Please note: The course descriptions displayed here are current as of Tuesday, September 22, 2020, but the official Course Catalog should be used for all official planning.

STAT 107: Principles of Statistics

For students in all disciplines. Provides the background needed to evaluate statistical arguments found in newspapers, magazines, reports, and journals and the logic and techniques necessary to perform responsible elementary statistical analysis. Topics include basic data analysis, one-variable regression, experimental and sampling design, random variables, sampling distributions, and inference (confidence intervals and significance testing). This course may not be taken on a Satisfactory/Unsatisfactory basis. Students with credit for MATH 140 or equivalent, or BIOL 170, or AP Statistics should not take this course and should instead consider STAT 255.
Units: 6.

BIOL 170: Integrative Biology: Experimental Design and Statistics

An introduction to experimental and sampling design in the fields of biology and biochemistry, as well as methods of data analysis and interpretation. The connection between statistical analysis and experimental design will be emphasized. Topics include descriptive, exploratory, and confirmatory statistical analyses. Lecture and computer laboratory.
Units: 6.
Prerequisite: BIOL 150 or consent of instructor

CMSC 205: Data-Scientific Programming

An introduction to programming with emphasis on learning from data in order to gain useful insights. Topics focus on elementary programming concepts in the R language and the necessary tools to handle, analyze and interpret data. This course will be taught in a workshop format, and students will complete regular assignments and a final project that provide hands-on programming/analysis experiences.
Units: 6.
Prerequisite: One prior course MATH, STAT, or CMSC course, or BIOL 170, or consent of instructor

STAT 205: Data-Scientific Programming

An introduction to programming with emphasis on learning from data in order to gain useful insights. Topics focus on elementary programming concepts in the R language and the necessary tools to handle, analyze and interpret data. This course will be taught in a workshop format, and students will complete regular assignments and a final project that provide hands-on programming/analysis experiences.
Units: 6.
Prerequisite: One prior course MATH, STAT, or CMSC course, or BIOL 170, or consent of instructor

ANTH 207: Quantitative Analysis in Anthropology

An introduction to the collection and manipulation of quantitative data in anthropological research. Topics include sampling, measurement, and basic nominal and ordinal statistics.
Units: 6.
Prerequisite: ANTH 110, 120, or 140, preferably all three. Recommended for anthropology majors in the sophomore year; must be completed by the end of the junior year.

CMSC 208: Machine Learning

An overview of techniques used to discover structural patterns and make predictions using complex datasets that are prevalent in today's world. The central machine learning tasks of classification, clustering, and regression will be explored, along with methods for training models and evaluating predictions. This course will be taught in a workshop format. Assignments will involve the use of statistical software.
Units: 6.
Also listed as Statistics 208, Linguistics 208
Prerequisite: CMSC 150, or CMSC 205, or CMSC 210, or consent of instructor

STAT 208: Machine Learning

An overview of techniques used to discover structural patterns and make predictions using complex datasets that are prevalent in today's world. The central machine learning tasks of classification, clustering, and regression will be explored, along with methods for training models and evaluating predictions. This course will be taught in a workshop format. Assignments will involve the use of statistical software.
Units: 6.
Also listed as Linguistics 208, Computer Science 208
Prerequisite: CMSC 150, or CMSC 205, or CMSC 210, or consent of instructor

CHEM 210: Analytical Chemistry

A course in the quantitative description of chemical equilibria in solution (acid-base, complexation, redox, solubility) using classical, separation, electrochemical, and spectrochemical methods of analysis. This course covers methods of quantification, statistics, and data analysis as applied to modern chemistry. Students will have the opportunity to individually design projects. Three lectures and two laboratory periods per week.
Units: 6.
Also listed as Environmental Studies 250
Prerequisite: CHEM 116, placement exam, or consent of instructor; concurrent enrollment in CHEM 211 required

CMSC 210: Introduction to Scientific Programming

An introduction to computer programming with an emphasis on numerical applications in mathematics and the sciences. Topics include elementary programming concepts in the Python language, design and implementation of numerical algorithms, and an introduction to symbolic computation.
Units: 6.
Prerequisite: One term of calculus (either MATH 140 or MATH 120), or consent of instructor

CHEM 211: Statistical Methods in Analytical Chemistry

This course covers methods of statistics and data analysis as applied to modern chemistry. Students in this course will develop a working knowledge of the basic and advanced capabilities of the spreadsheet program Microsoft Excel. Topics explored include descriptive statistics, hypothesis testing, correlation, regression, and tests of significance. This course is taught in a exercise-oriented approach where we use real data collected during CHEM 210.
Units: 3.
Prerequisite: CHEM 116, placement exam, or consent of instructor; concurrent enrollment in CHEM 210 is required

GEOS 214: Climate and Climate Change

In this class we will cover the fundamental scientific knowledge about climate, and the long-term patterns and variation in climates over Earth’s history. Students will be able to evaluate and explain major climate drivers in the past, and how past and future human activities are altering climates at both local and global scales.
Units: 6.
Also listed as Environmental Studies 235
Prerequisite: GEOL 110 or GEOS 110 or GEOL 150 or GEOS 150

