Coding and Aptitude Round before interview

Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.

Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.

Resources for Prep:

For algorithms and data structures prep,Leetcode and Hackerrank are good resources.

For aptitude prep, you can refer to IndiaBixand Practice Aptitude.

With respect to data science challenges, practice well on GLabs and Kaggle.

Brilliant is an excellent resource for tricky math and statistics questions.

For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.

Things to Note:

Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!

In case, you are finished with the test before time, recheck your answers and then submit.

Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.

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🚀 Top Coding Interview Concepts – Part 2 💻

11. Stack 
A linear data structure that follows the LIFO (Last In, First Out) principle. The last element added is the first one removed.

12. Queue 
A linear data structure that follows the FIFO (First In, First Out) principle. The first element added is the first one removed.

13. Hash Table (Hash Map) 
A data structure that stores key-value pairs and provides fast insertion, deletion, and lookup using a hash function.

14. Tree 
A hierarchical data structure consisting of nodes connected by edges, commonly used to represent hierarchical relationships.

15. Binary Search Tree (BST) 
A special type of binary tree where values smaller than the root are stored on the left, and larger values on the right.

16. Graph 
A collection of nodes (vertices) connected by edges, used to model relationships such as social networks or maps.

17. Sorting 
The process of arranging data in a specific order, such as ascending or descending, to improve searching and processing.

18. Searching 
The process of finding a specific element within a collection of data using algorithms like Linear Search or Binary Search.

19. Time Complexity 
A measure of how the execution time of an algorithm grows as the input size increases, commonly represented using Big-O notation.

20. Space Complexity 
A measure of the amount of memory an algorithm requires relative to the input size, helping evaluate memory efficiency.

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