Data Science and Machine Learning Interview Questions Using Python: A Complete Question Bank to Crack Your Interview, 2nd Edition

Know Data science with numpy, pandas, scipy, sklearn

Key Features

• Questions related to core/basic Python, Excel, basic and advanced statistics are included
• Book will prove to be a companion whenever you want to go for an interview
• Simple to use words have been used in the answers for the questions to help ease of remembering

Description

Data science and Machine learning interview questions using Python, a book which is a true companion of people aspiring for data science and machine learning, and it provides answers to most asked questions in an easy to remember and presentable form. Book mainly intended to be used as last-minute revision, before the interview, as all the important concepts and various terminologies have been given in a very simple and understandable format. Many examples have been provided so that the same can be used while giving answers in an interview. The book is divided into six chapters, which starts with the Data Science Basic Questions and Terms then covers the questions related to Python Programming, Numpy, Pandas, Scipy, and its Applications, then at the last covers Matplotlib and Statistics with Excel Sheet.

• You can learn the basic concept and terms related to Data Science, python programming
• You will get to learn how to program in python, basics of Numpy
• You will get familiarity with the questions asked in an interview related to Pandas and learn the concepts of Scipy, Matplotib, and Statistics with Excel Sheet

Who this book is for

The book is mainly intended to help people represent their answer in a sensible way to the interviewer. The answers have been carefully rendered in a way to make things quite simple and yet represent the seriousness and complexity of the matter. Since data science is incomplete without mathematics, we have also included a part of the book dedicated to statistics.

About the Author

Vishwanathan has twenty years of hard code experience in the software industry spanning across many multinational companies and domains. Playing with data to derive meaningful insights has been his domain, and that is what took him towards data science and machine learning.