Throughout my career I’ve taught numerous courses in mathematics,
statistics, and data science in a variety of settings. Information on
the courses I’ve taught can be found below.
Please visit my homepage for links to course websites and
materials.
Grinnell College
STA-209 - Applied Statistics
- STA-209 is an introductory course in applied statistics covering
topics including: data collection, visualization, summary statistics,
basic statistical inference for one and two samples, linear regression
(simple and multiple), one- and two-way ANOVA, and categorical data
analysis. The class involves several comprehensive labs that guide
students through the analysis of real data using Minitab and StatKey
software.
Semesters and sections taught: Fall 2018 (2), Spring 2019 (1), Fall
2019 (2), Spring 2020 (1)
STA-230 - Introduction to Data Science
- STA-230 introduces core topics in data science using the R
programming language. The course covers data cleaning, merging,
exploration, visualization, modeling, and other topics using guided labs
and case studies in a workshop style classroom.
- Semesters and sections taught: Spring 2019 (2), Fall 2019 (1), Fall
2022 (2)
STA-395/MAT-395 - Applied Data Science
- STA-395/MAT-395 is a fully applied course where students work in
small teams on a semester long, data-intensive project. The overarching
goal of this course is to prepare students to successfully navigate an
applied data science project involving working on a team to answer
questions posed by an external client.
- Semesters and sections taught: Spring 2020 (1)
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Xavier University
MATH-146 - Introductory Biostatistics
- MATH-146 is first course in statistics, with an emphasis on
applications and methods of particular relevance to the biological
sciences. Topics include: random sampling procedures, experiments and
observational studies, exploratory data analysis, correlation,
bootstrapping and resampling methods, the normal distribution,
confidence intervals hypothesis tests for proportions and means,
chi-square tests, ANOVA. Problems and examples are drawn from fields
such as bioinformatics, genetics, ecology, epidemiology, and public
health.
- Semesters and sections taught: Fall 2021 (2), Spring 2022 (2)
MATH-156 - General Statistics
- MATH-156 is a course intended for non-math majors that provides an
introduction to the concepts and tools used for collecting, analyzing,
and making inferences from data. Topics include: graphical displays,
correlation, regression, design of experiments, probability, simulation,
random sampling, confidence intervals and hypothesis testing.
- Semesters and sections taught: Fall 2020 (1), Spring 2021 (2),
Spring 2022 (1)
MATH-256 - Intro to Probability and Statistics
- MATH-256 is a calculus-based introduction to probability and
descriptive and inferential statistics. Topics include numerical and
graphical summaries of data, conditional probability, Bernoulli trials,
normal distribution, the central limit theorem, estimation, t-tests,
chi-square tests, type I and II errors, regression and correlation.
- Semesters and sections taught: Fall 2020 (1), Fall 2021 (1)
MATH-257 - Data Modeling
- MATH-257 is a second course in statistics that focuses on
exploratory data analysis and visualization, logistic regression,
estimation and inference of multiple regression models, model selection,
analysis of variance, multiple comparisons, experimental design, and
non-parametric modeling approaches.
- Semesters and sections taught: Spring 2021 (1)
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University of Iowa
BIOS-4120 - Introduction to Biostatistics
- BIOS-4120 is an introductory statistics course that covers basic
statistical concepts and methods that are frequently used in medicine,
public health, and the biological sciences. Some topics covered include
study design, descriptive statistics, basic rules of probability,
measures of association, hypothesis testing for one and two sample
continuous and categorical data, confidence intervals, correlation, and
an introduction to regression.
- Semesters and sections taught: Summer 2014 (1)