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

SciPy is an open-source Python-based library that provides support for mathematical, engineering, technical, and scientific computing. SciPy packages are generally needed for scientific computing. Scipy functions are optimized to perform high-level executions. The library is very easy to use, and has good documentation support. It can work with NumPy arrays, as SciPy is built on top of NumPy. Additionally, SciPy contains more data science-related functions than NumPy. It is freely available, as it was created under the BSD license, which allows users to access SciPy's source code and use it in any way.

Learn about Python's scientific computing library, SciPy. SciPy is a Python open-source library which has functions that supports mathematical operations, scientific computing, and other technical problems.

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

SciPy