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Shawn Wiggins

Shawn Wiggins has been an instructor within the Mathematics Department at City College of San Francisco since 2013. In addition to teaching, he serves as the department’s Data Science Coordinator and actively contributes to the college's Open Educational Resources (OER) initiatives.

He is currently on sabbatical for the Fall 2024 - Spring 2025 academic year and will resume his regular duties in Fall 2025.

Office: Batmale Hall Room 764
Email: swiggins@ccsf.edu
LinkedIn: shawnwiggins

Fall 2025

MATH 108 Foundations of Data Science

Online Hybrid | CRN 70950 | SEC 963 | Mission Center | Room 321 | Thursdays 6:10 - 8:25 pm

Catalog Description

Foundations of Data Science combines an introductory look into the fundamental skills and concepts of computer programming and inferential statistics with hands-on experience in analyzing datasets by using common tools within the industry. Additionally, the course investigates ethical issues surrounding Data Science, such as data privacy.

Student Learning Outcomes

  1. Communicate the statistical analysis of real world data using multiple representations such as graphs, text, and tables, being mindful of concerns around data bias and privacy.
  2. Write computer scripts to organize, produce visualizations, and numerically summarize data.
  3. Infer population information using tools from inferential statistics based on random sample data generated using computational techniques such as iteration.
  4. Make predictions using machine learning techniques such as clustering and linear regression.

MATH 120 Linear Algebra

Online Hybrid | CRN 73838 | SEC 961 | Mission Center | Room 321 | Thursdays 4:40 - 5:55 pm

Catalog Description

Real vector spaces, subspaces, linear dependence and span, matrix algebra and determinants, basis and dimension, inner product spaces, linear transformations, eigenvalues and eigenvectors, proofs of basic results.

Student Learning Outcomes

  1. Define and apply real vector spaces and linear transformations.
  2. Solve problems involving systems of linear equations and matrices.
  3. Solve problems by working with the geometric structure of inner product spaces.
  4. Prove basic linear algebra theorems.

STAT C1000 Introduction to Statistics

Online Hybrid | CRN 73857 | SEC 961 | Ocean Campus | Batmale Hall | Room 713 | Wednesdays 9:40 - 11:55 am

Catalog Description

This course is an introduction to statistical thinking and processes, including methods and concepts for discovery and decision-making using data. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-squared, and t-tests; and application of technology for statistical analysis, including the interpretation of the relevance of the statistical findings. Students apply methods and processes to applications using data from a broad range of disciplines.

Student Learning Outcomes

  1. Assess how data were collected and recognize how data collection affects what conclusions can be drawn from the data.
  2. Identify appropriate graphs and summary statistics for variables and relationships between them and correctly interpret information from graphs and summary statistics.
  3. Describe and apply probability concepts and distributions.
  4. Demonstrate an understanding of, and ability to use, basic ideas of statistical processes, including hypothesis tests and confidence interval estimation.
  5. Identify appropriate statistical techniques and use technology-based statistical analysis to describe, interpret, and communicate results.
  6. Evaluate ethical issues in statistical practice.