• Title The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists
• Author(s) Carl Shan, William Chen, Henry Wang, Max Song
• Publisher: Data Science Bookshelf, The (June 19, 2015)
• Hardcover/Paperback 346 pages
• eBook PDF
• Language: English
• ISBN-10/ASIN: 0692434879
• ISBN-13: 978-0692434871
• Share This:  

Book Description

The Data Science Handbook contains interviews with 25 of the world s best data scientists. We sat down with them, had in-depth conversations about their careers, personal stories, perspectives on data science and life advice.

In The Data Science Handbook, you will find war stories from DJ Patil, US Chief Data Officer and one of the founders of the field. You ll learn industry veterans such as Kevin Novak and Riley Newman, who head the data science teams at Uber and Airbnb respectively.

You ll also read about rising data scientists such as Clare Corthell, who crafted her own open source data science masters program. This book is perfect for aspiring or current data scientists to learn from the best. It s a reference book packed full of strategies, suggestions and recipes to launch and grow your own data science career.

This book is a compilation of in-depth interviews with 25 data scientists from a wide selection of backgrounds, disciplines, and industries, where they share their insights, stories, and advice.

• The four co-authors are all practicing data scientists. They've worked in places like billion-dollar technology startup Quora, machine learning startup Ayasdi, and e-commerce website Etsy. Between the four of them, the authors have done things from applying machine learning to public policy under President Obama's former Chief Scientist to using data-driven methods to find ways to invest multi-billion dollar investment funds.

• Amazon

• Data Science
• Data Analysis and Data Mining, Big Data
• The R Programming Language
• Statistics, Mathematical Statistics, and SAS Programming

• The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists
• The Mirror Site (1) -PDF

• Applied Data Science and Smart Systems (Jaiteg Singh, et al.)

  Focussed on innovation and practices in science, technology, and management such as artificial intelligence and machine learning, software engineering, automation, data science, business computing, data communication, and computer networks.

• Statistical Inference: Algorithms, Evidence, and Data Science

  A masterful guide to how the inferential bases of classical statistics can provide a principled disciplinary frame for the data science of the twenty-first century. Every aspiring data scientist should carefully study this book, use it as a reference.

• Computational and Inferential: The Foundations of Data Science

  Step by step, you'll learn how to leverage algorithmic thinking and the power of code, gain intuition about the power and limitations of current machine learning methods, and effectively apply them to real business problems.

• Data Science: Theories, Models, Algorithms, and Analytics

  It provides a bucket full of information regarding Data Science, covers a wide variety of sections by giving access to theories, data science algorithms, tools and analytics. You'll explore the right approach to best practices to guide you along the way.

• Information Theory for Data Science (Changho Suh)

  This book aims at demonstrating modern roles of Information Theory in a widening array of data science applications, focuses on applications that arise in data science, including social networks, ranking, and machine learning.

• Veridical Data Science: Data Analysis and Decision Making

  Data science is not simply a subfield of statistics or computer science. Instead, it is the integration of statistical and computational thinking into real-world domain problems in science, technology, and beyond.

• Statistical Inference for Data Science (Brian Caffo)

  The book gives a rigorous treatment of the elementary concepts in statistical inference from a classical frequentist perspective. The ideal readers are quantitatively literate and have a basic understanding of statistical concepts and R programming.

• Julia Data Science (Jose Storopoli, et al.)

  An accessible, intuitive, and highly efficient makes Julia a formidable language for data science. This book will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work.