Quantitative Theory and Methods

Intro to descriptive and inferential stats with emphasis on practice and implementation. Introduces basic statistical concepts and encourages critical thinking about data. A primary focus of the course is on implementation of appropriate statistical analysis and interpretation of results.

Credit Hours

4

GER

QR

Requisites

None

Cross-Listed

None

Introduces students to the style of analytic thinking required for research and concepts and procedures used in the conduct of empirical research: sampling, hypothesis testing, Bayesian inference, regression, experiments, instrumental variables, differences indifferences, regression discontinuity.

Credit Hours

3

GER

QR

Requisites

None

Cross-Listed

None

This course is an introduction to the R programming language. It will cover the programming basics of R: data types, controlling flow using loops/conditionals, and writing functions. In addition to these basics, this course will emphasize skills that are relevant for data analysis.

Credit Hours

2

GER

None

Requisites

None

Cross-Listed

None

The purpose of this course is to prepare students for upper-level, data analysis-related courses. This course emphasizes on skills that are relevant for data analysis which include 1) data manipulation such as merging, appending, and reshaping data, and 2) making plots for descriptive analysis.

Credit Hours

1 - 2

GER

None

Requisites

None

Cross-Listed

None

Topics course intended for early-career students. Topics allow students to explore the foundations, theory, and methods of data science, and examine the ways in which data driven solutions power industry, government, and the non-profit sector in an applied setting.

Credit Hours

1 - 4

GER

None

Requisites

None

Cross-Listed

None

Variable first-year seminar topics within QTM which may aim to provide an introduction to quantitative theory, practical applications of quantitative methods, introductory coding or statistics, or introduce other topics pertinent to quantitative fields.

Credit Hours

3

GER

FS

Requisites

None

Cross-Listed

None

Students will apply concepts and skills learned in QTM 100 to a broader field of statistical analysis: multivariable analysis and model building. Implementation of appropriate statistical methods, hands-on data analysis with statistical software, interpretation of analysis results.

Credit Hours

3

GER

QR

Requisites

None

Cross-Listed

None

Covers the structure of probability theory. Discusses the commonly encountered probability distributions, both discrete and continuous. Considers random sampling from the population, and the distribution of some sample statistics. Discusses the problem of estimation, and hypothesis testing.

Credit Hours

4

GER

QR

Requisites

MATH 210 or MATH_OX 210 or MATH 211 or MATH_OX 211 or equivalent transfer credit as prerequisite.

Cross-Listed

None

Introduces students to widely used procedures for regression analysis, and provides intuitive, applied, and formal foundations for regression and more advanced methods treated later in the major course sequence.

Credit Hours

4

GER

QR

Requisites

(QTM 110 or QTM_OX 110) & (QTM 150 or QTM_OX 150) & (QTM 120 or MATH 210 or MATH_OX 210 or MATH_211 or MATH_OX 211) & [QTM 210 or QTM_OX 210 or ECON 220 or ECON_OX 220 or (MATH 361 and MATH 362)] & (MATH 221 or MATH_OX 221) or eq. transfer cred.as prer.

Cross-Listed

None

This course teaches students how to think like data scientists. In combination with tools such as spreadsheets, SQL, and Python, students learn data analysis and applications of machine learning using real-world datasets.

Credit Hours

3

GER

QR

Requisites

QTM 100 or QTM_OX 100 or equivalent transfer credit as prerequisite..

Cross-Listed

None

Includes topics related to statistical computing.

Credit Hours

1 - 4

GER

None

Requisites

None

Cross-Listed

None

This writing-intensive course provides students with practice developing rhetorically effective and ethically sensitive communication in genres that characterize professional activity across and outside the university. No prior technical knowledge required.

Credit Hours

3

GER

None

Requisites

None

Cross-Listed

• ENGRD 302

This writing-intensive course provides students with practice developing rhetorically effective and ethically sensitive communication in genres that characterize professional activity across and outside the university. No prior technical knowledge required.

Credit Hours

4

GER

CW

Requisites

None

Cross-Listed

• ENGRD 302W

Upon completing this course, students will be able to define and discuss the concepts of bias, fairness, discrimination, ethics, and justice, with respect to data science, and will gain familiarity, via case studies and practical excercises, with how these concepts play out in data-driven inquiry.

Credit Hours

3

GER

None

Requisites

None

Cross-Listed

None

Introduction to game theory and strategic thinking. Foundational building blocks of non-cooperative games including normal and strategic form games, Nash equilibrium concept, various equilibrium concept refinements including backwards induction, sub-game perfection, and perfect Bayesian equilibrium.

Credit Hours

3

GER

HSC

Requisites

None

Cross-Listed

• POLS 376

Evolutionary Game Theory draws on ideas from classic Game Theory to explain these biological phenomena. The course will introduce basic concepts from Evolutionary Biology and from Game Theory, and combine them together to find evolutionarily stable strategies everywhere around us.

