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The online Graduate Certificate in Business Analytics requires the completion of four courses, two of which are electives and can be chosen based on your professional interests. Based on your prior academic preparation and work experience, some or all of The University of Scranton’s one-credit MBA modules may be waived. With these modules waived, the program can be completed in as little as eight months with full-time study.
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 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.
(Prerequisite MBA 501C) 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.
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)
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)
This course module is intended to develop the statistical concepts and techniques that are needed to make business decisions. Topics to be covered include detailed coverage of descriptive statistics, probability theory (including Bayes’ Theorem), and discrete and continuous probability distributions with an emphasis on business applications. A survey of modern statistical methods covering sampling distributions, interval estimation, hypothesis testing, and regression and correlation analysis will be discussed.
An introduction to the quantitative approaches used in today’s businesses to solve decision problems. Topics will include overviews of linear programming, spreadsheet modeling, project scheduling, waiting line systems, and simulation.
An introduction to computers and how they can be applied to the operations and management of business firms. Topics include data-processing concepts, overview of computer hardware and software, modern data and information processing systems, and applications of computers in business.
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