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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.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.
- 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 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.
- 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.
- 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.