### About Data Structure Training

Data Structures is a concept a means of storing a collection of data. Computer Science is a concern with study of methods for effectively using a computer to solve problems. These can be solve by algorithms and data structures. Data Structures tells you what way the data as to store in computer memory and how to access the data efficiently. Many Applications are designed by data structures stack applications like page visited history in a web-browser, chain of method calls in the Java virtual machine or C++ Run-time environment etc Queue Application Like Waiting Lines, Multi-programming etc For many applications the choice of proper data structure is the only major decision involving the implementation. Majorly the database designing and internal implementation is done only by using Data Structures techniques.

#### Data Structure Training Course Objective

This Course main objective for the student to understand Analysis and Designing of the Algorithms and how the different data structures are used for efficient accessing of the data and Manipulation of the data at the end of the session we can able to Know different Kinds of data structures and we can able to provide different algorithms for time and space complexity.

**Data Structure Training Course Duration**

30 Working days, daily one and half hours

**Data Structure Training Course Content OverviewI**ntroduction to Data Structure

Algorithms

**Performance Analysis**

Time complexity

Space complexity

**Asymptotic Notations-**

Big O

Omega

Theta notations

Arrays

Structures

Pointers

**Dynamic Memory allocation**

Malloc()

calloc()

realloc()

free()

**Stacks**

Stack Operations

push()

pop()

peex()

distzay()

isEmpty()

isFull()

Stack implementation using arrays

Applications

Decimal to Binary

String reverse

Number reverse

Recursion – Towers of Hanoi

Balanced Parentheses

Expressions

Stack Implementation using pointer (dynamic)

**Expression**

Introduction to Notations

Importance of Notations in expression evaluation

Conversion Algorithms

Infix to prefix

Infix to postfix

Prefix to infix

Prefix to postfix

Postfix to infix

Postfix to prefix

Implementation of all the conversions

**Queues**

Operations on Queue – enqueue(), dequeue()

Queue implementation using static arrays

Applications

Queues Implementations using pointer (dynamic)

Circular queues

Double Ended queue (Deques)

**Single linked list**

Introduction

Construction

Length

Insertion

Deletion

Sort

Reverse list

Swap node data

Swap nodes

Applications

Stack implementation using linked list

Queue implementation using linked list

Doubly linked list

Circular linked list

Circular Doubly Linked List

**Binary Tree**

Terminology

Differences between Tree and Binary Tree

Binary Tree Representations

Expression Trees

Traversals

In-order

pre-order

post-order

**Binary Search Tree**

Introduction to BST

Insertion

Deletion

Search

Implementation

**Graph**

Introduction & Terminology

Graph Representations

Traversal

BFS (Breadth First Search)

DFS (Depth First Search)

**Searching Algorithms**

Linear search

Binary search

**Sorting Algorithms**

Bubble sort

Selection sort

Insertion sort

Heap sort

Merge sort

Quick sort

**AVL TreesI**ntroduction

BST v/s AVL

Rotations

L-L-Rotation

R-R-Rotation

L-R-Rotation

R-L-Rotation

Insertion

Deletion

Traversal

**Red Black Trees**

Introduction

BST v/s AVL v/s RBT

Rotations

L-L-Rotation

R-R-Rotation

L-R-Rotation

R-L-Rotation

Insertion

Deletion

**B trees**

M-way Search Tree

Search

Insertion

Deletion

**Hashing**

Hash Table representation

Hash function-Division Method

Collision

Collision Resolution Techniques

Separate Chaining

open addressing

linear probing

quadratic probing

double hashing

Rehashing

**Priority Queue-Definition**

Operations-Insertion, Deletion

**Heap**

Definition

Max Heap

Min Heap

Insertion and deletion

**Pattern matching algorithms**

Brute force

Boyer –Moore algorithm

Knuth-Morris-Pratt algorithm

**Tries**

Standard Tries

Compressed Tries

Suffix tries

**Data Structure Using C Training**

Data Structure Using C training focuses on the significance of data structures and algorithms in computer programming and software development. Hands-on exposure on data structures lays a strong foundation in developing programs. According to the real life scenario, a well versed candidate can distinguish which data structure to be used to drive optimized results.

Participants in this training will learn how to organize and store data in a computer. Data structures such as arrays, stacks, queues, linked list, and trees are covered in detail. Different algorithms for searching, hashing, and sorting are explained with examples and programming. Candidates learn about the applications of binary search trees and graphs.

