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Practical data science cookbook : 89 hands-on recipes to help you complete real-world data science projects in R and Python /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2014.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Ojeda, Tony, |e author. | |
| 245 | 1 | 0 | |a Practical data science cookbook : |b 89 hands-on recipes to help you complete real-world data science projects in R and Python / |c Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta. |
| 260 | |a Birmingham : |b Packt Publishing, |c 2014. | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | |a Print version record. | ||
| 505 | 0 | |a Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a Simulating multiple games with outcomes decided by calculationsChapter 4: Modeling Stock Market Data (R); Introduction; Acquiring stock market data; Summarizing the data; Cleaning and exploring the data; Generating relative valuations; Screening stocks and analyzing historical prices; Chapter 5: Visually Exploring Employment Data (R); Introduction; Preparing for analysis; Importing employment data into R; Exploring the employment data; Obtaining and merging additional data; Adding geographical information; Extracting state- and county-level wage and employment information. | |
| 505 | 8 | |a Visualizing geographical distributions of payExploring where the jobs are, by industry; Animating maps for a geospatial time series; Benchmarking performance for some common tasks; Chapter 6: Creating Application-oriented Analyses Using Tax Data (Python); Introduction; Preparing for the analysis of top incomes; Importing and exploring the world top incomes dataset; Analyzing and visualizing U.S. top income data; Furthering the analysis of U.S. top income groups; Reporting with Jinja2; Chapter 7: Driving Visual Analyses with Automobile Data (Python); Introduction; Getting started with IPython. | |
| 505 | 8 | |a Exploring IPython NotebookPreparing to analyze automobile fuel efficiencies; Exploring and describing the fuel efficiency data; Analyzing automobile fuel efficiency over time; Investigating the makes and models of automobiles; Chapter 8: Working with Social Graphs (Python); Introduction; Preparing to work with social networks in Python; Importing networks; Exploring subgraphs within a heroic network; Finding the strong ties; Finding key players; Exploring the characteristics of entire networks; Clustering and community detection in social networks; Visualizing graphs. | |
| 520 | |a 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. | ||
| 546 | |a English. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Object-oriented programming (Computer science) | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Mathematical statistics |x Data processing. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 6 | |a Programmation orientée objet (Informatique) | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Statistique mathématique |x Informatique. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 7 | |a COMPUTERS |x Programming |x General. |2 bisacsh | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Mathematical statistics |x Data processing |2 fast | |
| 650 | 7 | |a Object-oriented programming (Computer science) |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 700 | 1 | |a Murphy, Sean Patrick, |e author. | |
| 700 | 1 | |a Bengfort, Benjamin, |d 1984- |e author. | |
| 700 | 1 | |a Dasgupta, Abhijit, |e author. | |
| 776 | 0 | 8 | |i Print version: |a Ojeda, Tony. |t Practical data science cookbook. |d Birmingham : Packt Publishing, 2014 |z 9781783980253 |w (OCoLC)892044301 |
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