Covered topics:
- Understand the scope of data science and complexities with real-world use cases
- Deep dive into the various steps involved in solving a data science problem
- Explore each execution step of data science projects and find out how to use R at each step
- Build models^ interpret^ and present using the newest of the R libraries and visualization framework tools such as D3 and Tableau
- Scale R^ and integrate it with Big Data and NoSQL data stores
- Access and learn using detailed code implementations
- Use standard and big data ETL tools for data collection
Description
Business analysts and developers are increasingly collecting^ curating^ analyzing^ and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics. This book is for those data science professionals who want to take their skills to the next level. With the help of insightful industry examples^ this book will take you to the depths of the core concepts in data science and how to use them in your day-to-day work without much hassle. The guide will cover solid R libraries and how they can be used in complex data science tasks such data manipulation^ missing data^ advanced graphics with R^ package creation in R^ machine learning^ exploring neural networks^ and so on. During the course of the book^ you will also explore advanced algorithms with R and various data visualization techniques with R. You will learn how to work with different data sources comfortably and explore advanced concepts in data science such as advanced predictive analytics^ modeling^ logistics regression^ regularization^ and data mining techniques.