The business world today is an ultra-competitive environment where different organizations are scrambling to find competitive advantage through business intelligence. In many fields, organizations are inundated with a huge amount of data. This valuable data is often languishing in databases and data warehouses. The problem is that organizations do not have enough trained human analysts to turn this data into information and knowledge. This course provides an introduction to the business intelligence process, including data collection, data exploration, data mining, data analysis, and model evaluation. It is designed to familiarize students with data collection, storage, analysis, and gleaming actionable information from this data. The course focuses on building the skills and competence of students by covering topics such as web data collection, open data, web analytics, data modeling, data mining development process, data preprocessing and exploration, and introduces students to different data analysis techniques such as regression, k-nearest neighbor, decision trees, neural networks, clustering, and social network analysis. Learning activities include lectures, case-based studies, web data collection, and demonstrations using the R software tool. The course does not expect students to have any prior computer programming skills nor heavy statistical analysis experience. Business intelligence, including the process of exploring large data sets in order to bring out knowledge and significant information they may contain, will empower firms to uncover useful patterns and trends from existing data sources. Organizations will use business intelligence to guide their actions and the decision-making processes. This course is designed to allow students an introduction to this process.