Undergraduate Courses
Are you considering a double major or taking a major and a minor?
Per College policy, you can:
- complete 1 major
- OR 1 major and 1 minor
- OR 2 majors.
You may NOT have 2 minors, 3 majors, 2 majors + 1 minor, etc.
- only double-count two courses between any two majors or a major and a minor. That means that if you are a Sociology-track QSS major and Sociology double major, a maximum of two courses between these two majors can overlap. This is true for any double major or major/minor combination in the college.
- not take some double majors. Check with your faculty advisor or Sadie Hannans to ensure your double major is allowed by the College.
QTM Majors & Minor Course Flow
All classes counting toward a QTM major or minor must be taken for a letter grade. Please note, students must meet the minimum GPA requirement of 2.0 to graduate with any major or minor from the department.
If you have any questions about undergraduate QTM courses, please contact the Program Coordinator, Ms. Sadie Hannans.
QTM Undergraduate Courses
Course Name | Course Description | Credit Hours | Offered | Notes |
| QTM 100: Intro to Statistical Inference | 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. | 4 | Fall, Spring, Summer | No pre-reqs Required for QSS minor, does not count toward any QTM majors
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| QTM 110: Intro to Scientific Methods | 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. | 3 | Fall | No pre-reqs Required for QTM majors and minors
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| QTM 150: Introduction to Statistical Computing I | 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. | 2 | No pre-reqs | |
| QTM 151: Introduction to Statistical Computing II | 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. | 1-2 | This course is taught in Python. QTM 150 is not required for this course. This course is an introduction to the Python programming language and SQL for students without prior programming experience. The purpose of this course is to prepare students for upper-level electives in data analysis related courses. It will cover the programming basics of Python which include understanding data types, controlling flow using loops and conditional statements, and writing functions. In addition to these basics, this course will put emphasis on skills that are relevant for data analysis which include 1) data manipulation such as merging, appending, and reshaping using SQL and Python, and 2) making various plots for descriptive analysis using Python. | |
| QTM 185: Applied Topics in QTM | 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. | 1 | IRR | |
| QTM 190: First-Year Seminar in QTM | 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. | 3 | Fall Spring | (Freshmen Seminar - Limited to First Year Students Only) Exploring Your World Through Data We live in a world overflowing with data. In this first year seminar, we build an understanding of how to use data to make insights about our world. We do this through a series of case studies, each focusing on a different social science topic and a different data science skill set. Our case studies include exercises from anthropology, human biology, sociology, economics, global health and demography and more. Through these cases, we will learn (and practice) a tool kit of computational skills and analytical thinking that includes data visualization, inferential thinking, basic statistical analyses, and text mining, and web scraping. By the end of the seminar, you will have made studied many interesting aspects of your world and learned some valuable computational skills. No programming experience is required!
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Course Name | Course Description | Credit Hours | Offered | Notes | |
| QTM 200: Applied Regression Analysis |
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| QTM 210: Probability and Statistics | 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. | 4 | Spring | QTM 120 or MATH 210 or MATH_OX 210 or MATH 211 or PHYS 211 or MATH_OX 211 or equivalent transfer credit as prerequisite.
A permission code is required to enroll in this course. QTM majors and minors received email instructions for how to request a code. Non-QTM students may contact Ms. Sadie Hannans (shanna9@emory.edu) for assistance. Please note the prerequisites for the course before requesting permission.
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| QTM 220: Regression Analysis | 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. | 4 | Fall | (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.
