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

Feature engineering refers to generating and extracting features. Features play a significant role in solving any data science problems. Often our datasets are messy and contain improper unstructured data. To create an efficient model, we need to make data in a way that the model can understand and train it. In this section, you will learn the need for feature engineering and how we can benefit from it to build better models.

Consider an example of a salary dataset. We were given a few attributes of a person like age, sex, occupation, maximum education, working hours and we have to predict the salary range of a person. We will use a similar dataset in upcoming sections. 

Delves further into feature engineering. 

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

The Need for Feature Engineering