Advance Data Structures

About Course

Advance Data Structures and Algorithms

Data structures are the most essential elements in the process for creating efficient algorithms and good software design. Knowledge of how to make and design good data structures is a crucial skill required in becoming an classic programmer. This course will teach you how to master the decisive ideas surrounding data structures.

When we think of data structures, there are usually four forms:

  • Linear: arrays, lists.
  • Tree: binary, heaps, space partitioning etc.
  • Hash: distributed hash table, hash tree etc.
  • Graphs: decision, directed, acyclic etc.

This course offers you with high quality animated videos explaining a multiplicity of data structures and how they are characterized visually. You will learn how to cypher various data structures together with simple to follow step-by-step commands. Every data structure presented will go together with some working source code to set your understanding of that particular data structure.

Data Structures are used for accumulating and managing data in an effective and organized way for faster and easy access and alteration of Data. Some of the basic data structures are Arrays, Linked List, Stacks, Queues etc. A data structure and algorithm  is a focused format for forming, dispensation, retrieving and storing data. There are quite a lot of basic and advanced types of data structures, all intended to arrange data to suit a specific purpose.

Advance Data Structures and Algorithms are one of the important branches of data science which is used for storing, organizing and management of data and information for effective, easy availability and alteration of data. They are the basic element for making effective and operative software design and algorithms. The knowledge of making and designing a good data structure is vigorous for becoming an admirable programmer. Its scope is also growing with the rise in new procedures of working in information technology.

Our training is divided into two formats=>Basic And Advanced

Mode of Training=>Online Live Classes

Certificate In Advance Data Structures

 

 

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What Will You Learn?

  • Learn and implement different Data Structures
  • Learn, implement and use different Algorithms
  • More interviews
  • Handle offers and negotiate raises
  • Become a better developer by learning computer science fundamentals

About the instructor

A

Course Curriculum

Introduction

  • What is An Algorithm
  • Algorithm Specification
  • Performance Analysis
  • Randomized Algorithms
  • References And Readings

Elementary Data Structures

  • Stacks And Queues
  • Trees
  • Dictionaries
  • Priority Queues
  • Sets And Disjoint Set union
  • Graphs
  • References And Readings

Divide And Conquer

  • General Method
  • Defective Shess Board
  • Binary Search
  • Finding the Minimum And Maximum
  • Merge Sort
  • Quick Sort
  • Selection
  • Strassen’s Matrix Multiplication
  • Sonvex Null
  • Reference And Readings

The Greedy Method

  • The General Method
  • Container Loading
  • Knapsack Problem
  • Tree Vertex Splitting
  • Job Sequencing With Trees
  • Minimum Cost Spalling Tree
  • Optimal Storage on Tapes
  • Optimal Merge Patterns
  • Single Source Shortest Paths
  • References And Readings

Dynamic Programming

  • The General Method
  • Multistage graph
  • All-Pairs Shortest Paths
  • Single Source Shortest Path
  • Optimal Binary Search Trees
  • String Editing
  • 0/1 Knapsack
  • Reliability Design
  • The Traveling Salesperson Problem
  • Flow Shop Scheduling
  • References And readings

Basic Traversal And Search Techniques

  • Techniques For Binary Trees
  • Techniques for Graph
  • Connected Components And Spelling Trees
  • Biconnected Components And DFS
  • References And Readings

Backtracking

  • General Method
  • The s-Queens Problem
  • Sum Of Subsets
  • Graph Coloring
  • Hamiltonian Cycles
  • Knapsack Problem
  • Reference And Readings

Branch And Bound

  • The Method
  • 0/1 Knapsa ck Problem
  • Traveling Sales Sperson
  • Efficiency Considerations
  • Reference And Readings

Algebraic Problems

  • General Method
  • Evaluation and Interpolation
  • The fast Fourier Transform
  • Modular Arithmetic
  • Even Faster Evaluation And Iterpolation
  • Reference And Readings

Lower Bound Theory

  • Comparison Trees
  • Oracles And Adversary Arguments
  • Lower Bounds
  • Through Reductions
  • Techniques For Algebraic Problems
  • Reference And Readings

Hard And NP-Complete Problems

  • Basic Concepts
  • Cook’s Theorem
  • Np-Hard Graph Problems
  • Np-Hard Scheduling Problems
  • Np-Hard Code Generation Problems
  • Some Simplified
  • Reference And Readings

Approximation Algorithms

  • Introduction
  • Absolute Approximation
  • Approximations
  • Polynomial Time Approximation Schemes
  • Fully Polynomial Time Approximation Schemes
  • Probabilistically Good Algorithms
  • Reference And Readings

Pram Algorithms

  • Introduction
  • Computation Model
  • Fundamental Techniques And Algorithms
  • Selection
  • Merging
  • Sorting
  • Graph Problems
  • Computing The Convex Hull
  • Lower Bounds

Mesh Algorithms

  • Computational Model
  • Packet Routing
  • Fundamental Algorithms
  • Selection
  • Merging
  • Sorting
  • Graph Problems
  • Computing The Convex Hull
  • References And Readings

Hypercube Algorithms

  • Computational Model
  • PPR Routing
  • Fundamental Algorithms
  • Selection
  • Merging
  • Sorting
  • Graph Problems
  • Computing The Convex Hull
  • References And Readings
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