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Practical Data Science Cookbook - Second Edition.
Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Pyth...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2017.
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| Edición: | 2nd ed. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Tattar, Prabhanjan. | |
| 245 | 1 | 0 | |a Practical Data Science Cookbook - Second Edition. |
| 250 | |a 2nd ed. | ||
| 260 | |a Birmingham : |b Packt Publishing, |c 2017. | ||
| 300 | |a 1 online resource (428 pages) | ||
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| 505 | 0 | |a Cover ; Copyright ; Credits; About the Authors; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Preparing Your Data Science Environment ; Understanding the data science pipeline; How to do it ... ; How it works ... ; Installing R on Windows, Mac OS X, and Linux; How to do it ... ; How it works ... ; See also; Installing libraries in R and RStudio; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Installing Python on Linux and Mac OS X; Getting ready; How to do it ... ; How it works ... ; See also; Installing Python on Windows. | |
| 505 | 8 | |a How to do it ... How it works ... ; See also; Installing the Python data stack on Mac OS X and Linux; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Installing extra Python packages; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Installing and using virtualenv; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Chapter 2: Driving Visual Analysis with Automobile Data with R ; Introduction; Acquiring automobile fuel efficiency data; Getting ready; How to do it ... ; How it works ... ; Preparing R for your first project. | |
| 505 | 8 | |a Getting readyHow to do it ... ; There's more ... ; See also; Importing automobile fuel efficiency data into R; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Exploring and describing fuel efficiency data; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Analyzing automobile fuel efficiency over time; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Investigating the makes and models of automobiles; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also. | |
| 505 | 8 | |a Chapter 3: Creating Application-Oriented Analyses Using Tax Data and Python Introduction; An introduction to application-oriented approaches; Preparing for the analysis of top incomes; Getting ready; How to do it ... ; How it works ... ; Importing and exploring the world's top incomes dataset; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Analyzing and visualizing the top income data of the US; Getting ready; How to do it ... ; How it works ... ; Furthering the analysis of the top income groups of the US; Getting ready; How to do it ... ; How it works ... | |
| 505 | 8 | |a Reporting with Jinja2Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Repeating the analysis in R; Getting ready; How to do it ... ; There's more ... ; Chapter 4: Modeling Stock Market Data ; Introduction; Requirements; Acquiring stock market data; How to do it ... ; Summarizing the data; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Cleaning and exploring the data; Getting ready; How to do it ... ; How it works ... ; See also; Generating relative valuations; Getting ready; How to do; How it works ... ; Screening stocks and analyzing historical prices. | |
| 500 | |a Getting ready. | ||
| 520 | |a Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts 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 real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides 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 using the two most popular programming languages for data analysis--R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization Downloa ... | ||
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