Quantitative Analysis Center

QAC Courses

Please see Wesmap for scheduling and additional information/requirements

Working with Excel and VBA (QAC151)

Credit: .25

Description: Many of us know Excel for its spreadsheets: a quick and easy way to store some information, share it, and maybe make some charts. The goal of this course is to show you the more advanced features of Excel. We will write code in Visual Basic for Applications, learn how to import data from external databases and web-based resources, create custom menus to interact with a user, and examine how Excel can be used in business decision-making.

Working with Mathematica (QAC 153)

Credit: .25

Description: The course introduces students to Mathematica's computing environment and all the basic features of the software. Starting with basic operations and computations students will be introduced to graphics and visualization, mathematical computations, and will learn through a series of hands-on lab exercises to use the Mathematica programming language for modeling and data analysis. While there are no prerequisites, a basic familiarity with computing tools, understanding of descriptive statistics along with a basic calculus background and a willingness to make mistakes and learn from them is expected.

Working with Python (QAC155)

Credit: .25

Description: The course introduces students to programming, data management and analysis with Python. Through a series of hands on lab exercises students learn to work with a variety of data using a high-level programming language and associated libraries to effectively manage and analyze their data. The emphasis is on data exploration and visualization and includes work with unstructured data generated by social media interactions. While there are no prerequisites, a basic familiarity with computing tools, understanding of descriptive statistics and a willingness to make mistakes and learn from them is expected.

Working With R (QAC 156)

Credit: .25

Description: The course introduces students to programming, data management and analysis with R. Through a series of hands on lab exercises students learn to work with a variety of data formats and use R's programming language and associated packages to effectively manage and analyze their data, with an emphasis on data exploration and visualization. While there are no prerequisites, a basic familiarity with computing tools, understanding of descriptive statistics and a willingness to make mistakes and learn from them in expected.

Working with SAS (QAC 157)

Credit: .25

Description: The course introduces students to programming, data management and analysis with SAS. Through a series of hands on lab exercises students learn to work with a variety of data formats and use SAS' programming capabilities to effectively manage and analyze their data, with an emphasis on data exploration and visualization. While there are no prerequisites, a basic familiarity with computing tools, understanding of descriptive statistics and a willingness to make mistakes and learn from them is expected.

Working With Stata (QAC 158)

Credit: .25

Description: The course introduces students to programming, data management and analysis with Stata. Through a series of hands on lab exercises students learn to work with a variety of data formats and use Stata's programming capabilities to effectively manage and analyze their data, with an emphasis on data exploration and visualization. While there are no prerequisites, a basic familiarity with computing tools, understanding of descriptive statistics and a willingness to make mistakes and learn from them is expected.

Applied Data Analysis (QAC201)

Crosslistings: SOC 257, GOVT 201, PSYC 280, NS&B 280
Credit: 1

Description: In this project-based course, you will have the opportunity to answer questions that you feel passionately about through independent research based on existing data. Students will have the opportunity to develop skills in generating testable hypotheses, conducting a literature review, preparing data for analysis, conducting descriptive and inferential statistical analyses, and presenting research findings. The course offers unlimited one-on-one support, ample opportunities to work with other students, and training in the skills required to complete a project of your own design. These skills will prepare you to work in many different research labs across the University that collect empirical data. It is also an opportunity to fulfill an important requirement in several different majors.

Digging the Digital Era: A Data Science Primer (QAC 211)

Credit: 1

Description: The course introduces students to the practice of what has come to be known as data science. Using a multidisciplinary approach and data from a variety of sources that cover any aspect of everyday life--from credit card transactions to social media interactions and web searches--data scientists try to analyze and predict events, and behavior. The first part of the course defines the area and introduces basic concepts, tools and emerging applications. We describe how "big data" analysis affects both business practices and public policy, and discuss applications in different areas/disciplines. We also discuss the ethical, legal, and privacy dimensions of "big data" analysis. In part two of the course, we work on data acquisition and management and introduce appropriate programming and data management tools. In part three, we concentrate on basic analytical and visualization techniques as we explore and understand the emerging patterns. Using a learning-by-doing approach in a computing laboratory, students will learn how to write computer programs in R to access, organize, and analyze data through a series of small projects designed to illustrate the application of the techniques we develop for a variety of data sets and situations. Students will also engage in a semester-long project where they will access and use data from social media (Twitter) to address their own research questions.

