![]() On the other hand, there are also a number of more technical books about data mining algorithms, but these are aimed at the statistical researcher or more advanced graduate student, and do not provide the case-oriented business focus that is successful in teaching business students. Data mining, data analysis, artificial intelligence, machine learning, and many other terms are all combined in business intelligence processes that help a company or organization make decisions and learn more about their customers and potential outcomes. Preparation for the course revealed that there are a number of excellent books on the business context of data mining, but their coverage of the statistical and machine-learning algorithms that underlie data mining is not sufficiently detailed to provide a practical guide if the instructor's goal is to equip students with the skills and tools to implement those algorithms. Data mining specialists clean and prepare the data, create models, test those models against hypotheses, and publish those models for analytics or business intelligence projects. When considering BI tools, see what kinds of charts they have available and the amount of customization possible with them. A good chart can convey insights faster than a plain table of numbers. This book arose out of a data mining course at MIT's Sloan School of Management and was refined during its use in data mining courses at the University of Maryland's R. The data mining process includes projects such as data cleaning and exploratory analysis, but it is not just those practices. Data visualization is a core component of most business intelligence applications.
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