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

We can use various tools offline to build ML models. Sometimes, we don’t have enough computing power to create models. Also, we may want our model to be deployed in an environment which will always be available.

To work within these constraints, we can use cloud machine learning solution providers. In the past,  people have used high-end machines and installed Python or R based libraries on their servers. Now, cloud service providers give specialized services for building machine learning and deep learning models that are fast and have an easy to understand GUI. In this lesson, we will discuss some top-tier cloud machine learning service providers.


An introduction to cloud ML service providers including AWS, Azure, Google, and IBM Watson.

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

Cloud ML Engines