Students taking lecture QTM 220 -1 must enroll in lab QTM 220-2. A permission code is required to enroll in this course. QTM majors and minors received email instructions for how to request a code. Non-QTM students may contact Ms. Sadie Hannans (shanna9@emory.edu) for assistance. Please note the prerequisites for the course before requesting permission. | |
| QTM 250: Applied Computing | 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. | 3 | QTM 100 or QTM_OX 100 or equivalent transfer credit as prerequisite. | ||
| QTM 285: Topics in Quantitative Science: Data Sci. Startups&Enterprise | Includes topics related to statistical computing. | 3 | Many executives know they should be using data science, but most have no idea where to start. This seminar trains both functional and technical professionals in the business of data science, so that they may provide that guidance to executives and their teams. As a result, QTM students learn to apply their top-tier training successfully in the business environment. This course is structured as a seminar with invited guest speakers. In the first part of the course, students learn how to identify data science use cases and build business cases around them, carefully differentiating the nature of a startup's business case from an enterprise one. In the second part of the course, students survey methodologies for turning "data science as a hobby" into "data science as a business" by building an organizational capability around those use cases. In the third part of the course, students gain a broader perspective on the strategic implications of these use cases, especially in an environment increasingly subject to statutory and regulatory scrutiny. Each part of the course contains 1) an interactive lecture component, which introduces the topic and provides a problem solving framework; 2) a group work component, in which students work in groups of 4-5 to tackle the topic and present their solutions, and; 3) invited guests, who provide key context with their experiences in the startup and enterprise spaces. This course counts for a QSS minor upper-level elective but does NOT count as a QSS, PPA, AMS or BBA+QSS upper-level elective. |
Course Name | Course Description | Credit Hours | Offered | Notes |
| QTM 302W: Technical Writing | 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. | 4 | (Same as ENGRD 302W) (Permission Required Prior to Enrollment) (Technical Writing for Data Science - In this writing-intensive course, we will practice the effective and ethical communication of specialized technical knowledge and quantitative data. Because data are information given order, we will consider their arrangement and re-arrangement as rhetoric. We will introduce rhetorical analysis as a model for understanding how quantitative data can be interpreted and conveyed for multiple audiences that range from professional researchers to the lay public. Guided by this knowledge, you will exercise strategies for communicating via the text, speech, and visual conventions that are integral to preparing "technical" genres such as research reports, research translations, informative and persuasive infographics, instructions, and data repositories. We will adapt these genres and their conventions for our course goals that center collaboration, transparency, and reproducibility, while emphasizing the methodological questions to ask when bringing new purposes to data prepared by others, as well as whom to ask, and how. As you gain comfort with thinking of writing and analysis as intertwined practices of exploratory inquiry, we will focus especially on how these practices shape the social impacts of our data-driven narratives. Email permission number requests to Sadie Hannans (shanna9@emory.edu). Do not contact the instructor or other staff directly to request placement in this class.) | |
| QTM 310: Introduction to Data Justice | 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 exercises, with how these concepts play out in data-driven inquiry. | 3 | Irregularly | Prerequisites: None Permission code request form (same for all QTM courses: https://forms.office.com/r/bq8grdpG25 This course, team-taught by faculty spanning the various areas represented in QTM, will introduce students to both theoretical and methodological issues related to data justice. This emerging field considers how questions about data, its collection, and its use, are connected to broader social and political concerns, and how data-driven systems can be designed more equitably. Such data is expansive and expanding and serves as the basis for automated systems that range from resume screening to voting redistricting, predictive policing to cellphone autocomplete. A central theme of the course is that choices (and trade-offs) are ubiquitous when bringing data to bear on technical and policy decisions. Few, if any, meaningful measures are truly ``theory-free,'' in the sense that a measure is (perhaps implicitly) measuring something, and in many cases, this something is latent and not directly observable. Furthermore, even seemingly objective algorithmic systems may not be 'neutral' in their effects: many of these systems rely upon data or were designed to achieve goals that reflect existing biases and inequalities embedded in the world. |
| QTM 315: Game Theory I | 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. | 3 | ||
| QTM 325: Evolutionary Game Theory | 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. | 3 | Fall | |
| QTM 329: Computational Linguistics | 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. | 3 | Spring | QTM 220 or equivalent transfer credit as prerequisite.
