Uday Jha
Assistant Teaching Professor
Decision & Information Sciences
Contact
508-999-8350
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Charlton College of Business 204
Education
2017 | Rochester Institute of Technology, Rochester, NY | MS Applied Statistics |
2012 | ICFAI University, Tripura, India | Master Aviation Management |
2009 | Madurai Kamaraj University, Madurai, Tamil Nadu, India | MS Physics |
2007 | ICFAI University, Tripura, India | MS Mathematics |
Teaching
- Applied Decision Techniques
- Supply Chain Management
- Introduction of Business Analytics
Teaching
Online and Continuing Education Courses
A case study approach involving the following statistical concepts: descriptive statistics, probability, sampling, probability distribution, statistical estimation, chi-square testing, analysis of variance and simple regression-correlation analysis.
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.
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.
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.
A case study approach involving the following statistical concepts: descriptive statistics, probability, sampling, probability distribution, statistical estimation, chi-square testing, analysis of variance and simple regression-correlation analysis.
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.
A case study approach involving the following statistical concepts: descriptive statistics, probability, sampling, probability distribution, statistical estimation, chi-square testing, analysis of variance and simple regression-correlation analysis.
Research
Research activities
- High Dimensional Multicollinear Datasets
Research
Research interests
- Big Data
- Business Analytics
Select publications
- Uday Kant Jha, Peter Bajorski, Ernest Fokoue, Justine Vanden Heuvel, Jan van Aardt, Grant Anderson, (2017).
Dimensionality Reduction of High-Dimensional Highly Correlated Multivariate Grapevine Dataset
Open Journal of Statis, 7, 702-717.
Rochester Institute of Technology ProQuest Dissertations Publishing - Uday Kant Jha, Peter Bajorski, (2017).
High-Dimensional Linear and Functional Analysis of Multivariate Grapevine Data
Latest from Uday
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