Introduction to Modeling: From Molecules to Markets (QAC 221)

Crosslisting: PHYS 221
Credit: 1

Description: The development of models to describe physical or social phenomena has a long history in several disciplines, including physics, chemistry, economics, and sociology. With the emergence of ubiquitous computing resources, model building is becoming increasingly important across all disciplines. This course will examine how to apply modeling and computational thinking skills to a range of problems. Using examples drawn from physics, biology, economics, and social networks, we will discuss how to create models for complex systems that are both descriptive and predictive. The course will include significant computational work. No previous programming experience is required, but a willingness to learn simple programming methods is essential.

Introduction to (Geo)Spatial Data Analysis and Visualization (QAC 231)

Credit: 1

Description: Geographic information systems (GIS) provide researchers, policy makers, and citizens with a powerful analytical framework for spatial pattern recognition, decision making, and data exploration. This course is designed to introduce social science and humanities students to spatial thinking through the collection, management, analysis, and visualization of geospatial data using both desktop and cloud-based platforms. Classes will consist of short lectures, hands-on training using different spatial analysis and geodesign technologies (e.g. ESRI ArcGIS, Google Fusion Tables, MapBox), group projects, critiques, and class discussions. Weekly readings and assignments will build skills and reinforce concepts introduced in class. The course will culminate in the development of a group project. Guest lectures by faculty across campus will allow students to comprehend the breadth of applicability geospatial thinking in today's research arena. The course is part of Wesleyan's Digital and Computational Knowledge Initiative and is aimed at students with limited or no prior GIS experience.

Special Topics in Computer Science ("Big" Data Analysis) (QAC 260/360)

Crosslisting: COMP 260/360
Credit: 1

Description: These two sections of COMP 260 and 360 will meet at the same time. In this class, Computer Science students will team up with students in other disciplines to work on a research problem that requires signi cant computation-intensive data analysis. All students will learn the fundamental techniques of such analysis. The speci c techniques to be learned will be determined by the research problems; some that we might cover are clustering, component analysis, Bayesian analysis, and time-series analysis. The Computer Sciene students will be responsible for developing a well-written software platform that can be used for the project-spei c analysis. Ideally the platform will be reusable in other projects. Along the way, they will learn appropriate langauges for data analysis and core software development principles. The students from other disciplines will fully develop their research proposal and produce an appopriate research paper describing the project and its results.

  • Enrollment is POI only and the course is limited to 19 students.
  • MATH 122 is a prerequisite for all students, and more mathematics and/or statistics background will be helpful.
  • COMP 360 is open to Computer Science students only and has COMP 212 prerequisite. To submit a POI request for COMP 360, the student must submit a brief statement indicating relevant background from other courses, employment, or independent projects. We encourage CS students to indicate what kinds of problems or data anlysis techniques they are interested in, so as to help us choose an appropriate mix of students.
  • COMP 260 is open to students from other disciplines; there are no COMP prerequisites for this section, though programming background will certainly be helpful. To submit a POI request for COMP 260, the student must submit a one-half to full-page proposal of the research project. The proposal must give a sense of the question to be answered and the kind of data to be colelcted and analyzed. We encourage proposals that have some faculty backing such as proposals that will contribute to an honors thesis or are part fo an independent research course. Students should also indicate relevant Computer Science, Mathematics, or statistics courses taken.
  • We encourage joint proposals from computing and non-computing students. However, students are not required to make joint proposals; we will match students as well as possible given project needs and student interests. Statements and proposals may be combined into a single document for joint proposals.

Statements (for COMP 360) and proposals (for COMP 260) must be in PDF format (no other formats are acceptable) and should be e-mailed to ndanner@wesleyan.edu by 08 May 2013.  The submission must also include e-mail contact information.

Project-based Programming for Research (QAC 261)

Crosslisting: PSYC 381
Credit: 1

Description: This project-based course will introduce students to programming in the context of research design, data visualization and analysis of Big Data, focusing on the essential concepts and tools needed to carry out research and problem solving and to keep abreast of new technologies. We will survey these topics by combining scientific problems and modern programming approaches and students will learn the fundamentals of programming required for structuring and conducting research.

Economics of Big Data (QAC 282)

Crosslisting: ECON 282
Credit: 1

Description: "Big data" is a popular buzzword that describes techniques using very large datasets, often from nontraditional sources. Many technology firms essentially base their businesses on big data; Google, Facebook, and Amazon are all examples. Increasingly there are opportunities and pressures to employ these techniques in other areas of the economy and society such as government, healthcare, and education. This course examines (1) big data analysis techniques and how they relate to conventional economic statistics, (2) the effect of big data on the economy, society, and privacy, and (3) practical methods of big data analysis using the R statistics package.