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| QTM 340: Approaches to Data Sci.w/Text | 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. | 3 | QTM 210 or CS 171 or CS 171Z or CS_OX 171 or equivalent transfer credit as prerequisite. | |
| QTM 345: Causal Designs and Inference I | 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. | 3 | Spring | QTM 220 or ECON 320 or equivalent transfer credit as prerequisites. |
| QTM 347: Machine Learning I | 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. | 3 | Fall, Spring | (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. This course is designed to introduce students to the field of machine learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This class will present a number of important modeling and prediction techniques that are staples in the fields of machine learning, artificial intelligence, and data science (more broadly). In addition, this course will cover the statistical underpinnings of common methods. |
| QTM 350: Data Science Computing
| 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. | 3 | QTM 220 or equivalent transfer credit as prerequisite. | |
| QTM 355: Introduction to Time Series Analysis | 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. | 3-4 | QTM 220 or equivalent transfer credit as prerequisite. | |
| QTM 365: Parametric Statistics | Understanding the limits of what we can learn from our data and how to reach those limits. Knowing this allows us to run the smallest experiment large enough to give us the precision we need and can keep us from wasting time trying to wring an unattainable level of precision from data. | 3 | Fall | (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. This class is to introduce fundamental ideas in statistical inference. It includes probability theory, and traditional parametric statistical inference, estimation, and hypothesis testing. |
| QTM 385/385W: Special Topics: QTM: Social Network Analysis | 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. | 3-4 | Fall, Spring | QTM 110 and QTM 120 and QTM 210 or equivalent transfer credit as prerequisite. Network analysis shifts the research focus from individual units to their connections and so brings both theoretical and methodological innovations. 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 actor-oriented model for dynamic network analysis, meta network analysis, weighted network analysis, text network analysis, causal analysis of network effects, and social network-based predictions and interventions. This course requires a basic knowledge of logistic regression and basic programming skills in R. Completion of QTM 220 is highly recommended prior to enrolling in this course. |
| QTM 390: Special Topics Taken Abroad | Study Abroad | 1-12 |
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| QTM 398R: Peer Mentoring in Statistics | 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) | 1-2 | Fall, Spring |
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Course Name | Course Description | Credit Hours | Offered | Notes |
| QTM 445: Advanced Causal Inference | When can causal statements be robust? Students will learn about advanced estimates, doubly robust estimators, synthetic controls, decision theory, and other advanced causal methods. | 3 | QTM 345 as prerequisite | |
| QTM 446/446W: Big/Small Data and Visualization | An interdisciplinary exploration of digital tools for analyzing and visualizing data in the humanities and social sciences. | 3-4 | (Same as SOC 446W 1, LING 446W 1) The course deals with new tools of analysis and visualization of text data. It is a 4-credits course, fulfilling the writing requirement with extensive weekly writing. The course does NOT require any prerequisites or prior knowledge of computational tools. Freshmen are not allowed to register. Text corpora will be assigned for analysis but students are welcome to use their own texts (e,g., newspaper articles, books, blogs). The course is based on a set of specialized Natural Language Processing tools (NLP), written in Python, designed for the analysis of small/large corpora of text. The tools come with an easy-to-use Graphical User Interface (GUI). From sentence splitter, to tokenizer, lemmatizer, parser with its Part-of-Speech tags (POSTAG), Dependency Relations (DEPREL), Named Entity Recognition (NER), semantic trees, sentence complexity and text readability, noun and verb analysis, n-grams viewer, sentiment analysis, topic modelling, extraction of SVOs (Subject-Verb-Object), and shape of stories, word embeddings (Word2Vec) you will learn the language of Natural Language Processing (NLP). The course also embeds tools of data visualization as word clouds, Excel-type charts, network graphs, and Geographic Information System (GIS) maps. The course uses freeware software exclusively. The course has no required books. | |
| QTM 447: Statistical Machine Learning 2 | 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. | 3 | Irregularly | 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 & 362]) & (MATH 221 or MATH_OX 221) req A second course in 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. Extends modeling/prediction techniques covered in introductory machine learning courses and introduces concepts central to statistical learning theory. Prerequisite: QTM 347, or another Machine Learning course with permission of instructor |
| QTM 465: Semiparametric Statistics | Machine learning/nonparametric models make individual predictions, but prediction is only half the story for some industry and policy applications. Analysts often want to construct summary statistics to interpret individual predictions, decompose the observed patterns in the data, or use predictions to understand the effects of hypothetical policy changes. Yet even cutting-edge machine learning algorithms are imperfect: they make prediction mistakes, large or small. How do we quantity the uncertainty of the resulting summary statistics? When will multi-stage procedures converge in large samples? How accurate can they be? How do we construct confidence intervals? | 3 | Irregularly | QTM 210 and QTM 220 or ECON 320 and QTM 150 are prerequisites |