ECON 223: Quantitative Decision-Making

The students will learn how to develop formal, quantitative approaches to structuring difficult problems, particularly those problems involving probabilistic factors. We will develop and practice the steps of defining a problem, gathering data, formulating a model, performing numerical calculations, evaluating numerical information, refining the model, analyzing the model's alternatives, and communicating the results.
Units: 6.
Also listed as Mathematics 223
Prerequisite: Sophomore standing

ENST 235: Climate and Climate Change

In this class we will cover the fundamental scientific knowledge about climate, and the long-term patterns and variation in climates over Earth’s history. Students will be able to evaluate and explain major climate drivers in the past, and how past and future human activities are altering climates at both local and global scales.
Units: 6.
Also listed as Geosciences 214
Prerequisite: GEOL 110 or GEOS 110 or GEOL 150 or GEOS 150

STAT 255: Statistics for Data Science

This course introduces modern statistical techniques in the context of predictive inference and modeling. Topics will include data analysis techniques such as linear and logistic regression, ANOVA, nonparametric methods, and computational approaches such as cross-validation and bootstrapping. Statistical software will be used frequently. This class will involve regular in-class and out-of-class assignments as well as exams and quizzes.
Units: 6.
Prerequisite: STAT 107, BIOL 170, or MATH 140, or instructor permission

GOVT 271: Research Methods in Political Science

Considers research approaches and methods political scientists use to create knowledge. The course will explore quantitative and qualitative techniques with theory and hands-on applications. The goal of the course is to improve students’ ability to read research critically and to make and test their own arguments in political science. Students should take this course in their sophomore or junior year.
Units: 6.
Prerequisite: GOVT 110 or consent of instructor; seniors must obtain consent of instructor

PSYC 280: Research Methods I

The first course in a two-term sequence designed to introduce psychology majors to the principles of research design, data collection, data analysis, and research report writing. This term focuses on philosophy of science, the role of theory in research, and research design. Students design an empirical project to be executed during Research Methods II. Sequence should be taken in the sophomore year and in consecutive terms.
Units: 6.
Prerequisite: Sophomore standing and previous or concurrent enrollment in one of MATH 107, ANTH 207, PSYC 170, or BIOL 170

PSYC 281: Research Methods II

The second course in a two-term sequence for psychology majors (see Psychology 280). This term focuses on the execution of empirical research projects, analysis of data, inferential and advanced correlational statistics, and interpretation of results. Students complete an empirical project. Sequence should be taken in the sophomore year and in consecutive terms.
Units: 6.
Prerequisite: PSYC 280

ETST 302: Research Methods in Ethnic Studies

An introduction to a variety of methodological ways of investigating our social world. We focus on applied (or public) ways of conducting research that explicitly inform social policy, programs, and practice on issues related to race and ethnicity. We work in collaborative research environments, understanding how research can be conducted both for the sake of research and to improve the lives of people.
Units: 6.
Prerequisite: Sophomore standing and a course in ethnic studies; ETST 110 recommended

MATH 340: Probability

An introduction to probability and its applications. Topics will include combinatorial and axiomatic probability, conditional probability and Bayes' Theorem, random variables, expectation and variance, discrete and continuous probability distributions, joint and conditional distributions, and limit laws.
Units: 6.
Also listed as Statistics 340
Prerequisite: MATH 200, and either MATH 220 or MATH 230

STAT 340: Probability

An introduction to probability and its applications. Topics will include combinatorial and axiomatic probability, conditional probability and Bayes' Theorem, random variables, expectation and variance, discrete and continuous probability distributions, joint and conditional distributions, and limit laws.
Units: 6.
Also listed as Mathematics 340
Prerequisite: MATH 200, and either MATH 220 or MATH 230

BIOL 360: Introduction to Bioinformatics

An introduction to the methods and software used to analyze biological data. Through lecture and guided tutorials, students will learn the structure and organization of biological databases, explore methods for examining genomic and proteomic data sets, and examine specific case studies relating to evolution, drug discovery and human variation.
Units: 6.
Prerequisite: BIOL 260

BIOL 375: Biostatistics

This analytical and writing course trains students on the use of advanced analytical methods common in biological data analysis. Students complete analyses in the R programming software and work on individual datasets. Extensive writing about analytical methods, appropriate applications of analyses, and interpretations of these analyses from a large portion of this course. Lecture only with a term-long project required.
Units: 6.
Prerequisite: BIOL 170 or consent of instructor.

