Pragmatic advanced analytics is the application of data science techniques to real-world problems. The goal of pragmatic data science is to use data to solve problems in the most efficient and effective way possible.
In contrast, theoretical advanced analytics is a term used to describe the application of advanced analytics techniques to problems that may not have a real-world counterpart. The goal of theoretical advanced analytics is to explore the potential of data and analytics to solve problems that have not been solved before.
What are common algorithms used for advanced analytics?
There is no single answer to this question, as different algorithms may be used for different purposes within pragmatic data science. However, some commonly used algorithms include decision trees, linear regression, logistic regression, and support vector machines.
What is the difference between data analysis and advanced analytics?
There is no definitive answer to this question, as the term "advanced analytics" can mean different things to different people. Generally speaking, advanced analytics refers to a more sophisticated and complex form of data analysis that goes beyond simple descriptive statistics to provide insights that can help organizations make better decisions. This can include predictive and prescriptive analytics, as well as more sophisticated forms of data visualization.