| QTM 490 R:Advanced Seminar: Social Choice | Selected advanced topics in quantitative sciences. Open only to junior and senior majors; others by permission of instructor. | 3 |
This course meets with POLS 555. You MUST have taken MATH 250 or equivalent to successfully perform in this course. Please contact Sadie Hannans (sadie.marie.hannans@emory.edu) for permission to enroll This course provides a rigorous introduction to the field of social choice theory. Social choice theory analyzes how groups of people should and do make collective decisions. It takes a collection of people with well-defined and heterogeneous preferences as an input and then examines the different ways in which we can construct a group preference from these inputs. The construction is achieved through the use of a preference aggregation rule which could be equivalent to a voting rule or something else entirely. The goal of the class is to examine the properties of different types of rules, and to characterize rules that yield desirable group outcomes. The elegance and power of many social choice-theoretic results such as Arrow's impossibility theorem, the McKelvey-Schofield chaos theorems, the Gibbard-Satterthwaite theorem, and the Plott conditions have changed the way that social scientists think about decision making procedures and outcomes. The influence of social choice influence extends across disciplines in the social and behavioral sciences with applications in political science, economics, sociology, information science, environmental management, and health sciences. | |
| QTM 490RW: Advanced Seminar: Natural Language Processing | Selected advanced topics in quantitative sciences. Open only to junior and senior majors; others by permission of instructor. | 4 | Same as SOC 489W-1 and LING 485W-1 (Permission Required Prior to Enrollment) Prerequisite 446 Goal. The seminar is a sequel to Soc/Ling/QTM 446 and Soc 585 (Big/Small Data & Visualization). It aims to bring to a co-authored journal submission the work carried out by students in 446/585 on different text corpora. Using a range of NLP tools, students will strive to provide a coherent picture of NLP analytics as these apply to their specific corpus. The course is a 4-credit course. Co-authorship. Co-authors for journal submissions include professor, undergraduate team members, and computer-science students who have helped develop the NLP tools. Graduate students are free to work alone on their corpus with no obligation of shared co-authorship. Teamwork and workload. Students will work in teams, one team per corpus. Teams will be expected to present their work, as a team, every two weeks throughout the semester and present four co-authored drafts of their ongoing work at regular intervals. Final paper and literature review. Since the final paper is expected to be submitted to a journal, the literature review part of the paper will engage both appropriate NLP literature and specific topic literature as appropriate for the corpus (thus, a corpus of country folktales will need to address the literature on folktales). Grading. Final grade will be based on presentations, paper drafts, and final paper. | |
| QTM 491: Design / Analysis Experiments | 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. | 3 | Spring | (Permission Required Prior to Enrollment) Prerequisites: [QTM 220 or ECON 320], QTM 110, QTM 150, [QTM 210 or ECON 220 or MATH 362], [MATH 210 or MATH 211], and MATH 221. Permission Code request form (same for all QTM courses): https://forms.office.com/r/bq8grdpG25 The use of experiments has grown notably across the social sciences, with the promise of delivering simple, transparent, and replicable causal inference. This course will discuss the design, analysis, and interpretation of introductory and advanced experimental designs. The first part of the course covers the theory and logic of experiments. The second part covers issues and challenges in implementing experiments that deliver on their promises. Topics include: models of potential outcomes, blocking and covariate adjustment, non-compliance, attrition, heterogeneous treatment effects, interference, mediation, and ethics. Assignments will focus on developing practical skills for the implementation of experiments in real research settings. |
| QTM 495A: Honors Research | 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. | 4 | ||
| QTM 495BW: Honors Research | 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. | 1-8 | ||
| QTM 497R: Directed Study | 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. | 1-12 | ||
| QTM 497RW: Directed Study | 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. | 1-12 | ||
| QTM 498R: Quantitative Sciences Capstone | 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. | 3 | QTM 220 or equivalent transfer credit as prerequisite. Data project with DeKalb County District Attorney's Office Capstone enrollment is by application only. QTM majors and minors should visit the QTM Prep Canvas webpage for application information. Co-enrollment in a 1-CR writing lab is required for QTM498R: QSS Capstone (total of 4 CR to enroll in QSS capstone). | |
| QTM 499R: Directed Research | 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. | 1-12 | Fall, Spring, Summer |
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QTM Undergraduate Course FAQs
Q: How do I get a permission code for a QTM course?
