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

Deep learning dives into every domain. It was started way back in the 90s. The algorithms did what they were supposed to do at that time; they gave better predictions. Over time, due to their speed, their popularity has decreased. Now, with the enhancement in GPUs, it is more popular than ever before. We can train with 100x speed, and it offers good library support. We also get predictions in real-time. This makes deep learning solutions appropriate for today’s real-world applications.

First, you will learn a few basics of deep learning, and then we will move deeper into this topic.


Learn the basics of deep learning and neural network algorithms.


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

Neural Network and Deep Learning