Data Structures and Algorithms Training

RexGalaxy Academy's Data Structures and Algorithms Training is a practical coding interview and placement preparation program designed for students, freshers and developers who want to build strong problem-solving ability for software developer, coding interview and online assessment roles.

This course covers the complete DSA learning path from programming logic and complexity analysis to advanced interview problem patterns. Students learn coding setup, online judge workflow, dry runs, time and space complexity, arrays, strings, prefix logic, two pointers, sliding window, recursion, backtracking, linked lists, stacks, queues, hashing, trees, binary search trees, heaps, priority queues, graphs, greedy algorithms, dynamic programming, bit manipulation, interview templates, timed contests and portfolio-ready problem documentation.

The training approach is based on learning by doing. Learners solve 150+ problems with concept explanation, whiteboard dry runs, code implementation, complexity analysis, alternate approaches, mentor review, timed practice, mock interviews and repeated placement-style problem solving.

Students should be able to:

• Read coding problems correctly by identifying inputs, outputs, constraints, edge cases and hidden assumptions.

• Choose suitable data structures and algorithms instead of guessing or memorizing solutions blindly.

• Solve high-frequency interview problems using arrays, strings, recursion, linked lists, stacks, queues, hashing, trees, graphs and dynamic programming.

• Explain brute force, optimized approach, dry run, code logic and time-space complexity confidently.

• Build a solved problem portfolio with pattern notes, complexity sheets, revision checklists, coding templates and mock interview evidence.

• Prepare for software developer roles, coding rounds, online assessments and technical interviews with placement-ready confidence.

Build the foundation for coding interviews with logic, tools, analysis and clean practice habits.

• Set up a practical programming workflow with editor setup, online judge use, input-output formats, code templates and clean file organization.

• Build logic using variables, conditions, loops, nested loops, pattern thinking, trace tables and step-by-step dry runs.

• Learn problem reading by identifying inputs, outputs, constraints, examples, edge cases and hidden assumptions before coding.

• Understand time complexity including constant, linear, logarithmic, quadratic, nested loops, recursion cost and comparison of possible solutions.

• Understand space complexity including auxiliary memory, arrays, recursion stack, in-place solutions and memory-aware decision making.

• Develop debugging habits such as print tracing, breakpoint awareness, failing test isolation, boundary checks and systematic correction.

• Review mathematical basics including divisibility, prime checks, GCD, LCM, modular arithmetic awareness, counting and simple combinatorics.

• Build coding discipline through readable names, reusable helper functions, useful comments and avoiding hard-coded assumptions.

• Practice drills include basic math problems, loop problems, pattern problems, constraints analysis and complexity classification.

• Outcome: confidently understand a coding problem, plan an approach, estimate complexity and write clean starter solutions.

Solve common placement problems using arrays, strings, prefix logic, sliding windows and pointer movement.

• Learn array fundamentals including indexing, traversal, insertion idea, deletion idea, searching, updating, copying and boundary conditions.

• Practice string fundamentals such as character access, substrings, frequency counting, comparison, immutability awareness and parsing basics.

• Use prefix and suffix techniques including prefix sums, running totals, range queries, suffix arrays concept and contribution thinking.

• Apply two pointer patterns including opposite direction scan, same direction scan, sorted array logic, pair sums, duplicates and window boundaries.

• Solve sliding window problems using fixed window, variable window, longest and shortest window, frequency windows and shrinking conditions.

• Practice Kadane and subarray logic including maximum sum, negative handling, reset condition and related subarray problems.

• Work with matrix basics such as row-column traversal, diagonals, transpose, rotation awareness, search patterns and grid edge cases.

• Solve common interview problems including reverse, rotate, merge intervals awareness, missing number, majority element, anagram, palindrome and pattern matching basics.

• Labs include array and string sets with dry runs, complexity notes, alternate approaches and final optimized code.

• Outcome: solve high-frequency array and string problems with correct boundaries, clean complexity and interview-ready explanation.

Understand recursive thinking and solve search-space problems with controlled exploration and pruning.

• Learn the recursion model including base case, recursive call, call stack, return flow, state variables and tracing recursive execution.

• Practice recursion patterns such as factorial, power, digit problems, array recursion, string recursion and recursion with indices.

• Understand backtracking fundamentals including choose, explore, unchoose, state restoration, visited marking and pruning invalid paths.

• Generate subsets and combinations using include-exclude logic, duplicate handling, sorted input awareness and clean result sets.

• Solve permutation problems with swapping approach, used-array approach, duplicate avoidance and factorial complexity reasoning.

• Practice grid and path problems such as rat in maze style movement, directional movement, visited cells, boundary checks and obstacle handling.

• Review classic problems including N-Queens awareness, Sudoku awareness, word search, parentheses generation and partitioning problems.

• Understand divide and conquer through split-solve-merge thinking, binary search relation, merge sort, quick sort concept and recurrence thinking.

