Academic

Numerical analysis; random number generation; computer experiments and resampling techniques (bootstrap, cross validation); numerical optimization; matrix decompositions and linear algebra computations; algorithms (Markov chain Monte Carlo, expectation-maximization); algorithm design and efficiency; parallel and distributed computing. Course page ↗

Incomplete data; life tables; nonparametric methods; parametric methods; accelerated failure time models; proportional hazards models; partial likelihood; advanced topics. Course page ↗

High-performance computing in high-level data analysis languages; different computational approaches and paradigms for efficient analysis of big data; interfaces to compiled languages; R and Python programming languages; high-level parallel computing; MapReduce; parallel algorithms and reasoning. Course page ↗

Essentials of using relational databases and SQL. Processing data in blocks. Scraping web pages and using web services and APIs. Basics of text mining. Interactive data visualization with web technologies. Computational data workflow and best practices. Course page ↗

Optimization algorithms for solving problems in statistics, machine learning, and data analytics. Review of computational tools for implementing optimization algorithms (gradient descent, stochastic gradient descent, coordinate descent, Newton’s method). Course page ↗

Focus on linear and nonlinear statistical models. Emphasis on concepts, methods, and data analysis; formal mathematics kept to minimum. Topics include resampling methods, regularization techniques in regression and modern classification, cluster analysis and dimension reduction techniques. Course page ↗

Linear and nonlinear statistical models, emphasis on concepts, methods and data analysis using professional level software; formal mathematics kept to minimum. Topics include linear mixed models, repeated measures, generalized linear models, model selection, analysis of missing data, and multiple testing procedures. Course page ↗

Focus on linear statistical models. Emphasis on concepts, method and data analysis; formal mathematics kept to minimum. Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, factorial designs and analysis of covariance. Use of professional level software. Course page ↗

Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. Course page ↗

Fundamental concepts of probability theory, discrete and continuous random variables, standard distributions, moments and moment-generating functions, laws of large numbers and the central limit theorem. Course page ↗

This "course" was actually work internship units. That internship happened to be working for the Kinoped project when it was much younger, and resulted in me coming back to the project full-time once I finished my masters.

Simple random, stratified random, cluster, and systematic sampling plans; mean, proportion, total, ratio, and regression estimators for these plans; sample survey design, absolute and relative error, sample size selection, strata construction; sampling and nonsampling sources of error. Course page ↗

Essentials of using relational databases and SQL. Processing data in blocks. Scraping web pages and using web services and APIs. Basics of text mining. Interactive data visualization with web technologies. Computational data workflow and best practices. Course page ↗

Introduction to computing for data analysis and visualization, and simulation, using a high-level language (e.g., R). Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. Course page ↗

No work posted for this course

Varieties of categorical data, cross-classifications, contingency tables, tests for independence. Multidimensional tables and log-linear models, maximum likelihood estimation; tests of goodness-of-fit. Logit models, linear logistic models. Analysis of incomplete tables. Packaged computer programs, analysis of real data. Course page ↗

No work posted for this course

Time series relationships; univariate time series models: trend, seasonality, correlated errors; regression with correlated errors; autoregressive models; autoregressive moving average models; spectral analysis: cyclical behavior and periodicity, measures of periodicity, periodogram; linear filtering; prediction of time series; transfer function models. Course page ↗

Multivariate normal distribution; Mahalanobis distance; sampling distributions of the mean vector and covariance matrix; Hotellings T2; simultaneous inference; one-way MANOVA; discriminant analysis; principal components; canonical correlation; factor analysis. Intensive use of computer analyses and real data sets. Course page ↗

Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. Course page ↗

No work posted for this course

Fundamental concepts of probability theory, discrete and continuous random variables, standard distributions, moments and moment-generating functions, laws of large numbers and the central limit theorem. Course page ↗

No work posted for this course

Introduction to game theory. Explanation of the behavior of rational individuals with interacting and often conflicting interests. Non-cooperative and cooperative theory. Applications to economics, political science and other fields. Course page ↗

No work posted for this course

Simple linear regression, variable selection techniques, stepwise regression, analysis of covariance, influence measures, computing packages. Course page ↗

No work posted for this course

Rigorous treatment of mathematical concepts with emphasis on developing the ability to understand abstract mathematical ideas, to read and write mathematical concepts, and to prove theorems. Designed to serve as preparation for the more rigorous upper division courses. Course page ↗

No work posted for this course

Basics of experimental design. One-way and two-way fixed effects analysis of variance models. Randomized complete and incomplete block design. Multiple comparisons procedures. One-way random effects model. Course page ↗

No work posted for this course

Descriptive and normative analysis of individual decision making, with applications to personal, professional, financial, and public policy decisions. Emphasis on decision making under uncertainty and over time. Heuristics and biases in the psychology of decisions; overcoming decision traps. Course page ↗

No work posted for this course

Advanced expository writing in the contemporary American classroom. Strongly recommended for teaching credential candidates. Course page ↗

Written up on the blog: Undergrad Project: Reforming Education Reform →

No work posted for this course

Sign and Wilcoxon tests, Walsh averages. Two-sample procedures. Inferences concerning scale. Kruskal-Wallis test. Measures of association. Chi square and Kolmogorov-Smirnov tests. Course page ↗

No work posted for this course

Theory of income, employment and prices under static and dynamic conditions, and long term growth. Course page ↗

No work posted for this course

Design and analysis of data structures using Python; trees, heaps, searching, sorting, and graphs. Course page ↗

No work posted for this course. This course was basically just leetcode problems LOL.

Introduction to programming and problem solving in Python. Aimed primarily at non-major students. Course page ↗

No work posted for this course

Concepts of computing and applications using personal computers, spreadsheets, database management, word processing and communications. Course page ↗

No work posted for this course

Syntax and semantics of the symbolic language sentence logic. Symbols of sentence logic. Translation between sentence logic and English. Truth table interpretation of sentence logic. Proof techniques. Application of truth tables and proof techniques to arguments in English. Course page ↗

No work posted for this course