This article discusses the analytical method of Hierarchical Clustering and how it can be used within an organization for analytical purposes.
This article discusses the analytical method of Hierarchical Clustering and how it can be used within an organization for analytical purposes.
This article provides a brief explanation of the SVM Classification method of analytics.
This article presents a brief explanation of Outliers, and how this type of analysis is used.
In this article, we will discuss the Decision Tree analysis method.
This article describes chi square test of association and hypothesis testing.
As with any other software implementation, a business may choose a solution that has a basic toolkit and features and spend time and money on customization, believing that they cannot find something that is designed to meet the needs of their industry or business function.
Self-serve data preparation allows business users with average technical skills to gather and prepare data for analysis without the help of an IT professional or a data scientist. So, why is that important? Data prep is often the forgotten step in advanced analytics but, without a self-service data preparation tool, the process can take a long time and it can result in incomplete data, data that is hard to analyze and, sometimes, a total work stoppage while IT or a data scientist attempts to sort through the issues and untangle the mess.
Imagine you are trying to search for something in a store, but there is no one there to help you and all of the products are mixed up. You have to walk up and down every aisle, trying to find what you want and if you want to compare two things you will have to remember where you found that other product or drag it with you and then try to remember where to put it when you are ready to put it back. Not fun!
We all know the frustration of seeing something you really like, something you really need and then finding out that the price is not as low as you thought. Whether it is an item of clothing or those concert tickets with all the service fees added on, you know what I am talking about.
Predictive Analytics is no longer limited to data scientists. Today, predictive analytics is, and must be, accessible to business users, if your enterprise is to grow and respond to the need for data democratization and increased productivity within the enterprise and to the rapid changes in the market, competition, resource and supplier needs and customer buying behavior. Every business user must have the tools to analyze data and make accurate, timely predictions and decisions.