Courses for All QTM Majors
No matter what QTM major program you pursue [QSS, AMS, PPA, or BBA + QSS], the QTM coursework is identical.*
You are required to take Calc I [or have AP/transfer credit] plus a minimum of 9 quantitative courses offered through the Institute for Quantitative Theory and Methods. Each major differentiates itself by its accompanying coursework, which you may find on each of our major pages.
Please note, students must meet the minimum GPA requirement of 2.0 to graduate with any major or minor from the department.
All classes counting toward the degree must be taken for a letter grade.
*with one exception. AMS majors are required to take MATH 211, 221, 361, and 362 instead of QTM 120 and QTM 210.
Calculus I
QTM 110: Introduction to Scientific Methods
This course is designed to introduce students to the style of analytic thinking required for research in the sciences and the concepts and procedures used in the conduct of empirical research. In short, this course teaches a set of skills that are essential for both understanding the research you will encounter in substantive classes, and being able to produce highquality original research of your own. Beyond simply learning how to be a more critical participant in the academic research community, you will also be betterprepared for career opportunities using statistical tools and the products thereof.
Students will be introduced to the basic toolkit of researchers which includes sampling, hypothesis testing, Bayesian inference, regression, experiments, instrumental variables, differences in differences, and regression discontinuity. More importantly, students will learn the principles of critical thinking essential for careful and credible research.
Prerequisites: None
GER: None
Credits: 3
Offering schedule: Fall, Spring
QTM 120: Math for Quantitative Sciences
This course is a mandatory course for all Quantitative Sciences majors. It is also a prerequisite for the more advanced course offerings in the major including Regression Analysis, Maximum Likelihood Estimation, Longitudinal Data Analysis as well as the game theory sequence. The goal of the course is to provide the necessary mathematical background for students to properly derive and implement common statistical modeling techniques employed in the sciences.
In the first half of the course we will cover core concepts in linear algebra. The second half of the course focuses on multivariable calculus. This course focuses on the computation skills necessary for quantitative research.
Prerequisites: Calculus I or equivalent
GER: MQR
Credits: 4
Offering schedule: Fall, Spring
QTM 150: Intro to Statistical Computing I
This course provides an introduction to statistical computational tools for analyzing data. The material is selected to enable you to become proficient enough to actively implement the methods and tools in your scientific research. This will require you to practice the material outside of class.
By the end of the course, students should be able to 1) deal with complex and messy real data, 2) use graphics to explore and understand data, 3) gain familiarity with basic data collections, storage, and manipulation, and 4) fluently reshape data into the most convenient form for analysis or reporting.
Prerequisites: None
GER: None
Credits: 1
Offering schedule: Fall, Spring
QTM 151: Intro to Statistical Computing II
This course provides a practicum of skills for data science and an introduction to how to do data science with R. The material is selected to enable you to get data into the most useful structure, transform it, visualize it, and model it. This will require you to practice the material outside of class.
By the end of the course, students should be able to (1) deal with complex and messy real data (2) use graphics to explore and understand data (3) gain familiarity with basic data manipulation, (4) fluently reshape data into the most convenient form for analysis, and (5) automate cleaning and analysis.
Prerequisites: QTM 150
GER: None
Credits: 1
Offering schedule: Fall, Spring
QTM 210: Probability & Statistics
This course covers the structure of probability theory, which is the foundation of statistics, and provides many examples of the use of probabilistic reasoning. It discusses the most commonly encountered probability distributions, both discrete and continuous. The course considers random sampling from a population, and the distributions of some sample statistics. It deals with the problem of estimation: the process of using data to learn about the value of unknown parameters of a model. Finally, it discusses hypothesis testing: the use of data to confirm or reject hypotheses formed about the relationship among variables.