Credit Hours

3

GER

None

Requisites

None

Cross-Listed

None

This course will focus on the analysis of syntactic and semantic structures, ontologies and taxonomies, distributional semantics and discourse, as well as their applications in computational linguistics. Assignments will include advanced statistical analyses.

Credit Hours

3

GER

MQR

Requisites

QTM 220 or equivalent transfer credit as prerequisite.

Cross-Listed

None

Teaches common theories & techniques in data science using Python. Focus is text analysis (e.g., text parsing, language models, sequence estimation, vector space models & distributional semantics, cluster analysis, supervised learning). Cloud computing, big data, & data visualization are discussed.

Credit Hours

3

GER

None

Requisites

(QTM 150 and QTM 151) or CS 170 or equivalent transfer credit as prerequisite.

Cross-Listed

None

Explore the fundamentals of causality. You will be introduced to 2 commonly used approaches to studying causality: Directed Acyclic Graph approach and Potential Outcome approach. Each module will expose you to a particular method and three components: intuition, formalization, and application.

Credit Hours

3

GER

None

Requisites

QTM 220 or ECON 320 or equivalent transfer credit as prerequisites.

Cross-Listed

None

Introduces students to the field of machine learning, an essential toolset for making sense of the vast and complex data sets that have emerged in the past 20 years. Presents modeling/prediction techniques that are staples in the fields of machine learning, artificial intelligence, and data science.

Credit Hours

3

GER

None

Requisites

(QTM 220 or ECON 320) and (QTM 110 or QTM_OX 110) and (QTM 150 or QTM_OX 150) and (QTM 210 or QTM_OX 210 or ECON 220 or ECON_OX 220 or MATH 361) and (MATH 210 or MATH_OX 210 or MATH 211 or MATH_OX 211) and (MATH 211 or MATH_OX 211) or transfer credit.

Cross-Listed

None

This course emphasizes programming for data science, rather than programming for the sake of programming. Students learn essential computer literacy (e.g. shell commands), computing concepts & workflow for reproducible research. Students primarily write Python code and use cloud computing resources.

Credit Hours

3

GER

None

Requisites

(QTM 150 and QTM 151) or CS 170 or equivalent transfer credit as prerequisite.

Cross-Listed

None

This course covers the fundamentals of time series analysis in both the natural and social sciences, utilizing analytical, statistical, and numerical approaches. We will focus on the application of these methods to complex, real world data from medicine, economics, geology, and other fields.

Credit Hours

3

GER

None

Requisites

QTM 220 or equivalent transfer credit as prerequisite.

Cross-Listed

• BIOL 355

Covers models for qualitative (count, binary, ordinal, or nominal) data, testing for goodness-of-fit, analyzing contingency tables, regression models for count, binary, and multiple categorical response data, etc.The Generalized Linear Models provide a unifying framework for the course.

Credit Hours

3

GER

None

Requisites

QTM 220 or equivalent transfer credit as prerequisite.

Cross-Listed

None

This course is to introduce fundamental ideas in statistical inference. It includes probability theory, and traditional parametric statistical inference, estimation, and hypothesis testing.

Credit Hours

3

GER

None

Requisites

(QTM 220 or ECON 320) & (QTM 110 or QTM_OX 110) & (QTM 150 or QTM_OX 150) & (QTM 210 or QTM_OX 210 or ECON 220 or ECON_OX 220 or MATH 361) & (MATH 210 or MATH_OX 210 or MATH 211 or MATH_OX 211) & (MATH 221 or MATH_OX 221) or equiv.transfer credit prereq.

Cross-Listed

None

Special Topics Courses. Includes Game Theory I/II, Maximum Likelihood Estimation, Longitudinal Data Analysis, Experimental Methods, Survey Research Methods, Computational Modeling, and Advanced Topics: Bayesian Statistics.

Credit Hours

1 - 4

GER

None

Requisites

None

Cross-Listed

None

Special Topics Courses that focus on the process and products of writing. Topics vary from semester to semester.

Credit Hours

1 - 5

GER

CW

Requisites

None

Cross-Listed

None

Study Abroad

Credit Hours

1 - 12

GER

None

Requisites

None

Cross-Listed

None

Engage in statistical study and mentor peers in statistics; attend an orientation, develop mentoring skills, have weekly meetings with lecturer, attend one QTM 100 section per week, and hold mentoring sessions for current students. (2 credits) OR Aid TA in QTM 100 Lab (1 credit)

Credit Hours

1 - 2

GER

None

Requisites

None

Cross-Listed

None

When can causal statements be robust? Students will learn about advanced estimates, doubly robust estimators, synthetic controls, decision theory, and other advanced causal methods.

