What Is The Ask Query Feature
We have the “Ask query” option with every lecture and every assignment in the course. You can ask your doubt using it anytime.
Let’s say you are practicing some assignment and you can’t figure it out or you are trying some lecture code yourself and all test cases are not passing you can paste your code, send images and ask your doubts.
Our trainer including the expert developers having 10+ experienced will execute your code or analyze your doubts give a detailed response to your query.
Focus On Less Problems Not More
This is comforting, right? Who wants to focus on 500 problems when you can focus on 100?
But heres the keyyou want to learn them in depth. This is where depth comes in.
When you analyze a problem in depth, it means:
To achieve this, you need to focus on a few representative problems Solve them a few times and youll start seeing patterns. You also start getting better at the coding part.
So youve 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 wont need to code many of them. Figure out a solution, and if its similar to one of your core problems , youre done. No need to actually code and debug it because youre already good at that.
The Truth About Algorithms And Data Structures
Is it time to look beyond the code?
You can do a lot with very little.
Modern, high-level languages abstract away lots of details. You dont need to know the difference between selection sort, bubble sort, merge sort, and quicksort, for instance, to use Pythons sorted function. It just works.
Do you really need to learn how to use data structures and algorithms?
Well Ive got news for you:
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Practice With Mock Interviews
The steps mentioned above can be rehearsed over and over again until you have fully internalized them and they become second nature to you. A good way to practice is by partnering with a friend and taking turns to interview each other.
A great resource for preparing for coding interviews is interviewing.io. This platform provides free and anonymous practice interviews with Google and Facebook engineers, which can lead to real jobs and internships.
Doing well in mock interviews will unlock the jobs page for candidates, and allow them to book interviews with top companies like Uber, Lyft, Quora, Asana, and more. For those who are new to coding interviews, a demo interview can be viewed on this site. Note that this site requires users to sign in.
I have used interviewing.io, both as an interviewer and an interviewee. The experience was great. Aline Lerner, the CEO and co-founder of interviewing.io, and her team are passionate about revolutionizing the process for coding interviews and helping candidates improve their interview skills.
She has also published a number of coding interview-related articles on the interviewing.io blog. I recommend signing up as early as possible with interviewing.io, even though it’s in beta, to increase the likelihood of receiving an invite.
How Do You Differentiate Between A Tree And Graph Data Structure
The following are the key differences between trees and graphs:
- Trees have a root node from which all other nodes originate. Graphs dont have root nodes.
- The vertices in a graph can have bidirectional connections. There is only a single path between two vertices in a tree.
- The nodes in a graph can connect to themselves, which is not possible in a tree.
- Trees are a hierarchical data structure whereas graphs are flat networks.
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What Operations Can Be Performed On Queues
- enqueue adds an element to the end of the queue
- dequeue removes an element from the front of the queue
- init is used for initializing the queue
- isEmpty tests for whether or not the queue is empty
- The front is used to get the value of the first data item but does not remove it
- The rear is used to get the last item from a queue
Data Structures And Algorithms Interview Questions
In this article
Algorithms and data structures are foundational to computer science. As the scaffolding for programming languages, tech recruiters place an emphasis on algorithms and data structures in interviews.
If youre looking for help with interview questions in those areas, youve come to the right place. Were going to cover all the data structure and algorithms interview questions that you should prepare for in 2022.
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Top Data Structures And Algorithms Interview Q& as
Data Structures can be defined as techniques used to define, store, and access data systematically. They form the most important component of any algorithm. Depending on the type of Data Structures, they store different kinds of data and are accessible in different ways. For an algorithm to return a result, it needs to operate on and manipulate a set of data structures in an organised and efficient manner to come to the final result.
In File Structures, the data is stored on disks following standard file storage policies and is not compatible with external, third-party applications. In Data Structures, on the other hand, the data is stored both on the disk as well as RAM in customised storage policies, and these are highly compatible with external apps.
