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

A tree is a non-linear data structure, consisting of nodes that are connected in a hierarchical way. Hierarchy exists everywhere. Consider the hierarchy of a company where managers report to directors, directors report to CXOs, CFOs, and CTOs, and the CMO reports to the CEO.

Every tree structure contains a tree. Each tree has one member that is the root node. There is no superior node to the root node. 




This lesson delves further into the tree data structure. The tree data structure is used to solve a lot of paths finding related problems efficiently. This chapter introduces the  binary tree,  tree terminologies, and tree traversal.  

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

Trees