Cargando…

The essentials of data science : knowledge discovery using R /

"The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Williams, Graham J. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Boca Raton, FL ; New York, NY : CRC Press, an imprint of the Taylor & Francis Group, [2017]
Colección:Chapman & Hall/CRC the R series (CRC Press)
Temas:
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Chapter 1 Data Science
  • 1.1 Exercises
  • Chapter 2 Introducing R
  • 2.1 Tooling For R Programming
  • 2.2 Packages and Libraries
  • 2.3 Functions, Commands and Operators
  • 2.4 Pipes
  • 2.5 Getting Help
  • 2.6 Exercises
  • Chapter 3 Data Wrangling
  • 3.1 Data Ingestion
  • 3.2 Data Review
  • 3.3 Data Cleaning
  • 3.4 Variable Roles
  • 3.5 Feature Selection
  • 3.6 Missing Data
  • 3.7 Feature Creation
  • 3.8 Preparing the Metadata
  • 3.9 Preparing for Model Building
  • 3.10 Save the Dataset
  • 3.11 A Template for Data Preparation
  • 3.12 Exercises
  • Chapter 4 Visualising Data
  • 4.1 Preparing the Dataset
  • 4.2 Scatter Plot
  • 4.3 Bar Chart
  • 4.4 Saving Plots to File
  • 4.5 Adding Spice to the Bar Chart
  • 4.6 Alternative Bar Charts
  • 4.7 Box Plots
  • 4.8 Exercises
  • Chapter 5 Case Study: Australian Ports
  • 5.1 Data Ingestion
  • 5.2 Bar Chart: Value/Weight of Sea Trade
  • 5.3 Scatter Plot: Throughput versus Annual Growth
  • 5.4 Combined Plots: Port Calls
  • 5.5 Further Plots
  • 5.6 Exercises
  • Chapter 6 Case Study: Web Analytics
  • 6.1 Sourcing Data from CKAN
  • 6.2 Browser Data
  • 6.3 Entry Pages
  • 6.4 Exercises
  • Chapter 7 A Pattern for Predictive Modelling
  • 7.1 Loading the Dataset
  • 7.2 Building a Decision Tree Model
  • 7.3 Model Performance
  • 7.4 Evaluating Model Generality
  • 7.5 Model Tuning
  • 7.6 Comparison of Performance Measures
  • 7.7 Save the Model to File
  • 7.8 A Template for Predictive Modelling
  • 7.9 Exercises
  • Chapter 8 Ensemble of Predictive Models
  • 8.1 Loading the Dataset
  • 8.2 Random Forest
  • 8.3 Extreme Gradient Boosting
  • 8.4 Exercises
  • Chapter 9 Writing Functions in R
  • 9.1 Model Evaluation
  • 9.2 Creating a Function.
  • 9.3 Function for ROC Curves
  • 9.4 Exercises
  • Chapter 10 Literate Data Science
  • 10.1 Basic LATEX Template
  • 10.2 A Template for our Narrative
  • 10.3 Including R Commands
  • 10.4 Inline R Code
  • 10.5 Formatting Tables Using Kable
  • 10.6 Formatting Tables Using XTable
  • 10.7 Including Figures
  • 10.8 Add a Caption and Label
  • 10.9 Knitr Options
  • 10.10Exercises
  • Chapter 11 R with Style
  • 11.1 Why We Should Care
  • 11.2 Naming
  • 11.3 Comments
  • 11.4 Layout
  • 11.5 Functions
  • 11.6 Assignment
  • 11.7 Miscellaneous
  • 11.8 Exercises
  • Bibliography
  • Index.