Prerequisites: QTM 120
GER: MQR
Credits: 4
Offering schedule: Fall, Spring
QTM 220: Regression Analysis
This course covers basic techniques in quantitative research. It introduces students to widely used procedures for regression analysis for descriptive and causal inference, and provides intuitive, applied, and formal foundations for regression and more advanced methods treated later in the major course sequence. The first half of the course addresses the foundations of statistical hypothesis testing via linear regression models. This module of the course will provide the formal derivation of the ordinary least squares regression model as well as an overview of its practical implementation and the underlying modeling assumptions. The second module shifts focus to the implications of violating the assumptions of the OLS model including issues of omitted variable bias, multicollinearity, and heteroskedasticity. While the course will emphasize the mathematical foundations of these concepts, each topic will also cover the implementation of the relevant methods in the statistical computing program R.
Prerequisites: QTM 120, QTM 150, QTM 210
GER: MQR
Credits: 4
Offering schedule: Fall, Spring
QTM UpperLevel Electives
Refer to Course Atlas for this semester's upperlevel elective offerings. QTM electives include 300 and 400level lecture and seminar style courses (excluding QTM 398R, QTM 496, QTM 497, and QTM 499).
Elective 
Description 
Prerequisite 
QTM 302W: Technical Writing 
This course introduces the methods of rhetorical analysis and user experience design as means of developing complex information for a variety of audiences, ranging from professional peers to the general public. Communication via prose, speech, visuals, and gestures springs from work in a variety of genres, which may include short research reports, informative and persuasive infographics, technical instructions, translations, and studentgenerated data sets. We will attend carefully to document design and explore especially the possibilities for developing narratives using quantitative data. You will develop your work with an eye toward publishing it in an electronic portfolio (ePortfolio) using readily available, easytouse tools as part of the Domain of One's Own initiative. No prior technical knowledge or mediamaking experience is required. 
QTM 110 or QTM 100 
QTM 315: Game Theory I 
From the study of legislatures and courts to the design of electoral systems and regulation, game theory applications are farranging and have become one of the predominant means of modeling and evaluating social interaction in fields as diverse as economics, law, and biology. Game theory insights have also profoundly changed and expanded the use of auctions across industries; auctions and related mechanisms are used to sell online advertising, to allocate resources in the “sharing economy,” to assign contractors to suppliers, and to apportion spectrum to telecommunication companies. Game theorists working on market design have transformed the medical resident matching market, improved school choice procedures, and started novel markets for kidney exchange. Increasingly, firms are adopting game theory tools to improve not only their competitive position via their external relations with customers, competitors, and suppliers, but also their internal markets for resources and talent. This course provides a technical introduction to the noncooperative theory of games and its various tools that analyze strategic interactions. Selected topics include normal and dynamic games, games of incomplete information and repeated games. Applications are drawn from a wide range of disciplines including business, economics, political science, biology, and sociology. 
None 
QTM 345: Advanced Statistics 
This course introduces the workhorse of data analysis: multiple regression. Violations of conditions for valid inference of multiple regressions are demonstrated in the observational data and typical treatments are introduced accordingly. Specifically, the course devotes to endogeneity and heterogeneity problems in social science. We deal with endogeneity problem with instrumental variable methods and simultaneous system. For heterogeneity, we bring in the additional time dimension for panel data models. For all methods, finite sample and large sample properties are studied. 
QTM 220 
QTM 355: Introduction to Time Series Analysis 
This course provides students with a theoretical and practical basis for analyzing complex data sets that depend on time. Concepts introduced include detrending, autoregressive models, time series forecasting, hetereoscedastic models, multivariate time series analysis, causality, spectral analysis, and dimensionality reduction. Interspersed with explorations of the mathematical underpinnings of these concepts are data analysis practicums using R, providing context to the theoretical ideas that are introduced, as well as a final project that allows students to study a particular problem in depth. Students emerge from this class knowing how to approach noisy realworld data of this type, possessing a broadlygeneralizable toolkit that spans disciplines from economics to astronomy to geology to medicine to ecology. 
QTM 220 
QTM 385: Data Science Computing 
This course exposed students to popular languages for data analysis, important programming and computing concepts useful when working with computers, and emphasizes good workflows for reproducible research using modern tools. It teaches basic Python concepts and syntax. Students primarily write code in Jupyter/IPython notebooks, start with essential computer literacy (e.g. shell commands), and finish with topics such as Numpy for scientific computing, pandas for data science, and cloud computing using notebooks on AWS and google cloud. Emphasis is placed on good work for reproducible research (e.g. data cleaning using scripts versus hacking away at an excel file and leaving no explanation of how you got from original data to clean data). Overall, the emphasis is on learning programming for the sake of using state of the art data science tools, as opposed to programming for the sake of programming. 