**Upon the completion of training, you will be able to:**

Understand the terminology used in data structures

Find algorithm complexity

Define and perform operations on different data structures say arrays, stacks, queues, linked list, and trees

Perform recursion operation

Execute data operations such as searching, sorting, and hashing

Use binary trees for insertion, deletion, and searching of elements

Use graphs and multi-graphs

Target audience

Students

Academicians

Software developers

Prerequisites

While there is no formal pre prerequisite for this training, however candidates with a strong interest in software development are the ideal participants for this course.

**Step 1: Understand Depth vs. Breadth**

We all have that friend who has solved 500 coding problems. They love to wear it as a badge of honor. But when it comes to interviews, they fail miserably. This is a very common scenario. It’s what we call the “Breadth-Only” approach. They are solely focused on solving as many problems as they can.

The Breadth-Only approach has a problem—you don’t build a strong foundation. Interviews require deep problem-solving knowledge and the ability to code fast and accurately. You only develop these skills with focused preparation.

Here’s a counter-intuitive approach that works better:

Focus on less problems, not more

This is comforting, right? Who wants to focus on 500 problems when you can focus on 100?

But here’s the key—you want to learn them in depth. This is where depth comes in.

**When you analyze a problem in depth, it means:**

You can code it quickly

You can code it with correct syntax, which means you are good at the language

You can write clean code in one go, because it’s second nature to you

You can apply the same code to a new problem quickly

You know the data structure you are using and can implement it if asked to

To achieve this, you need to focus on a few representative problems (around 100 works well.) Solve them a few times and you’ll start seeing patterns. You also start getting better at the coding part.

So you’ve covered Depth, congratulations! You have acquired a solid base.

You can now go all out and solve as many problems as you want. And best of all, you won’t need to code many of them. Figure out a solution, and if it’s similar to one of your core problems (which it often is), you’re done. No need to actually code and debug it because you’re already good at that.

**Step 2: Start the Depth-First Approach—**make a list of core questions

Identify a list of ~100 core problems. Many sites give you 100 curated problems.

**Step 3: Master each data structure.**

Now that you have finalized your list, start with the basics. Know every data structure.

Learn how to use each data structure in your language.

Also, learn how to implement them. Yes, implement them by hand. Many people ignore this, but it’s extremely important. Interviewers can ask you about data structure internals. Many problems modify data structures or re-engineer them for a specific use case. To utilize them fully, you need to know how they work.

**For example:**

Interviewer: “So you initialized an array-backed list. Good. Now let’s say you reach its capacity, what happens when you try to add another element?”

Candidate: blank “What do you mean capacity? I can keep adding elements to this list.”

**Interviewer: facepalm**

In this case, the candidate had been using Python, and there’s no concept of list capacity. You just keep adding elements. But that’s not what happens under the hood. You need to know what data structures back a list, and how that works.

**Here’s another example:**

Let’s say you’re asked to Implement a Queue using just Stacks (a popular question). This is a modified data structure. If you haven’t implemented either of those before, you’ll have trouble getting started.

Now, this doesn’t mean you need to know every implementation’s code. Some data structures are pretty hard to implement – for example, deleting a node from a Binary Search Tree is not trivial to code. But you should know how it works.

**Here is a list of data structures to master :**

Arrays and Lists

2D Arrays

Strings

Linked List

Stack

Queue

Hash Table & Hash Set

Heap

Graphs

Binary Tree

Binary Search Tree

Trie

How to go about it? Let’s say your core problems are divided by data structure. You can master each data structure when you start each section. Or, you can master them all at the beginning. Do what works for you.

(Check HackerEarth Data Structure & Algorithm practice)**Step 4: Spaced Repetition**

Alright. You made a list of questions and you started solving them. Here’s a common question we get:

“I solve many questions but can’t solve them a week later! How do I remember solutions?”

The key is to not remember solutions. The key is to practice them. When you see a problem, you should immediately be able to break it down and re-create the solution. This is different from rote learning. You’re recognizing different components, breaking them down and solving the problem.

The best technique we’ve seen – solve the problem again in 3 days. Then in a week. Then in a month. It will become second nature to you.

**Step 5: Isolate techniques that are reused. Isolate actual code blocks.**

This is where the Depth-First approach gets exciting. As you solve these problems, you’ll start to notice patterns.

Let’s say you solved 5 problems that used Binary Search. You can isolate the Binary Search code and practice it over and over. You know it will be used in similar problems.

