Teaching

Courses at UNL

Course	Title	Description
Stat 251	Statistical Computing I - Data Wrangling	Techniques for processing, cleaning, and visualizing messy data. Topics include data reduction strategies, data transformations, combining multiple data sources, and special types of data (text, spatial, dates and times, hierarchical).
Stat 451	Development of Statistical Software	Advanced statistical software development. Packaging code into functions, intelligent software design, compiled languages to speed up code, development and release cycles.
Stat 471	Analysis of Messy Data	Check data quality, impute missing values, wrangle data formats, and join data sources; write efficient and reproducible code so others are able to replicate the analysis; pull data together to solve a contemporary problem.
Stat 810	Alpha Seminar	Program requirements, resources available, tips for academic success, professional statistical organizations, career paths, history of statistics, ethics, statistical conferences, statistical blogs and online forums, frequentist and Bayesian paradigms, current research in department.
Stat 850	Computing Tools for Statisticians	Introductions to statistical computing packages and document preparation software. Topics include: graphical techniques, data management, Monte Carlo simulation, dynamic document preparation, presentation software

Courses at Iowa State

Course	Title	Description
Stat 579	Introduction to Statistical Computing	Basic statistical computing tools to deal with data. Learn how to deal with complex, messy, real data. Use graphics to explore and understand data. Gain familiarity with basic data collection, storage and manipulation. Fluently reshape data into the most convenient form for analysis or reporting. Automate cleaning and analysis in R.
Stat 585	Data Technologies in Statistics	Discussion of aspects of statistical computing as they are relevant for data analysis. Read and work with data in different formats: flat files, databases, web technologies. Elements of literate programming help us with making our workflow transparent and analyses reproducible. We will discuss communication of results in form of R packages and interactive web applications.
Stat 590	Visual Studies in the Literature	Taught in Spring 2023
DS 202	Data Acquisition and Exploratory Data Analysis	Data acquisition: file structures, web-scraping, database access; ethical aspects of data acquisition; types of data displays; numerical and visual summaries of data; pipelines for data analysis: filtering, transformation, aggregation, visualization and (simple) modeling; good practices of displaying data; data exploration cycle; graphics as tools of data exploration; strategies and techniques for data visualizations; basics of reproducibility and repeatability; web-based interactive applets for visual presentation of data and results.
Stat 528	Visual Business Analytics	Types of data displays; numerical and visual summaries of data; data structures for data displays; data vs info graphics; good practices of displaying data; human perception and cognition in data displays; graphics as tools of data exploration; graphical diagnostics of statistical models and machine learning procedures; strategies and techniques for data visualizations; basics of reproducibility and repeatability; web-based interactive applets for visual presentation of data and results; programming in R.
Stat 480	Applied Statistical Computing	Data management; spread sheets, verifying data accuracy, transferring data between software packages. Data and graphical analysis with statistical software packages. Algorithmic programming concepts and applications. Simulation. Software reliability.
Stat 332	Visual Communication of Quantitative Information	Communicating quantitative information using visual displays; visualizing data; interactive and dynamic data displays; evaluating current examples in the media; color, perception, and representation in graphs; interpreting data displays.
Stat 490	Independent Studies	(Spring 2014) Visual Communication of Statistical Results (Spring 2012) Visualizations for the Soccer World Cup (Spring 2012) Strategies for Good Graphics (Fall 2011) Data Visualization Competition for the DOT
Stat 415	Non-parametric tests	Taught in Fall 2013
Stat 557	Categorical Data Analysis	Statistical methods for analyzing simple random samples when outcomes are counts or proportions; measures of association and relative risk, chi-squared tests, loglinear models, logistic regression and other generalized linear models, tree-based methods. Extensions to longitudinal studies and complex designs, models with fixed and random effects.
Stat 430	Empirical Methods for Computer Science	Statistical methods for research involving computers; exploratory data analysis; selected topics from analysis of designed experiments - analysis of variance, hypothesis testing, interaction among variables; linear regression, logistic regression, Poisson regression; parameter estimation, prediction, confidence regions, dimension reduction techniques, model diagnostics and sensitivity analysis; Markov chains and processes; simulation techniques and bootstrap methods; applications to computer science, bioinformatics, computer engineering - programs, models and systems as objects of empirical study; communicating results of empirical studies.
Stat 330	Probability and Statistics for Computer Science	Topics from probability and statistics applicable to computer science. Basic probability; Random variables and their distributions; Elementary probabilistic simulation; Queuing models; Basic statistical inference; Introduction to regression.
Stat 511	Statistical Methods	Introduction to the general theory of linear models, least squares and maximum likelihood estimation, hypothesis testing, interval estimation and prediction, analysis of unbalanced designs. Models with both fixed and random factors. Introduction to non-linear and generalized linear models, bootstrap estimation, smoothing methods.
Stat 690	Special Topics in Statistical Graphics (section F)	Taught in Fall 2007
Stat 322	Probabilistic Methods for Electrical Engineers	Introduction to probability with applications to electrical engineering. Sets and events, probability space, conditional probability, total probability and Bayes’ rule. Discrete and continuous random variables, cumulative distribution function, probability mass and density functions, expectation, moments, moment generating functions, multiple random variables, functions of random variables. Elements of statistics, hypothesis testing, confidence intervals, least squares. Introduction to random processes.