
  • keyboard_arrow_right200+ Practice Problems on LeetCode
  • keyboard_arrow_rightHackerRank 5-Star Problem Solving Badge
  • keyboard_arrow_rightGeeksforGeeks Curated Company Archive
  • keyboard_arrow_rightWeekly Timed Virtual Coding Contests
  • keyboard_arrow_rightTCS Ninja & Digital Coding Rounds
  • keyboard_arrow_rightInfosys HackWithInfy & DSE Patterns
  • keyboard_arrow_rightImpetus & DXC Technical Assessment
  • keyboard_arrow_rightCognizant GenC Next & Wipro Elite
  • keyboard_arrow_rightThinking Aloud Problem Decomposition
  • keyboard_arrow_rightEdge Case Identification & Bounds
  • keyboard_arrow_rightDry Running Traces on Sample Data
  • keyboard_arrow_rightCalculating Big O Before Writing Code
  • keyboard_arrow_right45-Minute Timed Technical Coding Rounds
  • keyboard_arrow_rightSenior Mentor Rubric Evaluation
  • keyboard_arrow_rightActionable Bug & Optimization Feedback
  • keyboard_arrow_rightResume Tuning with DSA Projects & Badges

Frequently Asked Questions About DSA Training

Both Java and C++ are widely recognized and highly recommended for placement coding rounds due to their rich built-in libraries (Java Collections and C++ STL) and strong typing. Python is also supported for students targeting data science and automation roles. At Placement Adda, instructors demonstrate algorithms conceptually so you can implement them seamlessly in your preferred language.
The comprehensive Data Structures and Algorithms program typically spans 3 to 4 months (approximately 80 to 100 hours of classroom learning + 80+ hours of lab coding practice). We offer both regular weekday batches (1.5 hours daily) and weekend intensive batches for working candidates.
No advanced calculus or higher-level mathematics is required. A solid grasp of basic school-level arithmetic, logic, probability, and recurrence relations is sufficient. Our instructors explain all mathematical concepts, such as logarithms and permutations, from foundational scratch.
Yes. Every topic is accompanied by real questions asked in past campus hiring drives of companies like TCS, Infosys, Wipro, Cognizant, and product startups, alongside 1:1 whiteboard mock coding rounds.