Kam Hamidieh

Kam Hamidieh
  • Lecturer in Statistics

Contact Information

  • office Address:

    447 Jon M. Huntsman Hall
    3730 Walnut Street
    Philadelphia, PA 19104

Teaching

Current Courses

  • STAT422 - Predictive Analytics

    This course follows from the introductory regression classes, STAT 102, STAT 112, and STAT 431 for undergraduates and STAT 613 for MBAs. It extends the ideas from regression modeling, focusing on the core business task of predictive analytics as applied to realistic business related data sets. In particular it introduces automated model selection tools, such as stepwise regression and various current model selection criteria such as AIC and BIC. It delves into classification methodologies such as logistic regression. It also introduces classification and regression trees (CART) and the popular predictive methodology known as the random forest. By the end of the course the student will be familiar with and have applied all these tools and will be ready to use them in a work setting. The methodologies can all be implemented in either the JMP or R software packages.

    STAT422401 ( Syllabus )

    STAT422402 ( Syllabus )

    STAT422403 ( Syllabus )

    STAT422404 ( Syllabus )

  • STAT722 - Predictive Analytics

    This course follows from the introductory regression classes, STAT 102, STAT 112, and STAT 431 for undergraduates and STAT 613 for MBAs. It extends the ideas from regression modeling, focusing on the core business task of predictive analytics as applied to realistic business related data sets. In particular it introduces automated model selection tools, such as stepwise regression and various current model selection criteria such as AIC and BIC. It delves into classification methodologies such as logistic regression. It also introduces classification and regression trees (CART) and the popular predictive methodology known as the random forest. By the end of the course the student will be familiar with and have applied all these tools and will be ready to use them in a work setting. The methodologies can all be implemented in either the JMP or R software packages.

    STAT722401 ( Syllabus )

    STAT722402 ( Syllabus )

    STAT722403 ( Syllabus )

    STAT722404 ( Syllabus )

Past Courses

  • STAT101 - INTRO BUSINESS STAT

    Data summaries and descriptive statistics; introduction to a statistical computer package; Probability: distributions, expectation, variance, covariance, portfolios, central limit theorem; statistical inference of univariate data; Statistical inference for bivariate data: inference for intrinsically linear simple regression models. This course will have a business focus, but is not inappropriate for students in the college.

  • STAT102 - INTRO BUSINESS STAT

    Continuation of STAT 101. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications.

  • STAT422 - PREDICTIVE ANALYTICS

    This course follows from the introductory regression classes, STAT 102, STAT 112, and STAT 431 for undergraduates and STAT 613 for MBAs. It extends the ideas from regression modeling, focusing on the core business task of predictive analytics as applied to realistic business related data sets. In particular it introduces automated model selection tools, such as stepwise regression and various current model selection criteria such as AIC and BIC. It delves into classification methodologies such as logistic regression. It also introduces classification and regression trees (CART) and the popular predictive methodology known as the random forest. By the end of the course the student will be familiar with and have applied all these tools and will be ready to use them in a work setting. The methodologies can all be implemented in either the JMP or R software packages.

  • STAT430 - PROBABILITY

    Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.

  • STAT722 - PREDICTIVE ANALYTICS

    This course follows from the introductory regression classes, STAT 102, STAT 112, and STAT 431 for undergraduates and STAT 613 for MBAs. It extends the ideas from regression modeling, focusing on the core business task of predictive analytics as applied to realistic business related data sets. In particular it introduces automated model selection tools, such as stepwise regression and various current model selection criteria such as AIC and BIC. It delves into classification methodologies such as logistic regression. It also introduces classification and regression trees (CART) and the popular predictive methodology known as the random forest. By the end of the course the student will be familiar with and have applied all these tools and will be ready to use them in a work setting. The methodologies can all be implemented in either the JMP or R software packages.

Knowledge@Wharton

U.S.-China Trade Deal: Looking Beyond the Truce

The U.S.- China trade agreement signed on January 15 is a truce but it’s a step in the right direction, says Wharton’s Mauro Guillen.

Knowledge @ Wharton - 2020/01/20
Can Universal Basic Income Work?

Are guaranteed payments to every citizen a solution to the widening wealth gap or a detriment to the long-term health of the economy? A pilot program in Stockton, California, is aiming to find some answers.

Knowledge @ Wharton - 2020/01/17
Equal Work, Unequal Growth? Promotions and Recognition for Women in IT

What affects the career prospects of women who choose to work in information technology? Nishtha Langer offers her insights in this opinion piece.

Knowledge @ Wharton - 2020/01/17