Practical data science cookbook : 89 hands-on recipes to help you complete real-world data science projects in R and Python / Tony Ojeda... [y otros].
Material type:
TextLanguage: English Publisher: Birmingham : Packt Publishing, 2014Description: 396 páginas : IlustracionesContent type: texto Media type: computadora Carrier type: recurso en líneaISBN: 9781783980246Subject(s): Minería de datos | Estadística matemática -- Procesamiento de datos | Programas para computadorDDC classification: 006.331 | Item type | Current library | Collection | Call number | Copy number | Status | Notes | Date due | Barcode | Item holds |
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Recurso electrónico
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Biblioteca CESA
Diagonal 34 A No. 5 A - 23 Casa Incolda PBX: 339 53 00 serviciosbiblioteca@cesa.edu.co |
Recursos de información electrónicos y digitales | EBR006.331 / P712p 2014 (Browse shelf(Opens below)) | Ej. 1 | Available | Disponible Kindle No 0048, 166, 167, 171, 172 | LE00130 |
Disponible Kindle.
Incluye índice.
Chapter 1: Preparing Your Data Science Environment; Introduction; Understanding the data science pipeline; Installing R on Windows, Mac OS X, and Linux; Installing libraries in R and RStudio; Installing Python on Linux and Mac OS X; Installing Python on Windows; Installing the Python data stack on Mac OS X and Linux; Installing extra Python packages; Installing and using virtualenv; Chapter 2: Driving Visual Analysis with Automobile Data (R); Introduction acquiring automobile fuel efficiency dataPreparing R for your first project; Importing automobile fuel efficiency data into R; Exploring and describing the fuel efficiency data; Analyzing automobile fuel efficiency over time; Investigating the makes and models of automobiles; Chapter 3: Simulating American Football Data (R); Introduction; Acquiring and cleaning football data; Analyzing and understanding football data; Constructing indexes to measure offensive and defensive strength; Simulating a single game with outcomes decided by calculations Simulating multiple games with outcomes decided by calculations...
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.
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