Susanna Lange

Susanna Lange
  • Lecturer in Statistics and Data Science

Contact Information

Teaching

Current Courses

  • STAT1010 - Introductory Business Statistics

    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.

    STAT1010001 ( Syllabus )

    STAT1010002 ( Syllabus )

    STAT1010003 ( Syllabus )

Past Courses

  • OIDD7770 - Intro To Python Data Sci

    The goal of this course is to introduce the Python programming language within the context of the closely related areas of statistics and data science. Students will develop a solid grasp of Python programming basics, as they are exposed to the entire data science workflow, starting from interacting with SQL databases to query and retrieve data, through data wrangling, reshaping, summarizing, analyzing and ultimately reporting their results. Competency in Python is a critical skill for students interested in data science. Prerequisites: No prior programming experience is expected, but statistics, through the level of multiple regression is required. This requirement may be fulfilled with MBA courses such as STAT 6130/6210; or by waiving MBA statistics.

  • 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.

  • STAT7770 - Intro To Python Data Sci

    The goal of this course is to introduce the Python programming language within the context of the closely related areas of statistics and data science. Students will develop a solid grasp of Python programming basics, as they are exposed to the entire data science workflow, starting from interacting with SQL databases to query and retrieve data, through data wrangling, reshaping, summarizing, analyzing and ultimately reporting their results. Competency in Python is a critical skill for students interested in data science. Prerequisites: No prior programming experience is expected, but statistics, through the level of multiple regression is required. This requirement may be fulfilled with MBA courses such as STAT 6130/6210; or by waiving MBA statistics.

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The Business of Signing a Superstar Athlete

Kenneth Shropshire, professor emeritus of legal studies and business ethics at the Wharton School, explores the expectations, business opportunities, and community impact that come with signing a superstar athlete like LeBron James.…Read More

Knowledge @ Wharton - 2026/10/16
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