The necessity to handle many, complex statistical analysis projects is hitting statisticians and analysts across the globe. With increasing interest in data analysis, R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. With its constantly growing community and plethora of packages, R offers functionality for dealing with a truly vast array of problems.
It's been decades since the R programming language was born and has become an eminent and well known not only within the community of scientist, but also in the wider community of developers. It has grown into a powerful tool to help developers produce efficient and consistent source code for data related tasks. The R development team and independent contributors have created good documentation so getting started programming with R isn't that hard.
Module 1, Learning Data mining with R, will teach you how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on RHadoop projects. You will finish this module by feeling confident in your ability to know which data mining algorithm to apply in any situation.
Module 2, R Data Mining Blueprints, explores data mining techniques and shows you how to apply different mining concepts to various statistical and data applications in a wide range of fields. You will learn about R and its application to data mining, and give you relevant and useful information you can use to develop and improve your applications. This module will help you complete complex data mining cases and guide you through handling issues you might encounter during projects.
Module 3, Social Media Mining with R, begins by introducing you to the topic of social media data, including its sources and properties. It then explains the basics of R programming in a straightforward, unassuming way. Thereafter, you will be made aware of the inferential dangers associated with social media data and how to avoid them, before describing and implementing a suite of social media mining techniques.