Mohua Podder, PhD and MSTAT (Statistics), MA (Economics)

Associate Executive Professor, Alberta School of Business - Department of Accounting and Business Analytics

Pronouns: She/Her

Contact

Associate Executive Professor, Alberta School of Business - Department of Accounting and Business Analytics
Email
mohua1@ualberta.ca
Phone
(780) 492-0393
Address
4-21H Business Building
11203 Saskatchewan Drive NW
Edmonton AB
T6G 2R6

Email
mohua1@ualberta.ca
Phone
(780) 492-0393

Overview

Area of Study / Keywords

Econometric Models Robust Classification Dynamic variable selection


About

Education/Certification

  • MA in Economics, University of Alberta, Edmonton, AB (2017)
  • PSAT accreditation from Statistical Society of Canada (2014)
  • Ph.D. in Statistics, University of British Columbia, Vancouver, BC (2008)
  • Masters of Statistics (MSTAT), Indian Statistical Institute, Kolkata, India (2003)

Research

My research focuses on dynamic variable selection in econometric models, including hierarchical and non-nested frameworks, as well as the application of robust methodologies in classification models. I aim to develop statistical methods that improve model accuracy and stability in economic and business settings.


Teaching

I started teaching in 2015, and since then, I have taught courses in business analytics, decision sciences, and quantitative methods, helping students connect theory to real-world applications. I aim to simplify complex concepts and engage students while encouraging critical thinking and problem-solving. Beyond the classroom, I support student success by organizing help sessions, managing teaching teams, and mentoring students. My goal is to ensure learning is practical, accessible, and impactful.

Courses

MGTSC 212 - Probability and Statistics for Business

This course deals with model building, multiple regression analysis, and related methods useful in a business environment. Microcomputer software will be utilized throughout the course, with necessary computing skills being taught as the course proceeds. However, students are expected to already possess some basic familiarity with microcomputer applications. Prerequisite: STAT 161 or equivalent. Credit will be granted for only one of MGTSC 212 (formerly MGTSC 312) and STAT 252. Students may not receive credit for both MGTSC 212 and MGTSC 312.


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