Program Overview

Applied Statistics (APSTA-GE)

The Applied Statistics program offers a range of courses and specializations in statistical analysis, data science, and research methodology. The program is designed to provide students with a strong foundation in statistical theory and practical skills in data analysis, interpretation, and communication.

Course Offerings

• APSTA-GE 2001: Statistics for the Social and Behavioral Sciences I: Introduction to basic tools of applied statistics, using statistical software for hands-on experience with real data.
• APSTA-GE 2002: Statistics for Behav and Social Sciences II: Introduction to inferential techniques, including t-tests, one and two-way ANOVA, simple and multiple regression, and nonparametric methods.
• APSTA-GE 2003: Interim Quantitative Methods: General Linear Model: Introduction to regression techniques from a simulation-based perspective, with emphasis on applications rather than mathematical theory.
• APSTA-GE 2004: Introductory Statistical Inference in R: Covers regression techniques from a simulation-based perspective, with an emphasis on applications rather than mathematical theory.
• APSTA-GE 2006: Math, Statistics and R Programming Bootcamp: Covers material required as prerequisite knowledge for many advanced courses, including advanced algebra, pre-calculus, basic matrix algebra, and basics in calculus.
• APSTA-GE 2011: Supervised and Unsupervised Machine Learning: Introduction to classification and clustering techniques, including many variations, with an emphasis on applications.
• APSTA-GE 2012: Causal Inference: Introduction to advanced statistical methods for answering causal questions using observational data.
• APSTA-GE 2013: Missing Data: Introduction to missing data analysis, including types of missing data mechanisms, problems with ignoring missing data, and conventional fixes.
• APSTA-GE 2014: Stats Analysis of Networks: Introduction to the analysis and modeling of network data, with a focus on applications in the social sciences.
• APSTA-GE 2015: Applied Spatial Statistics: Introduction to methods for analyzing spatial data, including exploratory statistics, correlations, and regression.
• APSTA-GE 2017: Databases and Data Science Practicum: Hands-on introduction to extracting, transforming, and visualizing data using real-world datasets.
• APSTA-GE 2018: Advanced Causal Inference Designs and Applications: Builds on statistical and analytic skills in advanced causal inference techniques, focusing on advanced topics and applications.
• APSTA-GE 2040: Multi-Level Modeling Growth Curve: Introduction to models for multi-level growth curve data, including traditional methods and sophisticated techniques.
• APSTA-GE 2042: Multi-Level Modeling: Nested Data/Longitudinal Data: Introduction to models for multilevel nested data, including block designs, factorial, Latin square, and repeated measures.
• APSTA-GE 2044: Generalized Linear Models and Extensions: Introduction to generalized linear models, including probability distributions, assumptions of dependence and independence, and related approaches to statistical analysis.
• APSTA-GE 2047: Messy Data and Machine Learning: Introduction to complex real-world datasets, including survey research, data collection, and cleaning.
• APSTA-GE 2048: Generative AI for the Social Sciences: Introduction to using generative AI in social science research, including text classification, parsing unstructured documents, and analyzing images.
• APSTA-GE 2062: Ethics of Data Science: Introduction to ethical issues surrounding data, including research and applied ethics, concepts of privacy and publicity, and lifecycle of data.
• APSTA-GE 2085: Basic Statistics: Introduction to statistics, including methods for displaying and describing data, statistical inference, and probability distributions.
• APSTA-GE 2086: Basic Statistics II: Introduction to statistical inference, including hypothesis tests, analysis of variance, correlation, and simple regression.
• APSTA-GE 2094: Modern Approaches in Measurement: Introduction to latent construct measurement techniques, including continuous and categorical latent variables, dimensionality reduction, clustering, and finite mixture and diagnostic classification models.
• APSTA-GE 2110: Large Databases in Applied Research: Introduction to working with large-scale databases, including data preparation, workflow, and modeling using the Stata statistical software package.
• APSTA-GE 2122: Frequentist Inference: Introduction to foundations of statistical inference, including principles of estimation and hypothesis testing, and application to the general linear model.
• APSTA-GE 2123: Bayesian Inference: Introduction to Bayesian workflow, conjugate models, MCMC, prediction and model evaluation, Bayesian estimation of GLMs, and introduction to hierarchical/multilevel models.
• APSTA-GE 2134: Experimental & Quasi Experimental Design: Introduction to experimental designs, including block designs, factorial, Latin square, and repeated measures, as well as single case designs.
• APSTA-GE 2135: Data-Driven Methods for Policy Evaluation: Introduction to data-centric technologies for evaluating policies, including computational and statistical methods, and ethical questions surrounding data use.
• APSTA-GE 2139: Survey Research Methods: Introduction to survey research, including survey design, coverage, sampling, non-response, modes of data collection, questionnaire construction, and evaluation.
• APSTA-GE 2310: Internship: Practical training in working with real-world data, including data collection, analysis, and interpretation.
• APSTA-GE 2331: Data Science for Social Impact: Introduction to competencies required for data science, including data analysis, visualization, and communication, with a focus on social impact.
• APSTA-GE 2351: Practicum in Applied Statistics: Applied Probability: Introduction to probability, including Kolgomorov's axioms, set theory, discrete combinatorial probability, Bayes' theorem, and probability distributions.
• APSTA-GE 2352: Practicum in Applied Statistics: Statistical Computing: Introduction to statistical programming and simulation using R, including coding skills, principles of user-centered design, and development of interactive tools.
• APSTA-GE 2353: Practicum in Applied Statistics: SocSci Research Methodology: Introduction to active research in the social sciences, emphasizing connections between substantive research and statistical methods.
• APSTA-GE 2354: Applied Data Analytics for Public Policy: Introduction to data analytics, including hands-on training in working with real microdata, and application to social problems.
• APSTA-GE 2355: Data Science Translation: Writing and Visualization: Introduction to effective communication of empirical research, including writing, visualization, and oral presentation of technical material.
• APSTA-GE 2357: Practicum in Applied Statistics: Social Science Research Methods: Introduction to active research in the social sciences, emphasizing connections between substantive research and statistical methods.
• APSTA-GE 2358: Practicum in Applied Statistics: Interactive Data Sci using R Shiny: Introduction to interactive tool development for data analysis, including coding skills, principles of user-centered design, and development of interactive applications.
• APSTA-GE 2401: Statistical Consulting Research Seminar: Synthesis of benefits and drawbacks of various statistical choices and concepts, and application to real-world problems and case studies.
• APSTA-GE 3208: Management and Ethics of Data: Introduction to critical and ethical issues surrounding data, including research and applied ethics, concepts of privacy and publicity, and lifecycle of data.