Credit Hours

3

GER

None

Requisites

QTM 345 or equivalent transfer credit as prerequisite.

Cross-Listed

None

An interdisciplinary exploration of digital tools for analyzing and visualizing data in the humanities and social sciences.

Credit Hours

3

GER

HSC

Requisites

None

Cross-Listed

• SOC 446
• LING 446

An interdisciplinary exploration of digital tools for analyzing and visualizing data in the humanities and social sciences.

Credit Hours

4

GER

HSCW

Requisites

None

Cross-Listed

• SOC 446W
• LING 446W

Classical decision models rely on strong distributional assumptions about uncertain events; these topics are covered in QTM 347. QTM 447 covers advanced machine learning methods for modeling the of interplay between data, personalization, and decision optimization in the face of uncertainty.

Credit Hours

3

GER

None

Requisites

QTM 347 & (QTM 220 or ECON 320) & (QTM 110 or QTM_OX 110) & (QTM 151 or QTM_OX 151) & (QTM 120 or MATH 210 or MATH_OX 210 or MATH 211 or MATH_OX 211) & (QTM 210 or QTM_OX 210 or ECON 220 or ECON_OX 220 or [MATH 361 & MATH 362]) & (MATH 221 or MATH_OX 221) or equiv. transfer credit as prerequisite.

Cross-Listed

None

Machine learning (ML) models make predictions, but prediction is only half the data story. Even cutting-edge ML algorithms are imperfect: they make mistakes, large and small. How do we quantity this uncertainty? When will multi-stage procedures converge in large samples? How accurate can they be?

Credit Hours

3

GER

None

Requisites

(QTM 220 or ECON 320) and( QTM 151 or QTM_OX 151) or equivalent transfer credit as prerequisites.

Cross-Listed

None

Selected advanced topics in quantitative sciences. Open only to junior and senior majors; others by permission of instructor.

Credit Hours

3

GER

None

Requisites

None

Cross-Listed

None

Selected advanced topics in quantitative sciences. Open only to junior and senior majors; others by permission of instructor.

Credit Hours

4

GER

CW

Requisites

None

Cross-Listed

None

The first part of the course introduces the logic of experimentation and discusses various methodological issues in the design and analysis of experiments. The second part builds on this foundation to discuss some practical issues and ethical considerations in designing and implementing experiments.

Credit Hours

3

GER

None

Requisites

None

Cross-Listed

None

For students participating in the Quantitative Sciences honors program. Student is expected to pursue an honors committee approved project. Course objectives include support for research, analysis of data, synthesis and presentation of results/observations, and initiation of writing the thesis.

Credit Hours

4

GER

XA

Requisites

None

Cross-Listed

None

QTM 495B is for students participating in the Quantitative Sciences honors program. Students will focus on data analysis and writing the thesis. Students will also be mentored in oral presentation skills and preparation of their work for publication. This class is an independent study format.

Credit Hours

1 - 8

GER

CW

Requisites

None

Cross-Listed

None

Pre-reqs: QTM 110, 120, 210. Open to QSS majors only. Permission required by Internship Director. Supervised participation in a quantitatively focused internship approved by the Institute. No more than 4 credit hours may count toward QSS major elective credit. 3.0 minimum GPA required.

Credit Hours

1 - 12

GER

XA

Requisites

None

Cross-Listed

None

Pre-reqs: QTM 110, 120, 210. Open to QSS majors only. Permission required by Internship Director. Supervised participation in a quantitatively focused internship approved by the Institute. No more than 4 credit hours may count toward QSS major elective credit. 3.0 minimum GPA required.

Credit Hours

1 - 12

GER

XAW

Requisites

None

Cross-Listed

None

Permission required by instructor. Independent reading and research under the direction of a faculty member. No more than 4 credit hours may count toward QSS major elective credit.

Credit Hours

1 - 12

GER

None

Requisites

None

Cross-Listed

None

Permission required by instructor. Independent reading and research under the direction of a faculty member. No more than 4 credit hours may count toward QSS major elective credit.

Credit Hours

1 - 12

GER

CW

Requisites

None

Cross-Listed

None

The capstone course provides an opportunity for students to apply their knowledge of the foundations, theory and methods of data science, along with their substantive expertise to address data driven practical problems in industry, government, and the non-profit sector.

Credit Hours

1 - 4

GER

None

Requisites

QTM 220 or equivalent transfer credit as prerequisite.

Cross-Listed

None

Designed for majors (QSS, AMS, PPA, and BBA + QSS, etc.) working on independent research under the direction of faculty. Students expected to be familiar with the project, and involvement must include the employment of their statistical, computational, mathematical, and/or theoretical knowledge.

Credit Hours

1 - 12

GER

None

Requisites

None

Cross-Listed

None