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Learn to create systems and processes for data collection, mining, and forecasting to improve business decision-making. As part of the Rutgers Stackable Business Innovation Program (rSBI), the Data Forecasting and Mining Concentration is stackable with the following master's programs: Master of Information Technology and Analytics, MBA
This concentration prepares students to utilize collected data to make predictions and discern in them patterns of objects being observed. Statistical analyses, along with forecasting and data mining techniques will be taught. Classroom teaching will be combined with software use.
You can take the course listed below as individual classes or as stackable courses towards the completion of a concentration.
This course provides students with an understanding of database technology and its application in managing data resources. The conceptual, logical, and physical design of databases will be analyzed. A database management system will be used as a vehicle for illustrating some of the concepts discussed in the course.
The objective of this course is to study management’s role in the development and use of information systems that help businesses achieve their goals and objectives. This course will let students use a real software product, understand how it is developed, and how it is helping real business enterprises in achieving productivity and value. Students will learn the architecture, development methodologies, marketing infrastructure, support mechanisms, database interfaces, server security, and management issues as related to the software, customer interface, and documentation techniques. Extensive case studies will be used. Writing and presentations are major components. A professionally developed and publishable paper is due from each group of students.
Over the last few years Information Technology (IT) teams have evolved and continue evolving to establish IT organizations as business strategic partners, and CIOs and technology leaders are now included in the executive teams and are expected to play a leading role in delivering business value while solving both business and technical problems. Companies are increasing their investments in acquiring and maintaining information on themselves, the markets and on competitors, and they need systems and IT teams to enable a strategic use of the information that makes it a business asset to the organization. Developing and executing an effective Information Technology strategy that enables business strategy is critical for creating business value and gaining competitive advantage. This Course presents a framework and methodology for assessing, developing and implementing an effective IT strategy that is aligned with business needs. The course will be a combination of directed readings, lectures, case studies, one individual assignment and one group project.
Recent advances in information technology along with the phenomenal growth of the Internet have resulted in an explosion of data collected, stored, and disseminated by various organizations. Because of its massive size, it is difficult for analysts to sift through the data even though it may contain useful information. Data mining holds great promise to address this problem by providing efficient techniques to uncover useful information hidden in the large data repositories. Awareness of the importance of data mining for business is becoming widespread. The industry has created more and more job opportunities for people who have interdisciplinary data analytic skills. Indeed, this course intends to bridge the gap between data mining techniques and business applications. The students have the opportunity to learn both domain and technical knowledge to face the Big Data challenges in the industry.
The concentration and courses are offered by the Management Science & Information Systems Department
Sample Relevant Careers: Data Scientist, Demand Forecasting Analyst, Forecasting Planner, Demand Forecasting Manager, Demand Forecasting Analyst, Data Analyst, Business Analyst, Business Intelligence Specialist, Quantitative Researcher