BIOL 380: Ecological Modeling

An integrated lecture and computer laboratory introduction to the process of developing mathematical descriptions of the interactions between components of a population, community, or ecosystem, and the use of computer simulation as a tool for understanding ecology and natural resource management. Topics include population growth, predator-prey and competitor interactions, biogeochemical cycling, and mass balance in ecosystems.
Units: 6.
Also listed as Environmental Studies 380
Prerequisite: At least one of the following: BIOL 229, BIOL 230, BIOL 245, BIOL 330, BIOL 335 or BIOL 345

ECON 380: Econometrics

Statistical techniques and statistical problems applicable to economics, focusing on ordinary least-squares regression, classical inference, and detections of and adjustments for violations of the Classical Assumptions.
Units: 6.
Prerequisite: Sophomore standing, MATH 107, MATH 130 or MATH 140, and previous course in economics

STAT 390: Tutorial Studies in Statistics

Advanced work in statistics on topics not covered in regular offerings.
Units: 1 TO 98.
Prerequisite: Counter Registration Required.

STAT 391: Directed Study in Statistics

Directed study follows a syllabus set primarily by the instructor to meet the needs or interests of an individual student or small group of students. The main goal of directed study is knowledge or skill acquisition, not research or creative work.
Units: 1 TO 98.
Prerequisite: Counter Registration Required.

CMSC 405: Advanced Data Computing

This course builds on CMSC/STAT 205, providing a deeper exploration of statistical computing in R. Topics might include efficient programming techniques, parallelization, statistical algorithms, advanced data visualization, and creation of R packages. Statistical software will be used extensively in this course. The class will be taught in an interactive lab-based format. There will be regular assignments and a project.
Units: 6.
Also listed as Statistics 405
Prerequisite: CMSC 205

STAT 405: Advanced Data Computing

This course builds on CMSC/STAT 205, providing a deeper exploration of statistical computing in R. Topics might include efficient programming techniques, parallelization, statistical algorithms, advanced data visualization, and creation of R packages. Statistical software will be used extensively in this course. The class will be taught in an interactive lab-based format. There will be regular assignments and a project.
Units: 6.
Also listed as Computer Science 405
Prerequisite: CMSC 205

STAT 445: Mathematical Statistics

Development of the mathematical theory of statistics and its application to the real world. The course will focus on the principles of estimation and testing from both the frequentist and Bayesian perspectives. Resampling methods (permutation tests and bootstrap intervals) will also be explored.
Units: 6.
Prerequisite: MATH/STAT 340

STAT 450: Bayesian Statistics

A study of the Bayesian statistical philosophy, contrasting it with the traditional frequentist approach taught in other statistics courses. Topics include Bayes' Theorem, prior and posterior probability distributions, hierarchical models, and Markov Chain Monte Carlo methods. The course will involve a mixture of lecture, discussion, and use of statistical software. Requirements include exams, a project, and assignments involving the use of statistical software.
Units: 6.
Prerequisite: MATH/STAT 340

STAT 455: Advanced Statistical Modeling

This course expands on STAT 255, and introduces more sophisticated models, meant to capture complicated correlation structure in data. Topics might include generalized linear models, mixed-effects models, hierarchical models, spatial models, and time series. The course will involve the use of statistical software. There will be regular assignments, exams, and possibly projects.
Units: 6.
Prerequisite: STAT 255 or instructor permission

CMSC 470: Artificial Intelligence

A detailed investigation into foundational concepts of artificial intelligence: search, knowledge representation, and automated planning. Specific topics include uninformed and heuristic search techniques, logic-based knowledge representations, automated theorem-proving, logic programming (Prolog), action representations, means-ends analysis, regression and partial-order planning, and reachability analysis using graphs.
Units: 6.
Prerequisite: CMSC 250 and CMSC 270

ECON 481: Advanced Econometrics & Modeling

The course explores advanced econometric topics in model specification, estimation, and prediction (e.g., two-stage least squares, limited dependent variables and logistic regression, nonparametric regressions, censored regressions, time-series analysis). Techniques are introduced through work related to the instructor’s areas of interest and expertise (e.g., labor, development, health, education).
Units: 6.
Prerequisite: ECON 380

GOVT 538: Outside the Margin of Error: Polling and Quantitative Prediction in Modern Politics

Politicians and prognosticators have increasingly turned to “Big Data”, futures markets, and poll aggregation to predict political outcomes. The course will explore the theory and accuracy of these quantitative predictions, discuss contemporary issues of data quality, and suggest whether quantitative analysis of politics is more than entertainment for political junkies.
Units: 6.
Prerequisite: Junior standing and completion of the quantitative general education requirement, or consent of instructor

STAT 590: Tutorial Studies in Statistics

Units: 1 TO 98.
Prerequisite: Counter Registration Required.

STAT 591: Directed Study in Statistics

Directed study follows a syllabus set primarily by the instructor to meet the needs or interests of an individual student or small group of students. The main goal of directed study is knowledge or skill acquisition, not research or creative work.
Units: 1 TO 98.
Prerequisite: Counter Registration Required.

STAT 699: Independent Study in Statistics

Guided independent study of an advanced topic in undergraduate statistics or supervised work on an undergraduate research project, generally culminating in a final presentation and/or paper.
Units: 1 TO 98.
Prerequisite: Counter Registration Required.