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Through the online MS in Business Analytics, you’ll gain a direct understanding of how to effectively enhance business processes with data-driven, informed insights. The business analytics courses that you’ll explore in the program have been composed by faculty with extensive experience in improving organizational, managerial, and industrial business procedures. Designed for real-world, practical applications, the degree will prepare you to advance your career as you employ descriptive, prescriptive, and predictive analytical tools to solve business problems.
Scranton’s MS Business Analytics program is part of the Kania School of Management, which is among an elite 10 percent of U.S. business schools to have earned accreditation from the Association to Advance Collegiate Schools of Business (AACSB) International. Additionally, the program carries a STEM designation, which is especially important for international students looking for opportunities to continue training in the United States for up to three years after graduation.
Business analytics is widely recognized as a strategic weapon in today’s competitive business environment as being merely a supporting tool. As the gateway to the MBA specialization in Business Analytics, the goal of this introductory course is to provide an overview and exposure to the areas of descriptive, predictive, and prescriptive analytics. It will combine the study of key analytics concepts with hands-on exercises in data visualization and mining, statistical and predictive modeling, optimization and simulation.
Data mining refers to an analytic process designed to explore “big data” in search of consistent patterns and/or systematic relationships between variables, and to validate the findings by applying the detected patterns involved in a variety of phases that will involve data preparation, modeling, evaluation, and application. The instructor will provide hands-on demonstrations using a variety of data mining techniques (e.g. classification, association analysis, clustering, text mining, anomaly detection, feature selections) using widely adopted data mining software tools.
This course focuses on the overall structure of database management applications with emphasis on the relational approach. Topics covered include: database design, data dictionaries, query system, methods of storage and access, data definition and manipulation, data security and integrity, recovery and concurrence, distributed database management. Students will learn to design and implement database applications using micro and/or mainframe computers.
Business Intelligence (BI) systems are sophisticated analytical tools that attempt to present complex organizational and competitive information in a manner that allows decision makers to make effective decisions in a timely manner. This course will explore the capabilities and benefits of intelligence systems, data warehousing, and data mining techniques. This course will investigate business intelligence gathering techniques as well as providing hands-on experience. This course is not open to those students who have received credit for MIS 548.
The course focuses on the Big Data Eco-System, including data science, Internet of Things (IOT) and Artificial Intelligence (AI) to reveal patterns in data to get ever-closer to one’s customer, increasing revenue and/or reducing the cost per transaction with a data-driven business model. Students will learn not only the principles of this eco-system but also the business applications of Big Data, IOT, and AI, and their ethical use in business through real-world use cases in areas of manufacture, service, pharma, energy, education, healthcare, and the natural world. (Prerequisite BUAN 571 and BUAN 572)
Elective Courses (15 credits required)
This course focuses on using the programming language R in the field of business analytics. Students will be exposed to the wealth of information in R and its packages as it relates to data visualization, regression models, regression trees, text mining, clustering, and optimization.
This course deals with the study of quantitative forecasting techniques which include exponential smoothing, classical decomposition, regression analysis and Box-Jenkins (ARIMA) methodology, as well as qualitative (judgmental) methods.
This course focuses on the use of simulation modeling as a tool to analyze various business applications in the face of risk/uncertainty. Students will gain hands-on experience in using an appropriate software to build simulation models to tackle applications in project management, inventory stocking policies, financial planning, and revenue management.
This course focuses on the use of data visualization within data analysis. Students will learn what data visualization is, storytelling within data visualization and best practices using data visualization. Students will gain hands on experience using Tableau software which is a top visualization tool used in the business world today as well as a top software skill that employers in numerous fields seek. (Prerequisites MBA 501C)
Course description coming soon
The course focuses on Big Data and the ethics of data gathering, analysis, and leveraging to build new products and services for consumers. Used inappropriately, Big Data can usher in an age of unethical practice destroying trust in business. Students will be provided with an overview of the principles of this eco-system and the core tenants of responsible and ethical usage of Big Data through real world examples in various business sectors. The course focuses on Big Data and the ethics of data gathering, analysis, and leveraging to build new products and services for consumers. Used inappropriately, Big Data can usher in an age of unethical practice destroying trust in business. Students will be provided with an overview of the principles of this eco-system and the core tenants of responsible and ethical usage of Big Data through real world examples in various business sectors. (Prerequisite MBA 501C)
The course focus is on the use of multivariate statistics. Following review of basic statistical inference concepts, students will gain hands-on experience in applying methods in the context of addressing business problems from different functional areas (e.g., marketing, finance, operations). Methods used in both supervised (i.e., predictive modeling) and unsupervised (i.e., pattern recognition) scenarios are covered, such as multiple regression, logistic regression, conjoint analysis, factor analysis, and cluster analysis. Students will use appropriate software (e.g., Minitab, SPSS) for data analysis. (Prerequisite MBA 501A)
The course focuses on a enterprise wide strategy to create a successful customer relationship management program and maintain it on an ongoing basis, by utilizing systems, tools, and techniques that develop a shared view of the customer throughout the enterprise, and using best practice offerings tailored to the appropriate customer relationship life cycle stage. SAP-CRM software platform is utilized for the course. This course is not open to those students who have received credit for ERP 512.
This course provides an introduction to programming with Python. Students will learn how to solve business problems related to data processing and analysis using Python including how to use the proper techniques to uncover business insights. The course also provides an overview of Python programming language and the Pandas package for data analysis. (Prerequisite BUAN 571)
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