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Data Structures Assignment help

Data structure comes with a number of algorithms. It works with different types of data and structures and organizes data to fulfill a specific purpose. It deals with algorithms, Algorithm design, Algorithm analysis, Graph algorithms, Equivalence relations, Hash functions, hash tables, Theory of computation, linked lists, stacks, queues, searching and sorting techniques, graph data structure, trees, recursion of algorithms. It is technical way of storing data by using some specific techniques in order to use data efficiently.

Data structures is divided into two parts :

  • Primitive Data Structures: It is simple data structure that can be used to store data. It involves Float, Char, Integer, Boolean etc.
  • Abstract Data Structures: It is complex data structure that is used to store large and connected data. It involves Graphs, Linked Lists, Trees etc.

An algorithm is a set of instructions which use these data structures to execute a program. It can be expressed as high level description such as pseudo code by using a flowchart. Algorithm’s efficiency depends on following two factors:
1. Space Complexity : 

Data Structure Code help :

Let we take the example of B-trees ,which are particularly well suited for implementation of databases,but compiler implementations mainly use hash tables so as to look up the identifiers. Data structures provide a means which will help in managing huge amounts of data efficiently,such as large databases and internet indexing services

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DSA Programming Assignment help :

  • Data structure applications, DS college Projects
  • Data structure is the logical and mathematical way of storing and organizing data in a computer so that it can be used efficiently. Data structures provide a means to manage huge amounts of data efficiently,

Large databases and internet indexing services. Data structures are generally based on the ability of a computer to fetch and store data at any place in its memory, specified by an address , Primitive types-It means that the data is operated upon by the machine instruction.

Data Structures & Algorithms applications help by online tutors:

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Data Structures & Algorithms Assignment help tutors  assist with complex Data Structure & Algorithms assignment problems & application code

Data Structure Projects help include:

  • Non Primitive types-It is the extension of the primitive data types. It can further divided into Array, List and Files. List are of two types-
  • Linear-It is the homogenous collection of data,it contains Stack and Queue.
  • Non linear- It contains tree and graph.

Stack-It is a linear data structure in which insertion and deletion takes place from the top of the stack. It is also called FIFO list. The two operations which are performed on the stack are-Push (insert an item into the stack),Pop (remove an item from the stack),m PushStackItem(Stack, Item),Item PopStackItem(Stack),Implementation of stack-Stack is implemented by using array and linked list.

  • Array Implementation- In array, we have to reserve a block of memory cells large enough to hold all the items we want to put on the stack.
  • Pointer Implementation-It contains two fields one or storing the data and other field points out the address of the next data.
  • QUEUE-It is also called LIFO list in which the insertion takes place from the REAR and deletion takes place from the FRONT end.
  • Tree- A tree is an example of a nonlinear data structure. Two other examples are multidimensional arrays and graphs.A tree contains the ROOT and nodes.
  • Graph-It is the graphical representation of data which contains vertices and edges

Multidimensional data structure-

  • How data structures map onto physical memory.
  • Identify linear versus nonlinear data structures.
  • Manipulate data structures with basic operations.
  • Compare different implementations of the same data structure


  • Bubble, Selection, Insertion, Shell sorts and Sequential, Binary, Indexed Sequential Searches,Interpolation, Binary Search Tree Sort, Heap sort, Radix sort ,Analysis of Algorithms
  • Algorithm, Pseudo code for expressing algorithms, time complexity and space complexity,O-notation, Omega notation and theta notation.
  • HASHING TECHNIQUES-Hash function,Address calculation techniques, Common hashing functions,Collision resolution,Linear probing, Quadratic,Double hashing,Bucket hashing,Deletion and rehashing

 Homework help for :

  • Design and implementation of data structures ,arrays, stacks, queues, linked lists, binary trees, heaps,
  • Balanced trees (e.g. 2-3 trees, AVL-trees) and graphs, sorting, hashing, memory allocation, and garbage collection, Java programming
  • Collections of data with fast updates and queries, generics, analysis tools, sorting, linked lists and iterators, stacks and queues,search trees, maps, hashing, priority queues, and graphs.