QTM 151, or a background in CS 
QTM 385: Generalized Linear Model 
• Why a student might want to take the elective In many of the real data problems, the dependent variable may be count or categorical responses, such as the number of traffic accidents per day in a city, the number of trades in a time interval for a certain stock, whether a respondent has a depression symptom (“yes/ no”), how severe is their symptom (“none/ low/ moderate/ high”). For such data, the linear regression model is not appropriate. The generalized linear models are natural extensions of the linear regression model, designed to analyze count or categorical dependent variables. • What academic fields use the methods / skills in the elective Generalized linear models are widely used in both academic field, such as biology, psychology, political science, finance, marketing, and many others. • Concrete examples of how to apply learned skills outside the classroom These models are also standard quantitative tools in the industry, such as insurance, banking, marketing, information technology, and many others. • Anything else that might attract students to their classes In addition, the students will be trained to implement the models in a popular statistical software R, solve real data problems, and write a statistical report. 
QTM 220 
QTM 385: Intro to Applied Multivariate Statistics 
• Why a student might want to take the elective This course provides you with a practical introduction to statistical methods for analyzing multivariate data that involve multiple response variables. The techniques covered have been routinely applied to the investigation of problems in the physical, social, and medical sciences. Specifically, you will learn the basics of and how to apply: a) Data visualization techniques, b) Principal component analysis, c) Multidimensional scaling, d) Exploratory factor analysis, e) Confirmatory factor analysis, f) Cluster analysis, g) Confirmatory factor analysis, h) Structural equation modeling, i) Linear mixedeffects models • What academic fields use the methods / skills in the elective Many academic fields use the methods and skills studied in the course. Some examples are applied statistics, medicine and health, business and economics, psychology, biology, environmental studies, meteorology, sociology, education, geology, and sports. • Concrete examples of how to apply learned skills outside the classroom Students will be able to interpret 2D and 3D graphics and to prepare and use them in data mining and analysis. Students will be able to read and understand research literature and reports that employ the statistical techniques described above. Students will be able to formulate research questions and perform a data analysis to address them using the techniques described above. • Anything else that might attract students to their classes Students have the option to choose research articles and a data set of interests to them for reviews and data analysis. Students will develop practical data analysis and report writing skills. Students will explore and learn more about their research interests as well as those of their peers. 
QTM 220 
QTM 385: Intro to Statistical & Machine Learning 
• Why a student might want to take the elective Statistical learning refers to a set of tools for modeling and understanding complex datasets and making predictions. This subject is within an interdisciplinary field at the intersection of statistics and computer science, which develops statistical models and interweaves them with computational algorithms. The course will provide a first introduction to statistical learning and its core models and algorithms that are becoming increasingly popular in academic fields and industry. • What academic fields use the methods / skills in the elective In academic fields, such as genetics, neuroscience, political science, etc., statistical learning methods help to answer fundamental questions that previously would have been impossible, by extracting meaningful information from big data. • Concrete examples of how to apply learned skills outside the classroom In industry, they are the engines of new technologies, such as Internet search, speech recognition, computer vision, artificial intelligence, and advanced risk management methods in finance. • Anything else that might attract students to their classes In addition, students will be trained to implement the statistical learning methods using a popular statistical software R and gain experience on real data analysis. 

QTM 385: Mathematics of Voting 
This class is about the mathematics of Social Choice Theory, which is the theory of group decisionmaking. We will study voting methods mainly in the context of political elections and look at properties of various methods. It also covers the problem of apportionment (dividing up resources fairly) in the context of apportionment of seats in the U.S. House of Representatives; and possibly other topics such as yesno voting systems. The mathematics in this course is mainly logical deduction and general quantitative reasoning. We may use some probability, but note that there will be no use of statistics in this class. Using social choice theory as a framework, students learn approaches to modeling voting behavior based on winner selection and fair division.

