Information is the oil of the 21st century, and analytics is the combustion engine.
When faced with a business challenge, people need tools to overcome issues and find ways to create value. In the analytics forum, frequently that begins very early on with deciding what tool should be used. This is the third consideration before work is begun. The first two, where to store the data and how to prepare it for analysis, were discussed in Chapters 1 and 2. This chapter details some tools that are commonly used. Some I have great familiarity with and, in fact, contributed to their development. Others I have used as tools, just as you or your teams will, and a few I have not used personally, but they also should be considered. I have tried to provide a balanced assessment of the strengths and weakness of each tool.
Weka (Waikato Environment for Knowledge Analysis) is an open source data mining offering, fully implemented in Java, and primarily developed at the University of Waikato, New Zealand. Weka is notable for its broad range of extremely advanced training algorithms, its work flow graphical user interface (GUI), and its incorporation of data visualization tools. Weka allows users access to its sophisticated data mining routines through a GUI designed for productive data analysis, a command line interface, and a Java application programming interface (API). However, Weka does not scale well for big data analytics, as it is ...