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 (Spring 2024)

  • 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.

    STAT4700401 ( Syllabus )

    STAT4700402 ( Syllabus )

  • 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.

    STAT5030401 ( Syllabus )

    STAT5030402 ( Syllabus )

All 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.

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