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Undergraduate courses can be offered on either a general data science basis, focusing on building data science foundations and data and analytics computing, or specific areas such as data engineering, predictive modeling, and visualization. Double degrees or majors might be offered to train professionals who will gain knowledge and abilities across disciplines such as business and analytics or statistics and computing. A master of data science and analytics program aims to train specialists and foster the talent of those who can conduct a deep understanding of data and undertake analytics tasks in data mining, knowledge discovery, and machine learning-based advanced analytics. Interdisciplinary experts can be trained from those who have a solid foundation in statistics, business, social science, or other specific disciplines and can integrate data-driven exploration technologies with disciplinary expertise and techniques. A critical area in which data science and analytics should be incorporated is in MBA courses. This is where the next generation of business leaders can be trained for the new economy and a global view of economic growth. A PhD in data science and analytics program aims to train high-level talent and specialists who have independent thinking, leadership, research, innovation, and better practices for theoretical innovation to manage the significant knowledge and capability gaps, and for substantial economic innovation and raising productivity. Interdisciplinary research is encouraged to train leaders who have a systematic and strategic understanding of the what, how, and why of data and economic innovation.