Courses

Students are required to take a minimum of seven quantitative courses as a part of the major in Quantitative Sciences, all of which are offered through the Institute for Quantitative Theory and Methods. Each student will complete the four core QSS courses described below. After the four core courses are completed, the student will then complete three QTM 385 special topics courses or upper-level electives to fulfill the quantitative side of the QSS major. Additional elective courses may need to be taken to fulfill 50 credit hours for the QSS degree. QSS or substantive field courses can constitute additional electives.

Course Atlas


Calculus I

CALC I, MATH 111, MATH 115, MATH 119, or equivalent test/transfer credit is required as a part of the QSS major coursework. Students need to complete this requirement before enrolling in QTM 120.

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; the concepts and procedures used in the conduct of empirical research; and the use of computers for analysis of quantitative social science data. 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 high-quality original research of your own. Beyond simply learning how to be a more critical participant in the academic research community, you will also be better-prepared for career opportunities using statistical tools and the products thereof.

The first part of the course addresses critical issues in research design applicable to a variety of disciplines in the sciences including sample selection, concept definition and measurement, and types of data collection. The second half of the course shifts its attention to data manipulation and the implementation of statistical techniques in R and Mathematica. Students will learn the basic programming languages and techniques necessary for more advanced coursework in the program. 

Prerequisites: None

GER: None

Credits: 4

Offering schedule: every fall

NOTE: This course is not QTM 100, nor is it a sequence course related to QTM 100. QTM 100 is a basic statistics course unrelated to the QSS major.

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: every fall

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: every 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 210

GER: MQR

Credits: 4

Offering schedule: every fall

Electives

QTM Electives 

Prerequisites: All four core courses (QTM 110, QTM 120, QTM 210, QTM 220), unless otherwise denoted

Refer to Course Atlas for current upper-level elective listings. QSS major electives include 300- and 400-level courses (excluding QTM 496 and QTM 497)

Equivalent Elective Courses 

These courses are not formally cross-listed as "QTM" courses, but they count as QSS major electives. 

ENVS 250: Fundamentals of Cartography and GIS

Prerequisites: None

GER: None

Credits: 4

Offering schedule: Fall 2015

This course provides an introduction to the study and design of maps and the use of geographic information systems (GIS) as a problem-solving 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.

PHYS/BIO 212: Computational Modeling

Prerequisites: PHYS 151*

GER: SNT

Credits: 4

Offering schedule: Spring 2016

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.

  • 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).

SOC 489 / LING 485: Big/Small Data & Visualization

Prerequisites: None 

GER: None

Credits: 3

Offering schedule: Spring 2016

  • Why a student might want to take this class

The course deals with the new tools of data analysis and visualization developed to deal with big data. It is the huge amounts of (mostly textual) web-based data that offer both humanities and social sciences new avenues of research in the form of digital humanities. What used to be the esoteric domain of computer scientists in data mining, artificial intelligence, natural language processing, is rapidly becoming accessible to the average web user. That is the goal of the course: pull together a wide range of tools, from natural language processing (NLP) to data visualization of networks and geographic maps, with no prerequisite of specialized knowledge, beyond basic computer literary. We will focus on freeware software, from Stanford CoreNLP, topic modeling in Mallet, KWIC (Key Words in Context), Word2Vec (vectors representations of words, shown to capture many linguistic regularities of a corpus), Gephi, Cytoscape, Palladio, Google Earth, QGIS, CartoDB, TimeMapper, Google Fusion Tables. We will also use word cloud software to represent a “bag of words”: Tagcrowd, Tagul, Tagxedo, Wordclouds, Wordle, Voyant.