The ultimate guide to data science interviews. Developed by FAANG engineers, it covers Python, algorithms, ML concepts, and interview questions. Get interview-ready in just a few hours.

DSTar.tar.gz

code-job

r-job

This course will increase your skills to crack the data science or machine learning interview. You will cover all the most common data science and ML concepts coupled with relevant interview questions.
You will start by covering Python basics as well as the most widely used algorithms and data structures. From there, you will move on to more advanced topics like feature engineering, unsupervised learning, as well as neural networks and deep learning.
This course takes a non-traditional approach to interview prep, in that it focuses on data science fundamentals instead of open-ended questions.

In all, this course will get you ready for data science interviews. By the time you finish this course, you will have reviewed all the major concepts in data science and will have a good idea of what interview questions you can expect.

Data Science Interview Handbook

The queue is similar to the stack but with some fundamental differences. It’s an abstract data type with the following operations defined on it:

<li> Enque (Key): Insert an element in the collection.

<li> Deque(): Remove the oldest added element from the collection.

<li> isEmpty(): Determine whether the queue is empty or not.


<li> isFull(): Determine whether the queue is full or not.


All of the above operations take O(1) time complexity. The queue can be implemented using a linked list and an array.

An introduction to the queue data structure that supports First In First Out operation. In this lesson, we will learn about the queue and its applications, and will answer a few programming questions.


Are You Ready to Become a Data Scientist?

Python Basics

Python Libraries

More Data Science Tools

Data Structures and Algorithms - I

Data Structures and Algorithms - II

Statistics and Probability

Feature Engineering

Basics of Machine Learning

Regression

Classification

Unsupervised Learning

Advanced Topics in Machine Learning

Conclusion

Queue