Introduction to Modeling: From Molecules to Markets (QAC 221)
Instructor: Francis Starr
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.
Special Topics in Computer Science ("Big" Data Analysis) (QAC 260/360)
Instructor: Norman Danner
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 firstname.lastname@example.org by 08 May 2013. The submission must also include e-mail contact information.
Digging the Digital Era: A Data Science Primer (QAC 211)
Instructors: Arielle Selya/Manolis Kaparakis
WesMaps Listing: QAC 211
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 (Geo)Spatial Data Analysis and Visualization (QAC 231)
Instructor: Kim Diver
WesMaps Listing: QAC 231
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 aimed at students with limited or no prior GIS experience.
Proseminar: GIS in Research (QAC 239)
Instructor: Kim Diver
WesMaps Listing: QAC 239
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.
Digital History (COL/HIST) (COL 370)
Instructor: Michael Printy
WesMaps Listing: COL 370
Description: This course is an introduction and critical examination of the emerging field of Digital History.
Digital History is related to the new and vibrant filed of Digital Humanities, which has taken the academy by storm. The term “Digital Humanities” refers to the application of computing techniques to traditional humanities disciplines. This new field has implications for teaching and research, as well as for the presentation of cultural artifacts to the scholarly and general public. Digital humanists employ a wide-ranging set of techniques from text and data-mining to network analysis, topic modeling and 3D visualizations and animation. Digital Humanities (or DH) is also a highly collaborative field, and has sponsored far-flung interactions among scholars and students from disciplines that have traditionally been relatively isolated from one another.
Narrowing some of the broad questions raised by Digital Humanists, this course will take a disciplinary focus and will examine traditional questions pertinent to historical study, and ask how or whether they have been reconfigured by new media and new applications of computing power. How do we evaluate truth claims in this new environment? Does the change in the mode of historical representation also change the types of questions and research we do? Has the Web flattened the differences between public and scholarly history (and do these distinctions make sense)? How do digital tools enable new approaches to traditional fields such as scholarly editing?
The course will have a theoretical and practical side. We will explore readings on the promises and perils of digital techniques for historical practice, look at earlier embraces of technology in the historical sciences, and think through the relationship between historical research and historical representation. We will also briefly explore the history of computing and the Internet as it pertains to scholarly research and communication as well as public history. Students will explore and evaluate websites, tools, and other digital resources.
On the practical side, we will experiment with text-mining tools such as Voyant, Mallet, GIS, and n-grams in order to assess their usefulness in the analysis of historical document and corpora. We will look at online presentation and cataloging environments--particularly Omeka--to explore how these new tools may or may not change the way we represent the past. Students will work closely with resources in Wesleyan's Special Collections and Archives for hands-on experience with digital editing and presentation.