  • LINEAR LISTS ,Stacks: LIFO structure, create, POP, PUSH, delete stack
  • Queues: FIFO structure Priority Queues, Circular Queues, operations on Queues,Linear List Concept
  • List v/s Array, Internal pointer & External pointer, head, tail of a list, Null list, length of a list
  • Linked Lists ,Nodes, Linked List Data Structure ,Linked Lists algorithms
  • Create List ,Insert Node (empty list, beginning, Middle, end)
  • Delete node(First, general case) ,Search list ,Retrieve Node, add node, Remove node, Print List
  • Append Linked List, array of Linked Lists ,Complex Linked List structures
  • Header nodes ,Circularly-Linked List ,Doubly Linked List ,Insertion, Deletion ,Multilinked Lists
  • Insertion, Deletion ,Binary Trees ,Travesals (breadth-first, depth-first) ,Expression Trees
  • (Infix, Prefix, Postfix Traversals) ,General Trees ,Search Trees ,Binary Search Trees
  • HEAPS ,Structure ,Basic algorithms – ReheapUp, ReheapDown, Build heap, Insert, Delete
  • MULTIWAY TREES ,M-way search trees ,B-Trees ,Insertion (Inseet node, Search node, Split node, Insert entry)
  • Deletion (Node delete, Delete entry, Delete mid, ReFlow, Balance, Combine) ,Traverse B-Tree
  • B-Tree Search ,Operations (Add vertex, Delete Vertex, Add Edge, Delete Edge, Find Vertex)
  • Traverse Graph (Depth-First, Breadth-First) ,Graph Storage Structures (Adjacency Matrix, Adjacency List)
  • Networks ,Minimum Spanning Tree ,Shortest Path Algorithm ,(Dijkstra’s algorithm, Kruskal’s algorithm, Prim’s algorithm, Warshall’s
  • algorithm)

Data Structures and Algorithms help :