QTM 120 
QTM 385: Practical Approaches to Data Science w/ Text 
This course teaches theories and techniques commonly used in practice of data science. The primary focus is on text analysis covering text parsing, language models, sequence estimation, vector space models and distributional semantics, as well as statistical approaches including cluster analysis and supervised learning. Modern topics such as cloud computing, big data analysis, and data visualization are also discussed. Introductory courses on computer programming and probabilities & statistics are recommended as prerequisites for this course. All exercises assume Python programming. Students are expected to present their work on the final project in groups towards the end of the term. 
QTM 220 
QTM 385: Social Choice and Electoral Systems 
This course provides students with a rigorous introduction to the field of social choice theory. Social choice concerns the study of preference aggregation: most commonly, taking a collection of individuals with heterogeneous preferences as an input and then examining potential ways in which a collective, or social, preference is constructed. Our goal is to mathematically formalize and axiomatize the properties of different aggregation rules and to characterize rules that yield desirable outcomes. Students master various proof techniques as they work through the major results in this field (Arrow’s theorem, the GibbardSatterthwaite theorem, and the revelation principle, among many others), and learn how these results can be applied to a variety of fields in the social sciences. The course concludes with an introduction to mechanism design, which lies at the intersection of social choice and game theory. 
None 
QTM 385: (Adv) Social Network Analysis 
Interest in social network analysis has exploded in the past few years, partly due to the latest advancements in statistical modeling and the rapid availability of network data and partly due to the recognition that many analytical problems can be recast as a network problem. Aiming to examine social connections and interactions from structural perspectives, network analysis has become an essential method and tool for studying a variety of issues in social and natural sciences, such as friendship formation, peer influence, social inequality, career mobility, social marketing, organizational competition, economic development, political alliance, diffusion of innovations, contagion of health outcomes, and even protein interactions, to name only a few. This course covers the major methods to collect, represent, and analyze network data. Selected topics include centrality analysis, positional analysis, clustering analysis, the exponential random graph model for modeling network formations, the stochastic actororiented model for dynamic network analysis, meta network analysis, weighted network analysis, text network analysis, causal analysis of network effects, and social networkbased predictions and interventions. Examples are drawn from a wide range of disciplines including business, economics, education, political science, public health, and sociology. Students learn handson skills to conduct their own research by using popular network packages in R such as “statnet” and “RSiena”. 
QTM 220 
QTM 446W: Big/Small Data and Visualization 
The course deals with new tools of data analysis and visualization, especially for text data (Natural Language Processing, NLP). The course relies on the Stanford parser CoreNLP as the main NLP engine, but a number of other NLP tools will also be used (topic modeling with Mallet and Stanford Topic Modeling Toolbox, Word2Vec, vectors representations of words, shown to capture many linguistic regularities of a corpus, Ngram and word cooccurrences). Through these tools, the course will show how to analyze large corpora of text. All NLP tools can be downloaded from the PCACE website (Program for ComputerAssisted Coding of Events, www.pcace.com). The course will also show how to use different tools of data visualization, especially network graphs dealing with relationships between objects (social actors, concepts, or just words), both static and dynamic (changing with time), and spatial maps dealing with objects in space (and time, dynamic maps) through Geographic Information System (GIS) tools. We will focus on freeware software, from Gephi to Cytoscape, Palladio, Google Earth Pro, QGIS, Carto, TimeMapper, Google Fusion Tables. 
None 
QTM 490: Social Choice Theory (Senior Seminar) 
This course provides students with a rigorous introduction to the field of social choice theory. Social choice concerns the study of preference aggregation: most commonly, taking a collection of individuals with heterogeneous preferences as an input and then examining potential ways in which a collective, or social, preference is constructed. Our goal is to mathematically formalize and axiomatize the properties of different aggregation rules and to characterize rules that yield desirable outcomes. Students master various proof techniques as they work through the major results in this field (Arrow’s theorem, the GibbardSatterthwaite theorem, and the revelation principle, among many others), and learn how these results can be applied to a variety of fields in the social sciences. The course concludes with an introduction to mechanism design, which lies at the intersection of social choice and game theory. 
