Professor B. Rai

Bharatendra Rai

Professor / Chairperson

Decision & Information Sciences

Research site

508-910-6434

508-999-8646

brai@umassd.edu

Charlton College of Business 326

Education

1991Meerut University, IndiaBA, Statistics
1993Indian Statistical Institute, Calcutta IndiaMA, Quality, Reliability, & OR
2004Wayne State UniversityPhD, Industrial Engineering

Teaching

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

Teaching

Programs

Teaching

Courses

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.

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.

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.

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.
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.

External links

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