  • Data Structures,Costs and Benefits,Abstract Data Types and Data structures,Design,patterns,Flyweight,Visitor,Composite,Strategy,Problems, Algorithms, and Programs,Mathematical Preliminaries,Sets and Relations,Logarithms,Summations and Recurrences,Recursion,Mathematical Proof Techniques,Direct Proof,Proof by Contradiction
  • Proof by Mathematical Induction,Estimation,Algorithm Analysis,Best, Worst, and Average Cases,A Faster Computer, or a Faster Algorithm?,Asymptotic Analysis,Upper Bounds,Lower Bounds,Notation,Simplifying Rules,Classifying Functions,Calculating the Running Time for a Program,Analyzing Problems,Common Misunderstandings
  • Multiple Parameters,Space Bounds,Speeding Up Your Programs,Empirical Analysis,Projects,Fundamental Data Structures,Lists, Stacks, and Queues,Lists,Array-Based List Implementation,Linked Lists,Comparison of List Implementations,Element Implementations,Doubly Linked Lists,Stacks,Array-Based Stacks,Linked Stacks,Comparison of Array-Based and Linked Stacks,Implementing Recursion,Queues,Array-Based Queues
  • Linked Queues,Comparison of Array-Based and Linked Queues,Dictionaries,Projects,Binary Trees,Definitions and Properties,The Full Binary Tree Theorem,A Binary Tree Node ADT,Binary Tree Traversals,Binary Tree Node Implementations,Pointer-Based Node Implementations,Space Requirements,Array Implementation for Complete Binary Trees,Binary Search Trees,Heaps and Priority Queues,Huffman Coding Trees,Building Huffman Coding Trees,Assigning and Using Huffman Codes,Search in Huffman Trees
  • Projects,Non-Binary Trees,General Tree Definitions and Terminology,An ADT for General Tree Nodes,General Tree Traversals,The Parent Pointer Implementation,General Tree Implementations,List of Children,The Left-Child/Right-Sibling Implementation,Dynamic Node Implementations,Dynamic “Left-Child/Right-Sibling” Implementation,K-ary Trees,Sequential Tree Implementations,Sorting and Searching,Internal Sorting,Sorting Terminology and Notation
  • Three [1](n2) ,Insertion Sort,Bubble Sort,Selection Sort,The Cost of Exchange Sorting,Shellsort,Mergesort,Quicksort,Heapsort,Binsort and Radix Sort,An Empirical Comparison of Sorting Algorithms,Lower Bounds for Sorting,Projects,File Processing and External Sorting,Primary versus Secondary Storage,Disk Drives,Disk Drive Architecture,Disk Access Costs
  • Buffers and Buffer Pools,The Programmer’s View of Files,External Sorting,Simple Approaches to External Sorting,Replacement Selection,Multiway ,Merging,Projects,Searching,Searching Unsorted and Sorted Arrays,Self-Organizing Lists,Bit Vectors for Representing Sets,Hashing,Hash Functions,Open Hashing,Closed Hashing,Analysis of Closed Hashing,Deletion,Projects
  • Indexing,Linear Indexing,ISAM, Tree-based Indexing, Trees, B-Trees, B+-Trees, B-Tree Analysis,Projects,Advanced Data Structures,Graphs,Terminology and Representations,Graph Implementations,Graph Traversals,Depth-First Search,Breadth-First Search,Topological Sort,Shortest-Paths Problems,Single-Source Shortest Paths, Minimum-Cost Spanning Trees, Prim’s Algorithm, Kruskal’s Algorithm, Projects, Lists and Arrays Revisited
  • Multilists,Matrix Representations,Memory Management,Dynamic Storage Allocation,Failure Policies and Garbage Collection,Projects,Advanced Tree Structures,Tries,Balanced Trees,The AVL Tree,The Splay Tree,Spatial Data Structures,The K-D Tree,The PR quadtree,Other Point Data Structures,Other Spatial Data Structures,Projects,Theory of Algorithms,Analysis Techniques,Summation Techniques,Recurrence Relations,Estimating Upper and Lower Bounds,Expanding Recurrences,Divide and Conquer Recurrences
  • Average-Case Analysis of Quicksort, Amortized Analysis, Projects, Lower Bounds, Introduction to Lower Bounds Proofs, Lower Bounds on Searching Lists, Searching in Unsorted Lists, Searching in Sorted Lists,Finding the Maximum Value,Adversarial Lower Bounds Proofs,State Space Lower Bounds Proofs,Finding the ith Best Element,Optimal Sorting,Projects,Patterns of Algorithms,Dynamic Programming
  • The Knapsack Problem,All-Pairs Shortest Paths,,Randomized algorithms for finding large values,Skip Lists,Numerical Algorithms,Exponentiation,Largest Common Factor,Matrix Multiplication,Random Numbers,The Fast Fourier Transform,Projects,Limits to Computation,Reductions,Hard Problems,The Theory of N P-Completeness
  • N P-Completeness Proofs,Coping with N P-Complete Problems,Impossible Problems,Uncountability,The Halting Problem Is Unsolvable,Projects,Utility Functions
  • Fundamental data structures, algorithms in computing, techniques in computing, Basic structures , stacks, queues , linked lists, Advanced structures , trees, hash tables , heaps, Algorithms , sorting , recursion, Complexity analysis , Amortised analysis, Disjoint sets , union find, Binary search trees , Red Black trees, splay trees, Max flow , min cut in networks, applications, Linear programming, Approximation algorithms, Randomised algorithms, Fixed paramter tractability, Exponential algorithms, Fast Fourier transform, Stable matching

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Minimum spanning tree using Prim’s algorithm , help for complex DS programs & application problems.

Data Structure Assignment Help, Complex Data Structure, and Data Structure Homework Help 

Data Structure Assignment Help , Arrays .Get instant help for technical reports on Data Structures & Algorithms , case studies.


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