R for Data Science - R Data Science Tips, Solutions and Strategies
暫譯: R 數據科學 - R 數據科學技巧、解決方案與策略

Key Features

• Explore R’s key features and discover why it was built for data science
• From data mining to analysis and visualization - learn each step in the data workflow
• Get started with predictive analytics with R's powerful machine learning packages

Book Description

Make sure you’re ready to tackle Big Data with R for Data Science. Created to help you harness R and its extensive range of scientific tools for a wide range of data analysis tasks, this book covers the fundamental elements of data science from data mining to data analysis to visualization. If you want to learn the latest cutting-edge techniques being used by data scientists, and to experience the extensive features and power of R, follow each step in this vital collection of data science tutorials.

The book is broken down into four sections – data mining, data analysis and data visualization and machine learning, ensuring that you gain insights into the core components of data science. Learn different data mining patterns and sequences; get to grips with the latest in text mining, and then explore a range of approaches to data analysis including clustering, regression analysis and correlation. Find out how to draw insights from data with data visualization techniques and tools, to bring meaning to your data, and learn how to use R for machine learning and predictive analytics.

Better data science can transform the world – start learning how R can help you make data more meaningful with R for Data Science.

What you will learn

• Develop, execute, and modify R scripts
• Learn how to use different data mining sequences
• Find out how to organize your data effectively
• Produce high-quality data visualizations
• Get to grips with a number of approaches to the statistical analysis of data
• Learn how to cultivate a strategic approach to your data to use the right tools, models and visualizations to get the job done

About the Author

Dan Toomey has been developing applications for over 20 years. He has worked in a variety of industries and companies in different roles, from a sole contributor to VP and CTO. For the last 10 years or so, he has been working with companies in the eastern Massachusetts area.

Table of Contents

1. Data Mining Patterns
2. Data Mining Sequences
3. Text Mining
4. Data Analysis - Regression Analysis
5. Data Analysis - Correlation
6. Data Analysis - Clustering
7. Data Visualization - R Graphics
8. Data Visualization - Plotting
9. Data Visualization - 3D
10. Machine Learning in Action
11. Predicting Events with Machine Learning
12. Supervised and Unsupervised Learning