• Labs include recursion trees, backtracking sets, brute force versus pruned search comparison and complexity documentation.

• Outcome: design recursive solutions, control state safely and explain backtracking decisions in interviews.

Master linear data structures used in interviews, memory-based reasoning and common implementation patterns.

• Understand linked list structure including nodes, data, next pointer, head, tail, traversal and difference from arrays.

• Practice singly linked list operations such as insertion, deletion, search, length, reversal and middle element detection.

• Solve two-pointer linked list problems including slow-fast pointer, cycle detection, kth node, merge lists and intersection awareness.

• Learn stack concepts including LIFO behavior, push, pop, top, empty checks and use cases for reverse, parsing and previous/next elements.

• Solve stack problems such as valid parentheses, monotonic stack awareness, next greater element, stock span and expression evaluation basics.

• Learn queue concepts including FIFO behavior, enqueue, dequeue, front, rear, circular queue awareness and BFS preparation.

• Practice deque awareness for sliding window maximum, double-ended operations and efficient window management.

• Build implementation discipline with null checks, pointer safety, underflow/overflow awareness and dry-run diagrams.

• Labs include linked list reversal, cycle detection, stack expression problems, queue simulation and monotonic stack practice.

• Outcome: implement and use linked lists, stacks and queues confidently in placement-level problems.

Use hashing to solve lookup, duplicate, counting and pattern-matching problems efficiently.

• Understand hashing purpose for fast lookup, frequency counting, duplicate detection, mapping relationships and reducing nested loops.

• Use maps for key-value storage, counts, indices, prefix results, grouping and fast retrieval.

• Use sets for uniqueness, membership checks, duplicate removal and visited-state tracking.

• Solve frequency problems such as anagrams, first non-repeating character, majority-style counting and grouped strings.

• Use prefix hash patterns for subarray sum, zero-sum awareness, longest subarray conditions and count-based solutions.

• Practice pair and triplet problems using hash lookup, sorted strategy comparison and complexity trade-offs.

• Understand collision awareness, average complexity, worst-case thinking and memory trade-offs.

• Apply hashing in real interview patterns such as two sum, longest consecutive sequence, distinct counts and window frequency.

• Labs include frequency maps, hash set problems, prefix hash problems and optimized lookup practice.

• Outcome: replace unnecessary nested loops with efficient hashing solutions and explain lookup-based optimization clearly.

Build strong foundations in searching, sorting, binary search and comparison-based problem solving.

• Review linear search, binary search conditions, sorted data requirements and boundary management.

• Implement binary search with low, high, mid, loop conditions, overflow-safe mid awareness and answer validation.

• Solve binary search on answer problems such as minimum capacity, allocation style, feasibility check and monotonic condition detection.

• Learn sorting fundamentals including comparison sorting, stable sorting awareness, custom comparator logic and sorting as preprocessing.

• Understand merge sort and quick sort concepts, partitioning, merge process, recursion cost and space trade-offs.

• Use sorting to solve interval problems, pair problems, duplicate problems, greedy preparation and ranking tasks.

• Practice lower bound and upper bound style thinking for first occurrence, last occurrence, insert position and range search.

• Avoid common binary search mistakes such as infinite loops, wrong boundary update, missing answer variable and invalid monotonic assumption.

• Labs include classic binary search, rotated array, peak element, search range, sorting-based optimization and binary search on answer sets.

• Outcome: apply searching and sorting patterns accurately with clean boundary reasoning and confident dry runs.

Learn hierarchical data structures and solve tree traversal, recursion and BST interview problems.

• Understand tree terminology including root, child, parent, leaf, height, depth, subtree, ancestor and level.

• Implement binary tree traversals including inorder, preorder, postorder, level order and recursive versus iterative awareness.

• Solve basic tree problems such as height, count nodes, sum nodes, diameter awareness, balanced tree checks and mirror tree.

• Use recursion carefully for tree problems with base cases, returned values, global answers and subtree contribution thinking.

• Learn binary search tree properties, insertion, search, min-max, validation and sorted inorder concept.

• Solve BST problems such as lowest common ancestor awareness, kth smallest, floor/ceil idea and range queries.

• Understand level-order traversal with queues, BFS thinking and connection to graph traversal basics.

• Practice tree construction awareness from traversals, serialization concept and interview explanation of tree diagrams.

• Labs include traversal sets, BST operations, recursive tree problems and dry-run diagrams.

• Outcome: solve tree and BST problems with correct recursion, traversal logic and interview-ready explanation.

Use priority-based data structures for top-k, scheduling, merging and optimization problems.

• Understand heap structure, min-heap, max-heap, complete binary tree property and priority queue operations.

• Use heaps for top-k elements, kth largest, kth smallest, stream problems and efficient repeated maximum/minimum retrieval.

• Solve merge problems such as merging k sorted lists, sorted arrays awareness and priority-based selection.

