The era has come when data science is changing the world and everyone s life. Data Science Interviews Exposed is the first book in the industry that covers everything you need to know to prepare for a data science career: from job market overview to job roles description, from resume preparation to soft skill development, and most importantly, the real interview questions and detailed answers. We hope this book can help the candidates in the data science job market, as well as those who need guidance to begin a data science career.

The full list of topics are as follows:

Introduction

This chapter presents an overview to the data science job market and the book organization.

Find the Right Job Roles

Get confused about the various data science job titles? This chapter provides a detailed description for each of them, the differences among them, as well as the guidance for choosing the one that suits you the most.

Find the Right Experience

Don't know how to prepare yourself with the right experience to meet the job requirements and your career goals? This chapter helps you to identify the experience you need to land your dream position. It also provides suggestions for new graduates as well as candidates from a different industry who want to transfer to data science field.

Get Ready for the Interviews

Think you have a clear goal and have possessed all the required skill sets, but just don t know how to get job interviews? This chapter walks you through how to build good resumes and professional profiles that would bring you the right exposure to the right person -- recruiters and hiring managers.

Polish Your Soft Skills

Heard of your competent peers failing job interviews and want to know why? This chapter reveals the secrets that most companies don t talk about publicly -- the soft skills. What are behavior questions, why are they important, how do you prepare for them? You will find the answer here.

Technical Interview Questions

An interview is not a pop quiz. You should take the time to practice on real interview problems and learn their patterns. This chapter lists eight major topics that are frequently covered by data science job interviews, associated with example interview questions for each of them. All of them are either real interview questions or adapted from real interview questions:

Probability Theory

Statistical Inference

Dataset Manipulation

Product, Metrics and Analytics

Experiment Design

Coding

Machine Learning

Brain Teasers

Solutions to Technical Interview Questions

This chapter attaches the solutions and thought process for each question in the previous chapter. We hope the readers can grasp the key points behind each of them, hence be able to apply the approaches to other similar questions in the real interviews.

We, the Davocado team, are a group of five passionate data science professionals who have been growing our career in the golden time of data science. We are from leading technology companies, where data science is the ultimate motor that keeps changing the business and the whole world.

Jane is a machine learning scientist at Amazon.com. She received her PhD in computer science in Purdue University. During her 5 years in Amazon.com, she has been doing customer review analysis, product pricing, demand forecasting, image processing and pattern recognition and recommendation systems. She is passionate about identifying business opportunities from data, and always enjoys learning new technologies.

Iris is a data scientist at LinkedIn. She received her degree in University of Michigan, Ann Arbor, studying Mathematics, Economics and Computer Science. She has been performing web analytics as well as consumer (behavior) analytics. Her passion aligns with applying data science to make awesome products.

Yanping received his PhD in machine learning from University of Washington. His research interests include reinforcement learning and neural networks. He worked at Facebook on recommendation systems and he is now working at Google on marketing technologies for creative content. He enjoys building scalable systems that can automatically make data driven decisions.

Feng received his Master's degree in Computer Science in Case Western Reserve University, specializing on machine learning and artificial intelligence. He worked as a software development engineer in Amazon, focusing on building ML systems and developing ML/NLP solutions to improve catalog data quality. He has broad interests in every technical aspect of a software system, from front end to back end. He believes a robust system is the foundation of a successful data product.

Ian is a data scientist at Microsoft. He received his PhD in Computer Science from North Carolina State University. Ian has extensive working experience on machine learning projects in areas such as natural language processing, information extraction, and text mining. He believes in data science for social good and aspires to tame the big data beast.

We aim to reduce information asymmetry on data science landscape, to bridge the gap between the demand and supply of data science talents, and to help hundreds and thousands of data science candidates to begin and advance their career.