A: You will need to contact our Undergraduate Program Coordinator Sadie Hannans to gain permission codes for QTM undergraduate courses.
Q: Can I skip or self-study the pre-requisite for a QTM course?
A: No. Pre-requisites are required.
Q: Can I take a QTM course’s pre-requisite at the same time as the course?
A: No. Pre-requisites are required.
Q: Can I be overloaded to take a QTM course with a specific instructor? They fit my learning style better, but their class is full.
A: No. Unfortunately, we cannot accommodate instructor preference in any circumstance.
Q: How do I enroll in the Spring QTM Capstone course?
A: The QTM Capstone Program is part of QTM's suite of experiential learning. This is handled by application only. Learn more about it here.
Q: How likely am I to get off the waitlist for a QTM class?
A: There are no guarantees with the waitlists. However, there is a lot of movement on the waitlists during Add/Drop/Swap. Your schedule must keep your desired course’s timeslot open to ensure you are not skipped during auto-enroll.
Q: I joined the waitlist for a QTM course, but now I have a conflict, so the waitlist will not enroll me. Can you fix this?
A: QTM administrators cannot fix this. Click here for the Swap-If instructions or contact the Registrar.
Q: Will you open another section of a specific QTM course?
A: The sections you see available are what we have the capacity to offer. If we have the capacity to open more, we will open more based on our internal assessment of need.
Q: I am a QSS major, and I want to change my track but keep my major the same. How can I do that?
A: Submit a new declaration of major form. You will still need to meet with the QTM Program Coordinator for a brief appointment to complete the change.
Q: If I change my QSS track, can I keep my current advisor?
A: We assign advisors based on which of our faculty are equipped to advise you in a relevant field. If both you and your current advisor agree that there is still a good fit despite your change of track, then you may keep your advisor. Be sure to provide written agreement from your current advisor to the QTM Program Coordinator during your re-declaration appointment.
Q: I have declared one of the four QTM majors, but I would like to switch to a different QTM major. Will my courses still count?
A: The beauty of the QTM curriculum is that core courses are largely transferrable across QTM majors. In general, a more technical course can be used to substitute for a more applied course, but the reverse is not true. For example, if you were an AMS major and completed MATH 361 + MATH 362, these two courses would be able to substitute for QTM 210. If you were a QSS minor and completed QTM 200: Applied Regression, that course would not be able to substitute for QTM 220: Regression Analysis.
You may also find it helpful to read the "Common Course Equivalencies" tab!
Q: I have declared one of the four QTM majors, but I want to switch to the QSS minor. Will my courses still count?
A: See the answer above.
Q: I am a QSS major, and I need help choosing track courses. What do you recommend I take?
A: The best resource for track courses is your QSS track advisor. You can view your advisor on OPUS.
Below are courses offered outside of QTM that QTM considers equivalent for its majors:
- MATH 112 may be taken in place of MATH 111 as a QTM major prerequisite
- MATH 211 may be taken in place of MATH 210 for the QSS, PPA, or QSS+BBA majors
Below are courses that act as equivalents to their respective QTM courses:
QTM 210 may be substituted with:
- ECON 220 and [MATH 210 or MATH 211]
- OR - MATH 362
QTM 220 may be substituted with:
- ECON 320, (QTM 110, QTM 150, MATH 221 and [MATH 210 or MATH 211])
QTM equivalents are only valid within the QTM department and QTM majors. Unless otherwise stated on a non-QTM department website or official documentation, these equivalents may not be recognized as valid outside of QTM.