Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and much more challenging.
Traditionally, most researchers perform statistical analysis using historical samples of data. The main downside of this process is that conclusions drawn from statistical analysis are limited. In fact, researchers usually struggle to uncover hidden patterns and unknown correlations from target data. Aside from applying statistical analysis, machine learning has emerged as an alternative. This process yields a more accurate predictive model with the data inserted into a learning algorithm. Through machine learning, the analysis of business operations and processes is not limited to human-scale thinking. Machine-scale analysis enables businesses to discover hidden value in big data.
The most widely used tool for machine learning and data analysis is the R language. In addition to being the most popular language used by data scientists, R is open source and is free for use for all users. The R programming language offers a variety of learning packages and visualization functions, which enable users to analyze data on the fly. Any user can easily perform machine learning with R on their dataset without knowing every detail of the mathematical models behind the analysis.
