Bharatendra Rai

faculty

Bharatendra Rai, PhD

Professor / Chairperson

Decision & Information Sciences

Research site

Contact

508-910-6434

brai@umassd.edu

Charlton College of Business 326

Education

2004Wayne State UniversityPhD in Industrial Engineering
1993Indian Statistical Institute, Calcutta IndiaMTech in Quality, Reliability & OR
1991Meerut UniversityMSc in Statistics

Teaching

  • Business Organization
  • Process Management
  • Business Statistics
  • Quantitative Business Analysis
  • Operations Management

Teaching

Programs

Teaching

Courses

A technology-based, cross-discipline course for first-year students, the first business core course. It introduces first-year business majors to the world of business and enriches their first year experience. It provides students with an overview of business, its environment and its subsystems (e.g. operations, marketing, accounting, finance and information systems); and enhances their computer and team-working skills. Through informational and advising experiences students make decisions in areas such as the selection of courses, a major, a career and the utilization of on-campus student resources.

A technology-based, cross-discipline course for first-year students, the first business core course. It introduces first-year business majors to the world of business and enriches their first year experience. It provides students with an overview of business, its environment and its subsystems (e.g. operations, marketing, accounting, finance and information systems); and enhances their computer and team-working skills. Through informational and advising experiences students make decisions in areas such as the selection of courses, a major, a career and the utilization of on-campus student resources.

Examines both descriptive and inferential statistics as applied to business. Topics include graphical and tabular methods of data presentation, probability theory and distributions, hypothesis testing, analysis of variance, regression and forecasting. Emphasis is placed on concepts, applications, and the proper use of statistics to collect, analyze, and interpret data. Throughout this course students will use computer software to perform statistical analyses. Students will learn how to make decisions using facts and the techniques of data analysis. Students will also use the internet to supplement classroom learning.

Teaching

Online and Continuing Education Courses

Data analytics to describe, predict, advise decision-making, & improve business performance. The student will learn how to analyze business problems using a quantitative decision-making approach. This course focuses on methods, descriptive/predictive models for decision-making, & possible actions that would profit from analysis & results examined in a business context. This course is required of all undergraduate business majors.
Register for this course.

Data analytics to describe, predict, advise decision-making, & improve business performance. The student will learn how to analyze business problems using a quantitative decision-making approach. This course focuses on methods, descriptive/predictive models for decision-making, & possible actions that would profit from analysis & results examined in a business context. This course is required of all undergraduate business majors.
Register for this course.

A comprehensive overview of cybersecurity issues and current best practices in several applicative domains. The course discusses emerging cybersecurity threats and available countermeasures with respect to the most recent information technologies, including access control, cryptography, and protections of wired & wireless networks & data systems. The course presents current trends & open problems in cybersecurity.
Register for this course.

Manufacturing and service applications of selected analytical decision-making tools and techniques. The course illustrates, by example, how manufacturing and service operations can apply quantitative tools to decisions involving queuing, staffing, scheduling, product mix planning, and inventory control.
Register for this course.

Introduction to business analytics and data mining. Topics covered include data mining, exploratory data analysis, methods for classification and prediction, affinity analysis, multiple regression, logistic regression, discriminant analysis, and clustering. Applications of business analytics and data mining methodologies to a wide variety of real world business data are included.
Register for this course.

Data analytics to describe, predict, advise decision-making, & improve business performance. The student will learn how to analyze business problems using a quantitative decision-making approach. This course focuses on methods, descriptive/predictive models for decision-making, & possible actions that would profit from analysis & results examined in a business context. This course is required of all undergraduate business majors.
Register for this course.

Introduction to business analytics and data mining. Topics covered include data mining, exploratory data analysis, methods for classification and prediction, affinity analysis, multiple regression, logistic regression, discriminant analysis, and clustering. Applications of business analytics and data mining methodologies to a wide variety of real world business data are included.
Register for this course.

Research

Research interests

  • Business analytics & data mining
  • Big data research
  • Reliability prediction
  • Six-sigma
  • Quality & reliability engineering

Select publications

  • Xiaoling, Lu.; Rai, B.; Yan, Z.; Li, Y. (2018).
    Cluster-based Smartphone Predictive Analytics for Application Usage and Next Location Prediction
    International Journal of Business Intelligence Research , 9(2), 64-80.
  • Rai, Bharatendra; Nepal, Bimal; Gunasekaran, Angappa; Li, Julia (2013).
    Optimization of process audit plan for minimizing vehicle launch risk using MILP
    International Journal of Procurement Management, 6, 379-393.
  • Gunasekaran, Angappa; Rai, Bharatendra; Griffin, Michael (2011).
    Competitiveness of Small and Medium size Enterprises: An Empirical Research
    International Journal of Production Research, 19, 5489-5509.

Additional links