R: Data Analysis and Visualization
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Free ChapterTable of Contents
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R: Data Analysis and Visualization
-
I. Module 1: Data Analysis with R
- I. Module 1: Data Analysis with R
- 1. RefresheR
- 2. The Shape of Data
- 3. Describing Relationships
- 4. Probability
- 5. Using Data to Reason About the World
- 6. Testing Hypotheses
- 7. Bayesian Methods
- 8. Predicting Continuous Variables
- 9. Predicting Categorical Variables
- 10. Sources of Data
- 11. Dealing with Messy Data
- 12. Dealing with Large Data
- 13. Reproducibility and Best Practices
-
II. Module 2: R Graphs
- II. Module 2: R Graphs
- 1. R Graphics
- 2. Basic Graph Functions
- 3. Beyond the Basics – Adjusting Key Parameters
- 4. Creating Scatter Plots
- 5. Creating Line Graphs and Time Series Charts
- 6. Creating Bar, Dot, and Pie Charts
- 7. Creating Histograms
- 8. Box and Whisker Plots
- 9. Creating Heat Maps and Contour Plots
- 10. Creating Maps
- 11. Data Visualization Using Lattice
- 12. Data Visualization Using ggplot2
- 13. Inspecting Large Datasets
- 14. Three-dimensional Visualizations
- 15. Finalizing Graphs for Publications and Presentations
-
III. Module 3: Learning Data Mining with R
- III. Module 3: Learning Data Mining with R
- 1. Warming Up
- 2. Mining Frequent Patterns, Associations, and Correlations
- 3. Classification
- 4. Advanced Classification
- 5. Cluster Analysis
- 6. Advanced Cluster Analysis
- 7. Outlier Detection
- 8. Mining Stream, Time-series, and Sequence Data
- 9. Graph Mining and Network Analysis
- 10. Mining Text and Web Data
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IV. Module 4: Mastering R for Quantitative Finance
- IV. Module 4: Mastering R for Quantitative Finance
- 1. Time Series Analysis
- 2. Factor Models
- 3. Forecasting Volume
- 4. Big Data – Advanced Analytics
- 5. FX Derivatives
- 6. Interest Rate Derivatives and Models
- 7. Exotic Options
- 8. Optimal Hedging
- 9. Fundamental Analysis
- 10. Technical Analysis, Neural Networks, and Logoptimal Portfolios
- 11. Asset and Liability Management
- 12. Capital Adequacy
- 13. Systemic Risks
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V. Module 5: Machine Learning with R module
- V. Module 5: Machine Learning with R module
- 1. Introducing Machine Learning
- 2. Managing and Understanding Data
- 3. Lazy Learning – Classification Using Nearest Neighbors
- 4. Probabilistic Learning – Classification Using Naive Bayes
- 5. Divide and Conquer – Classification Using Decision Trees and Rules
- 6. Forecasting Numeric Data – Regression Methods
- 7. Black Box Methods – Neural Networks and Support Vector Machines
- 8. Finding Patterns – Market Basket Analysis Using Association Rules
- 9. Finding Groups of Data – Clustering with k-means
- 10. Evaluating Model Performance
- 11. Improving Model Performance
- 12. Specialized Machine Learning Topics
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A. Reflect and Test Yourself Answers
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B. Bibliography
-
Index
About this
book
The R learning path created for you has five connected modules, which are a mini-course in their own right. As you complete each one, you'll have gained key skills and be ready for the material in the next module!
This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility.
The second place to explore is R Graphs, which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. You'll learn how to produce, customize, and publish advanced visualizations using this popular and powerful framework.
With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs.
The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions.
Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, and so on.
- Publication date:
- June 2016
- Publisher
- Packt
- ISBN
- 9781786463500
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