Giles Hooker

Giles Hooker
  • Professor of Statistics and Data Science

Contact Information

  • office Address:

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

Research Interests: Machine Learning, Functional Data Analysis, Differential Equations, Computational Statistics, Statistical Ecology

Links: Personal Website

Teaching

Current Courses

  • STAT4700 - Data Analytics And Statistical Computing

    This course will introduce a high-level programming language, called R, that is widely used for statistical data analysis. Using R, we will study and practice the following methodologies: data cleaning, feature extraction; web scrubbing, text analysis; data visualization; fitting statistical models; simulation of probability distributions and statistical models; statistical inference methods that use simulations (bootstrap, permutation tests). Prerequisite: Waiving the Statistics Core completely if prerequisites are not met. This course may be taken concurrently with the prerequisite with instructor permission.

    STAT4700401 ( Syllabus )

    STAT4700402 ( Syllabus )

  • STAT5030 - Data Analytics And Statistical Computing

    This course will introduce a high-level programming language, called R, that is widely used for statistical data analysis. Using R, we will study and practice the following methodologies: data cleaning, feature extraction; web scrubbing, text analysis; data visualization; fitting statistical models; simulation of probability distributions and statistical models; statistical inference methods that use simulations (bootstrap, permutation tests). Prerequisite: Two courses at the statistics 4000 or 5000 level.

    STAT5030401 ( Syllabus )

    STAT5030402 ( Syllabus )

  • STAT9916 - Sem In Adv Appl Of Stat: Statistical Consulting

    This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.

    STAT9916301 ( Syllabus )

Past Courses

  • STAT4700 - Data Analy & Stat Comp

    This course will introduce a high-level programming language, called R, that is widely used for statistical data analysis. Using R, we will study and practice the following methodologies: data cleaning, feature extraction; web scrubbing, text analysis; data visualization; fitting statistical models; simulation of probability distributions and statistical models; statistical inference methods that use simulations (bootstrap, permutation tests). Prerequisite: Waiving the Statistics Core completely if prerequisites are not met. This course may be taken concurrently with the prerequisite with instructor permission.

  • STAT5030 - Data Analy & Stat Comp

    This course will introduce a high-level programming language, called R, that is widely used for statistical data analysis. Using R, we will study and practice the following methodologies: data cleaning, feature extraction; web scrubbing, text analysis; data visualization; fitting statistical models; simulation of probability distributions and statistical models; statistical inference methods that use simulations (bootstrap, permutation tests). Prerequisite: Two courses at the statistics 4000 or 5000 level.

  • STAT9916 - Sem in Adv Appl of Stat

    This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.

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Jeremy Siegel: How Tariffs Could Impact the U.S. Economy

The economic fallout of import tariffs on Canada, Mexico, and China depends largely on how long they stay in effect, according to Wharton’s Jeremy Siegel.…Read More

Knowledge @ Wharton - 2025/02/10
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