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- Title: What is Data Science?
- Author(s) JMike Loukides
- Publisher: y O’Reilly Media, Inc.,; eBook (Online Edition))
- Hardcover/Paperback: N/A
- eBook: HTML and PDF
- Language: English
- ISBN-10/ASIN: 1334177049
- ISBN-13: 978-1449327552
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The future belongs to the companies and people that turn data into products. This book examines the many sides of data science — the technologies, the companies and the unique skill sets.
Reviews, Rating, and Recommendations: Related Book Categories:- Data Science
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Introduction to Data Science (Rafael A. Irizarry)
Introduces concepts and skills that can help tackling real-world data analysis challenges. Covers concepts from probability, statistical inference, linear regression, and machine learning. Helps developing skills such as R programming, data wrangling, etc.
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R for Data Science: Visualize, Model, Transform, Tidy, Import
This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.
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Introduction to Probability for Data Science (Stanley Chan)
This book is an introductory textbook in undergraduate probability in the context of data science to emphasize the inseparability between data (computing) and probability (theory) in our time, with examples in both MATLAB and Python.
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Data Science at the Command Line, 2nd Ed. (Jeroen Janssens)
This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. Learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.
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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.
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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.
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Python Data Science Handbook: Essential Tools (Jake VanderPlas)
Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all - IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.
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Regression Models for Data Science in R (Brian Caffo)
The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming.
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The Ultimate Guide to Effective Data Cleaning
With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Experts share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges.
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Elements of Data Science (Allen B. Downey)
This book is an introduction to data science for people with no programming experience. The goal is to present a small, powerful subset of Python that allows you to do real work in data science as quickly as possible.
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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.
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Exploring Data Science (Nina Zumel, et al)
This book introduces readers to various areas in data science and explains which methodologies work best for each, with practical examples in R, Python, and other languages.
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