• Apply heap logic to scheduling, meeting rooms awareness, minimum cost problems and frequency-based tasks.

• Understand heap complexity for insertion, deletion, peek and build heap awareness.

• Review trie basics for prefix searching, word storage, autocomplete awareness and string dictionary problems.

• Learn disjoint set union awareness for components, union, find, path compression and graph connectivity preparation.

• Compare when to use heap, sorting, balanced structure, queue, set or map based on operation requirements.

• Labs include heap implementation awareness, top-k practice, priority queue problems, trie introduction and DSU basics.

• Outcome: solve priority and advanced structure problems with correct data-structure selection and complexity explanation.

Develop graph problem-solving skills for traversal, connectivity, shortest path and real interview patterns.

• Understand graph terminology including vertices, edges, directed, undirected, weighted, unweighted, connected components and cycles.

• Represent graphs using adjacency list, adjacency matrix awareness, edge list and memory trade-offs.

• Implement DFS for traversal, connected components, cycle awareness, path discovery and recursive exploration.

• Implement BFS for level traversal, shortest path in unweighted graphs, grid BFS and queue-based exploration.

• Solve grid problems using graph thinking, directions array, visited matrix, islands, rotten oranges style and shortest path awareness.

• Learn topological sort awareness for dependency ordering, prerequisites, DAG concepts and cycle detection in directed graphs.

• Understand shortest path basics including Dijkstra awareness, priority queue use, relaxation and weighted graph reasoning.

• Review DSU use in graph connectivity, cycle detection awareness and component merging.

• Labs include graph traversal, grid BFS/DFS, connected components, topological sorting and shortest path practice.

• Outcome: model problems as graphs and solve traversal or path problems with clean visited handling and complexity notes.

Learn greedy decision-making and common patterns used in placement coding rounds.

• Understand greedy approach through local optimal choice, proof intuition, sorting support and when greedy may fail.

• Solve activity selection, meeting scheduling, interval merging, minimum platforms awareness and non-overlap problems.

• Practice greedy with sorting, priority queues, two pointers and frequency-based decisions.

• Learn interval patterns including merge intervals, insert interval awareness, overlapping ranges and start-end sorting.

• Use greedy in coin/change awareness, fractional knapsack awareness and resource allocation style problems.

• Understand proof habits by explaining why the local choice leads to global result or where counterexamples may appear.

• Compare greedy with dynamic programming when current choice impacts future possibilities.

• Practice common interview patterns such as sweep line awareness, sorting by end time, min/max selection and deadline-style tasks.

• Labs include interval sets, scheduling problems, greedy sorting problems and approach-proof discussions.

• Outcome: identify suitable greedy problems and explain decision logic instead of applying greedy blindly.

Build dynamic programming confidence from recursion to memoization, tabulation and classic interview patterns.

• Understand DP meaning through overlapping subproblems, optimal substructure and avoiding repeated computation.

• Convert recursion to memoization using state definition, cache table, base cases and return values.

• Build tabulation solutions with iteration order, table initialization, transitions and answer extraction.

• Practice one-dimensional DP such as Fibonacci style, climbing stairs, house robber awareness and min/max cost problems.

• Solve two-dimensional DP such as grid paths, unique paths, minimum path sum and matrix-based transitions.

• Learn subsequence DP awareness including LCS, LIS, edit distance idea and pick-not-pick patterns.

• Practice knapsack-style thinking including 0/1 choices, capacity state, subset sum awareness and partition problems.

• Understand DP optimization through rolling arrays, state reduction and complexity improvement where practical.

• Labs include recursion-to-DP conversion, memoization tables, tabulation practice and classic DP problem sets.

• Outcome: solve DP problems systematically with state, transition, base case and complexity explanation.

Prepare a placement-ready DSA portfolio with timed practice, revision strategy and interview explanation skills.

• Build a solved problem portfolio with arrays, strings, recursion, linked lists, stacks, queues, hashing, trees, heaps, graphs, greedy and DP topics.

• Maintain pattern notes explaining when to use each approach, important signals, common mistakes and template code.

• Create complexity sheets for repeated revision of time and space complexity across all solved problems.

• Practice timed coding contests to improve speed, accuracy, debugging under pressure and test-case discipline.

• Prepare mock interview flow including problem reading, clarifying questions, brute force explanation, optimization, code writing and dry run.

• Build GitHub or portfolio evidence with clean problem folders, README notes, approach explanations and selected best solutions.

• Review common mistakes such as missing edge cases, wrong loop boundaries, overflow, bad recursion base case and untested assumptions.

• Prepare revision checklists for last-week placement preparation, topic-wise problem sets and weak-area improvement.

• Practice final mock rounds with mentor review, code review, complexity discussion and communication feedback.

• Outcome: complete an interview-ready DSA capstone with problem explanations, GitHub evidence, timed mock results and revision plan.