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Data Structures can be broadly divided into two categories:
4. What are some key usage areas of Data Structures?
Data Structures are pretty much required in all the fields of computing that you can think of, especially Algorithms and Algorithm Optimization. Here are some other areas where Data Structures are extensively used:
- Operating system design
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Transformation & Opportunities in Analytics & Insights
Can You Explain The Working Of A Selection Sort
Selection sort follows a simple process to sort a list of elements. To start, the list is divided into two parts. The part on the left is sorted and the part on the right is unsorted. Initially, the list is unsorted so only the first element is on the left.
We then scan the array and find the smallest element. That element is swapped with the first element and becomes part of the sorted array. We then repeat this process with the second element and so on. The following image depicts the process.
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Picking A Programming Language
Before anything else, you need to pick a programming language for your algorithmic coding interview.
For the most part, I recommend using a language that you are extremely familiar with, rather than one that is new to you but that the company uses widely.
There are some languages that are more suitable than others for coding interviews. Then there are some that you absolutely want to avoid.
Personally, Python is my de facto choice for coding algorithms during interviews. It is succinct and has a huge library of functions and data structures.
One of the top reasons I recommend Python is that it uses consistent APIs that operate on different data structures, such as len, for … in … and slicing notation on sequences . Getting the last element in a sequence is arr , and reversing it is simply arr. You can achieve a lot with minimal syntax in Python.
Collecting The Best Resources For Learning And Practice
Our next goal would be to collect the best learning resources: books, blogs, talks, pdf, courses, etc. Internet could be handy for this. We need to follow the authentic sources and organize them according to the above syllabus. Here is the list of some best learning resources:
Data Structures and Algorithms for beginners
- Algorithms Unlocked by Thomas Coreman, MIT Press
- Algorithmic Puzzles by Anany and Maria Levitin, Oxford University Press
- Mathematics for computer science by MIT Open Courseware
Fundamentals of Programming
For data structures and algorithms interview
- Cracking the Coding Interview by Gayle Laakmann McDowell
- Leetcode for the coding problems practice
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Ready To Nail Your Next Coding Interview
Whether youâre a coding engineer gunning for a software developer or software engineer role, a tech lead, or youâre targeting management positions at top companies, IK offers courses specifically designed for your needs to help you with your technical interview preparation!
If youâre looking for guidance and help with getting started, As pioneers in technical interview preparation, we have trained thousands of software engineers to crack the most challenging coding interviews and land jobs at their dream companies, such as Google, Facebook, Apple, Netflix, Amazon, and more!
Describe The Types Of Data Structures
- Linear Data Structure: A data structure that includes data elements arranged sequentially or linearly, where each element is connected to its previous and next nearest elements, is referred to as a linear data structure. Arrays and linked lists are two examples of linear data structures.
- Non-Linear Data Structure: Non-linear data structures are data structures in which data elements are not arranged linearly or sequentially. We cannot walk through all elements in one pass in a non-linear data structure, as in a linear data structure. Trees and graphs are two examples of non-linear data structures.
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What Are Asymptotic Notations
Asymptotic Notation represents an algorithm’s running time – how long an algorithm takes with a given input, n. Big O, large Theta , and big Omega are the three distinct notations. When the running time is the same in all circumstances, big- is used, big-O for the worst-case running time, and big- for the best case running time.
Develop A Deep Knowledge Of Data Structures
You should understand and be able to talk about different data structures and their strengths, weaknesses, and how they compare to each other. Taking the time to implement data structures from scratch will leave you much better prepared for DS& A interviews, than studying them as an abstract concept.
Which Data Structure Is Used To Perform Recursion
Ans. Stack is used in recursion because of its Last In First Out nature. Operating System maintains a stack in order to store the values of each iteration.
Stack is used in recursion because of its Last In First Out nature. Operating System maintains a stack in order to store the values of each iteration.
Knowing When Youre Ready
Youll never feel ready no matter how long you study. Theres always more topics to dive deeper into or weak areas that could use more practice.
