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...
Clasificación: | Libro Electrónico |
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Autor principal: | |
Formato: | Electrónico eBook |
Idioma: | Inglés |
Publicado: |
Boca Raton, FL ; New York, NY :
CRC Press, an imprint of the Taylor & Francis Group,
[2017]
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Colección: | Chapman & Hall/CRC the R series (CRC Press)
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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.