Statistics Education Practicum (QAC 301)

Credit: 1

DescriptionThis course will serve students who are pursuing their undergraduate degree in a variety of disciplines, but who want to expand their skills in statistics and applied data analysis in preparation for a future career. It will also serve students who are currently pursuing independent, quantitative research at the undergraduate or graduate level. 

The course will center on personal interaction in support of introductory statistics students. Active peer mentoring and supporting experiences will be based on the theory that good teachers (and learners) of statistics need to be "developed", as opposed to being "trained". In line with this theory, this hands-on course will provide an intensive opportunity to build specific knowledge regarding teaching and learning in the area of data driven statistical inquiry. 

Students enrolled in this course will a) attend statistics mentoring development sessions (1 hour/week); b) provide one-on-one support for introductory statistics students during workshop oriented class sessions (3 hours/week) ; c) lead small group mentored meetings for 5 to 6 statistics students (1 hour/week); and d) monitor and critique progress on applied data assignments (1 hour/week). In addition to these hands on experiences, students will pursue a project aimed at furthering the field of statistics education. Projects may take the form of course evaluation, content/conceptual curriculum development, or translation of educational, statistical software materials. 

Advanced GIS and Spatial Analyses (QAC 344)

Crosslisting: E&ES 344
Credit: 1

Description: A geographic information system (GIS) is a powerful database that allows for the collection, manipulation, analysis, and presentation of spatially referenced data. GIS technologies facilitate natural science, social science, and humanities research and any other project that utilizes location-based data. The Advanced GIS course will focus on individual projects conducted within a collaborative learning framework. Each student is responsible for developing and producing a semester-long project focused on advanced spatial data analyses and/or advanced cartographic design using a GIS. Students will enter the course with an individual or small team (2-3 students) project in mind. The project may be a component of a senior thesis, work on a faculty member's research project, a community-based service learning project, etc. Course sessions will be a mix of studio time for projects (e.g. work time, critiques), skill development (lectures, student-led skills training sessions), and intellectual advancement (e.g. guest speakers, conference attendance). Specific skills training sessions will be determined by components of each project.

Introduction to Statistical Consulting (QAC 380)

Crosslisting: PSYC 395
Credit: 1

Description: In this course, students will be exposed to realistic statistical and scientific problems that appear in typical interactions between statisticians and researchers. The goal is for students to apply what they have learned in their basic statistics and data analysis courses to gain greater experience in the areas of research collaboration, data management and analysis, and writing and presenting reports on the results of the analyses. An important objective of the course is to help develop communication skills, both written and verbal, as well as the professional standards and the interpersonal skills necessary for effective statistical consulting.

Proseminar: GIS in Research (QAC 239-1)

Credit: 1

Description: A geographic information system (GIS) is a powerful database that allows for the collection, manipulation, analysis, and presentation of spatially referenced data. GIS technologies facilitate natural and social science research and any other project that utilizes location-based data. The purpose of the proposed course is to develop, support, and expand the GIS users on campus by enriching geospatial literacy and enticing faculty, staff, and students to incorporate spatial data in their endeavors. Participants will learn tips and skills helpful to their individual projects up to and including advanced techniques for more experienced GIS users. Meetings will also include outside speakers currently applying GIS to their scholarship and/or teaching, skills workshops to expose participants to GIS techniques (e.g. georeferencing, Google Fusion Tables), group consultation sessions, and individual consultation.

Proseminar: Network Analysis (QAC 239-2)

Crosslisting: CIS 239
Credit: 1

Description: Seminar leaders from physics, political science, psychology, and chemistry, as well as outside speakers, will introduce participants to network analysis and explore its applications across different topics and disciplines. The purpose of the course is to enable participants to use network analysis in their work and facilitated collaborations across disciplinary lines. In addition to the regular class meetings, we will schedule hands-on workshops for participants to become familiar with appropriate software and further develop their computing skills.

Individual Tutorials

Credit: 1

Description: The QAC has also begun to provide individual tutorials for undergraduates interested in applied statistical training in support of independent projects. Through weekly meetings with an instructor as well as on-line materials and peer tutoring made available through the QAC, students across the disciplines are now able to more easily engage in independent quantitative work. For example, as a senior Earth and Environmental Studies major, Gabrielle Jehle completed a project that examined the relationshop between daily river discharge in the Mattabessett River and the richness and abundance of the local fish population.