QTM 220 
QTM 491: Design and Analysis of Experiments 
Experiments are a prominent instrument of inquiry in the natural and the social sciences. The first part of the course introduces the logic of experimentation and discusses various methodological issues in the design and analysis of experiments. Topics include randomization inference, blocking, noncompliance, attrition, interference, and heterogeneous treatment effects. The second part of the course builds on this foundation to discuss some practical issues and ethical considerations in designing and implementing experiments.

QTM 220 
Equivalent Elective Courses
These courses are not formally crosslisted as "QTM" courses, but they count as QTM electives.
Equivalent Electives 
Description 
Prerequisites 
ENVS 250: Fundamentals of Cartography and GIS 
This course provides an introduction to the study and design of maps and the use of geographic information systems (GIS) as a problemsolving tool for geographic and spatial analysis. Course lectures will focus on fundamental concepts and the applications of GIS, data collection and processing, cartographic design techniques, and trends in geospatial technologies. The Bureau of Labor Statistics has reported that GIS and geospatial skills are in high demand among employers. These skills are particularly important for students interested in pursuing careers in academia, in the government, or in the private sector. Many local and federal governmental agencies, such as the Department of Defense, hire employees with GIS skills. Some large consulting firms also require employees to have mapping and GIS skills. Universities tend to be one of the larger employers of those with GIS skills, as a variety of research projects require employees who can use the program. In addition, many private firms also look to hire people with these skills, including real estate, surveying, oil, and electric companies. Exposure to GIS and cartography programs can be helpful career training for interested students. 
None 
PHYS / BIOL 212: Computational Modeling 
Computation is one of the pillars of modern science, in addition to experiment and theory. In this course, various computational modeling methods will be introduced to study specific examples derived from physical, biological, chemical, and social systems. We study how one makes a model, implements it in computer code, and learns from it. We focus on modeling deterministic dynamics, dynamics with randomness, on comparison of mathematical models to data, and, at the end, on high performance computing. · Why a student might want to take the class As was said by the great Leonardo, "He who loves practice without theory is like the sailor who boards ship without a rudder and compass and never knows where he may cast.” Theory nowadays is often equivalent to mathematical models, which one solves using computers. This course will teach you how to formulate mathematical models and how to solve them using computers. Along the way you will learn Python — a flexible computer language that you will be able to use in other applications as well. · What academic fields use the methods / skills taught in this class Essentially any academic field that does mathematical modeling uses methods and skills developed by this elective course. · Examples of how to apply learned skills outside the classroom We will study examples in epidemiological modeling (relevant to a lot of CDC work), in chemical physics (will be of relevance to those developing new materials in chemistry and physics), or physiology (of relevance to those of you who will become doctors or biomedical researchers). You cannot be an effective researcher nowadays without knowing how to solve problems on a computer — and this class will give you these skills. *PHYS 151 requirement may be waived if student has taken QTM 120, 210, 220, and one class that exposes him/her to how mathematics is used to represent natural phenomena (like Game Theory).

Physi 151* 
BIOL 463: Population Biology and Evolution of Disease 
Application of basic principles of population genetics and population biology to the study of infectious diseases, aging, and cancer. Infectious diseases as well as aging and cancer will be treated as population dynamic and evolutionary phenomena. Primary consideration will be given to the following topics: (1) the withinhost population and evolutionary dynamics of microparasite (viruses, bacteria,and protozoa) infections, the immune defenses, and the treatment of these infections with antibiotics and other chemotherapeutic agents; (2) the epidemiology of infectious diseases and their control by vaccination, prophylaxis, and chemotherapy; (3) the evolution of parasites and their virulence (why do parasites harm their hosts?); (4) the population biology and evolution of cancers and the evolution of senescence. The course will include lectures, discussion, and group project work which will involve handson computer simulations and modeling. 
BIOL 141/142L; Math 112 or 116 (or equivalent) 