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

Python is a programming language. It was released in 1991, and has the capability of completing tasks for software development. Using Python, we can create client-server applications, connect to databases, perform operations, solve big mathematical problems, and prototype or create production-level software systems. It is easier to use than other programming languages where you have to write several lines of code just to print a word. Python is available across all major operating systems (Windows, Linux, Mac, etc.). It is not strictly object-oriented, but it can be used in that way.

With this level of flexibility, it is very convenient for a user to develop applications in Python.


This lesson goes over Python, the difference between different versions of it, its memory management, and a few simple programs.


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

Introduction to Python