Practical Data Science Cookbook

Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta

  • 出版商: Packt Publishing - ebooks Account
  • 出版日期: 2014-09-29
  • 定價: USD $29.99
  • 售價: $1,216
  • 貴賓價: 9.5$1,155
  • 語言: 英文
  • 頁數: 448
  • 裝訂: Paperback
  • ISBN: 1783980249
  • ISBN-13: 9781783980246

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商品描述

89 hands-on recipes to help you complete real-world data science projects in R and Python

About This Book

  • Learn about the data science pipeline and use it to acquire, clean, analyze, and visualize data
  • Understand critical concepts in data science in the context of multiple projects
  • Expand your numerical programming skills through step-by-step code examples and learn more about the robust features of R and Python

Who This Book Is For

If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of data science projects, the steps in the data science pipeline, and the programming examples presented in this book. Since the book is formatted to walk you through the projects with examples and explanations along the way, no prior programming experience is required.

In Detail

As increasing amounts of data is generated each year, the need to analyze and operationalize it is more important than ever. Companies that know what to do with their data will have a competitive advantage over companies that don't, and this will drive a higher demand for knowledgeable and competent data professionals.

Starting with the basics, this book will cover how to set up your numerical programming environment, introduce you to the data science pipeline (an iterative process by which data science projects are completed), and guide you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples in the two most popular programming languages for data analysis—R and Python.