And this is one of many techniques you can isolate. Here are some other common ones:

**Depth First Search**

Recursion + Memoization

Hash Table + Linked List combination

Searching a Binary Tree etc.

Now, you have a collection of techniques you can apply to new problems.

**Step 6: Now, it’s time for Breadth.**

Let’s say you’ve mastered your core problems. Using common data structures is second nature to you. You can now look beyond your core set. Because you’ve implemented so many techniques already, you don’t even have to code all the new questions.

During this time, try to solve realistic interview problems. Once you get good, there’s a tendency to focus on really hard problems. The thought process is – “if I can solve these really hard problems, then interview problems will be a piece of cake!”. That’s not usually the case. Techniques in really hard problems often have nothing to do with interview-level problems.

**Step 7: Practice on paper**

We recommend practicing on paper at some point in your prep. When you code without an IDE and Stack Overflow, it takes you away from your comfort zone.

Here are some benefits of practicing on paper:

You’re forced to plan your code before writing. You can’t just go back and retype.

You will start learning correct language syntax and data structure usage. With an IDE, code used to write itself.

You can take a paper and pen anywhere with you to practice.

And more importantly, it is a realistic simulation of a whiteboard interview.

**Why study data structures?**

Programs are comprised of two things: data and algorithms. The algorithms describe the way the data is to be transformed. The reason for learning about data structures is because adding structure to our data can make the algorithms much simpler, easier to maintain, and often faster.

**Why study data structures?**

Programs are comprised of two things: data and algorithms. The algorithms describe the way the data is to be transformed. The reason for learning about data structures is because adding structure to our data can make the algorithms much simpler, easier to maintain, and often faster.

**Key Points**

In general, the more sophisticated the data structure, the simpler the algorithm.

Simple algorithms are less expensive to develop.

There is less code to read and comprehend.

The logic is simpler and modifications are less likely to introduce errors.

It’s usually much easier to repair defects, make modifications, or add enhancements.

AboutHow It WorksCoursesInstructorsEnrollment OptionsFAQ

Data Structures and Algorithms Specialization

University of California San Diego

WHAT YOU WILL LEARN

Apply basic algorithmic techniques such as greedy algorithms, binary search, sorting and dynamic programming to solve programming challenges.

Apply various data structures such as stack, queue, hash table, priority queue, binary search tree, graph and string to solve programming challenges.

Apply graph and string algorithms to solve real-world challenges: finding shortest paths on huge maps and assembling genomes from millions of pieces.

Solve complex programming challenges using advanced techniques: maximum flow, linear programming, approximate algorithms, SAT-solvers, streaming.

**SKILLS YOU WILL GAIN**

Debugging

Software Testing

Algorithms

Data Structure

Computer Programming

Dynamic Programming

Binary Search Tree

Priority Queue

Hash Table

Stack (Abstract Data Type)

List

Graph Theory

**About this Specialization**

This specialization is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems and will implement about 100 algorithmic coding problems in a programming language of your choice. No other online course in Algorithms even comes close to offering you a wealth of programming challenges that you may face at your next job interview. To prepare you, we invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs. Sorry, we do not believe in multiple choice questions when it comes to learning algorithms…or anything else in computer science! For each algorithm you develop and implement, we designed multiple tests to check its correctness and running time — you will have to debug your programs without even knowing what these tests are! It may sound difficult, but we believe it is the only way to truly understand how the algorithms work and to master the art of programming. The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco (1000 times faster than the standard shortest path algorithms!) Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine.

**Applied Learning Project**

The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco (1000 times faster than the standard shortest path algorithms!) Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine.

## Why choose us Coding Trainings

**1. Having More Than 15 Years Of Experience In Medical Coding Training****2. 13+ Years Of Experience In Real-Time Industry****3. 100% Lab Facility For Every Student By Experienced Lab Coordinators****4. Training On Live Projects And Assignments****5. 100% Placement Assistance****6. Lifetime Access To Latest Content****7. 24 X 7 Trainer’s Support On Online Forum****8. Giving Credits For Real Time Internship****9. Get Full Access To Paid SEO Tools****10. Become A Certified Professional**

## How much Does Medical Coding Training Fees Hyderabad

**Fees **for **Medical coding 6,000+ ** only if ur Looking for **All training 12,000+** Classroom training and internship training with **live projects **

**S Nandini reddy**

★★★★★ 1 days ago

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**shiva bkb**

★★★★★ 8 days ago

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javeed ahmed**