The essentials of data science : knowledge discovery using R / Graham J. Williams

By:

Williams, Graham J [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Chapman & Hall / CRC the R seriesPublisher: Boca Raton ; London ; New York : CRC Press, [2017]Description: 1 Online-Ressource (343 p)ISBN:

1351647490
9781351647496
1498740014
9781498740012

Subject(s):

Additional physical formats: 1138088633. | 9781138088634. | 9781498740005. | Erscheint auch als: Essentials of data science. Druck-Ausgabe Boca Raton : CRC Press, Taylor & Francis Group, 2017. xix, 322 SeitenRVK: RVK: ST 601LOC classification:

QA76.9.B45

Online resources:

Zugang im Netz des KIT

Summary: 10.1 Basic LATEX Template10.2 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; IndexSummary: 3.11 A Template for Data Preparation3.12 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 ExercisesSummary: Cover ; Half Title ; Series Editors; Published Titles; Title ; Copyright ; Dedication ; Preface; Contents; List of Figures; List of Tables; 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 DatasetSummary: Chapter 7 A Pattern for Predictive Modelling7.1 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 SciencePPN: PPN: 898096936Package identifier: Produktsigel: ZDB-4-NLEBK

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