Shuva Gupta

Shuva Gupta
  • Senior Lecturer in Statistics and Data Science

Contact Information

  • office Address:

    331 Academic Research Building
    265 South 37th Street
    Philadelphia, PA 19104

Research Interests: Theory and Methods: Penalized methods in high dimensional data analysis, variable selection, nonparametric statistics.
Application: Statistical analysis of genomic and toxicology data.

Teaching

All Courses

  • COGS3998 - Senior Thesis

    This course is a directed study intended for cognitive science majors who have been admitted to the cognitive science honors program. Upon admission into the program, students may register for this course under the direction of their thesis supervisor.

  • STAT0001 - Intro To Stat & Data Sci

    In this course, we will learn introductory statistics using R with a focus on the application of statistical thinking to business problems. We will learn basic statistical concepts such as mean, variance, quantiles and hypothesis testing, and basic R programming for data management and analysis. We will work with traditional R's data, frame structure as well as the modern tibbles structure. Prerequisite: Percentages, average, powers, exponential, linear equation of a line, polynomials.

  • STAT1010 - 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.

  • STAT1020 - Intro Business Stat

    Continuation of STAT 1010 or STAT 1018. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications.

  • STAT4300 - Probability

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

Awards and Honors

  • Wharton Undergraduate Teaching Excellence Award, 2021

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