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.

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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 stack is an abstract data type with the following operations:

<li><b>Push(value):</b> Add a value into the underlying collection.

<li><b>Pop(): </b>Remove the most recently pushed value from the collection.

<li><b>Top():</b> Get the most recent key without removing it from the collection.

All the above operations are completed in O(1) time.



We can think of the stack as a pile of elements. Elements can be added to the top of the pile. To extract any element, we have to remove the elements on top of it. 

The stack is also known as the Last In First Out (LIFO) data structure meaning that the element inserted last is the one that comes out first.

<h2> Applications of the stack </h2>

The following are the general applications of the stack in general. The application are used to build data science solutions in different ways.


Introduction to the data type stack. The stack is an abstract data type of defined operations. In this lesson, we will learn about the stack and its applications. We will also do some programming exercises to help you understand the concept through practice.


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

Stack

Applications of the stack