There is definitely an element of luck in the whole process. Its very possible that you get a question on a topic you havent prepared for at all or are really weak on. Its also possible that you get all questions on topics youre really strong in since two months ago and didnt need to spend that extra time studying to pass. You cant prepare for every possible outcome, but your chances of success get higher than everyone elses with more practice.
Keeping track of your progress and getting feedback is very important in knowing when youre truly ready to perform on the real interview without relying on just emotion.
Keep track of your completion times when you do practice questions and aim for at most 40 minutes to complete most medium level questions and 1 hour to complete hard level questions on sites like leetcode.com or hackerrank.com.
Realize that you must pass every edge case for it to count on those sites but luckily this isnt the case for real interviews! Most interview code is evaluated on a small set of edge cases and small errors get passed or considered to be irrelevant all the time. The point of evaluation isnt to be so strict as to expect perfection, but they need to see enough signalsâ to be convinced.
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What Is Data Abstraction
Ans:- Data abstraction is the reduction of a particular body of data to a simplified representation of the whole. Data abstraction is a powerful tool for breaking down complex data problems into manageable chunks. Abstraction, in general, is the process of taking away or removing characteristics from something to reduce them to a set of essential characteristics. As in abstract art, the representation is likely to be one potential abstraction of several possibilities. This is applied by initially specifying the data objects involved and the operations to be performed on these data objects without being overly concerned with how the data objects will be represented and stored in memory.
Data abstraction is the reduction of a particular body of data to a simplified representation of the whole. Data abstraction is a powerful tool for breaking down complex data problems into manageable chunks. Abstraction, in general, is the process of taking away or removing characteristics from something to reduce them to a set of essential characteristics. As in abstract art, the representation is likely to be one potential abstraction of several possibilities. This is applied by initially specifying the data objects involved and the operations to be performed on these data objects without being overly concerned with how the data objects will be represented and stored in memory.
Binary Tree Coding Interview Questions
So far, we have looked at only the linear data structure, but all information in the real world cannot be represented in a linear fashion, and thats where tree data structure helps.
The tree data structure is a data structure that allows you to store your data in a hierarchical fashion. Depending on how you store data, there are different types of trees, such as a binary tree, where each node has, at most, two child nodes.
Along with its close cousin binary search tree, its also one of the most popular tree data structures. Therefore, you will find a lot of questions based on them, such as how to traverse them, count nodes, find depth, and check if they are balanced or not.
A key point to solving binary tree questions is a strong knowledge of theory, e.g. what is the size or depth of the binary tree, what is a leaf, and what is a node, as well as an understanding of the popular traversing algorithms, e.g. pre-, post-, and in-order traversal.
Here is a list of popular binary tree-based coding questions from software engineer or developer job interviews:
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Find The Subsequence Of Length 3 With The Highest Product From A Sequence Of Non
- Input: n = 8 arr =
The three increasing elements of the given arrays are 10, 11, and 12, which form a three-size subsequence with the highest product.
vector< int> maxProductSubsequence it-- largestOnLeft=*it } int m=0 long long p=INT_MIN vector< int> result result=-1 for else } } } return v }
- Time Complexity: O)
Tips For Smart Learning
- Our goal must be to cover at least 80% syllabus and prioritize the topics based on strengths and weaknesses. A topic that requires more time should get more time.
- Doing a weekly critical review could help us to understand the progress. We need to think what is the further scope of improvement in the topic? What is the current level of our problem-solving ability? For the fact-based view, we recommend rating each topic on a scale of 1 to 10.
- Few ideas always come during learning DSA: Brute force solution, efficient solution, boundary conditions, time and space complexity analysis, coding style. Keep preparing notes if you find something unique or essential patterns.
- In the starting journey, rather than solving many problems, focus on the top 100 coding interview questions. The best idea would be to pick famous coding interview problems which can be solved using various techniques.
- Learn to visualize critical operations, implementations, and use cases of each data structure.
- Never mind going through the same concepts and problems again and again. This would help in figuring out hidden ideas. Focus on reason and never try to memorize!
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