Courses for Data Analytics

This course explores the use of computer code as a form of creative practice and artmaking. Students discuss the history, practice, and current trends in computational art through a blend of theoretical and project-based learning. Through weekly examples and projects, students learn core concepts of computer science and apply them to the creation of digital artworks. Creative coding, the practice of writing computer programs for creative purposes, is practiced in many different domains of art and design. These include graphic design, generative art, interaction design, digital fabrication, data visualization, and installation art. To explore these different applications of creative coding, this course is oriented around four core topics: generative design, interaction design, data-driven art, and virtual environments. Prior experience with computer programming is not required.

An introduction to the interactions of individuals in a population, populations in a community, and communities in ecosystems. Laboratories are designed to illustrate ecological principles and give experience in approaches and techniques of ecology. Experimental design, quantitative data analysis, and statistics are emphasized throughout the course.

This course introduces MCB majors who did not take BIOL 211 (General Ecology) to important statistical concepts, experimental design, and data analysis tools that are covered in BIOL 211. Topics of study include: Introduction to the software R and RStudio, and introduction to basic statistical tests and data analysis and graphing using R and Excel.

This course introduces students to the principles and practical applications of bioinformatics in the analysis of genomic data. Students learn how to use bioinformatics software to evaluate and analyze genomic data to answer questions in molecular and evolutionary genetics.

How are different organisms connected? What characteristics do we use to classify and explain these connections across time and space? What cultural values have been used to construct current ecological theory? In this course we will experiment with what theories of ecology and evolution we can build by considering different philosophical starting points. Readings will include biology primary literature, articles from other fields, and from narrative, cultural knowledge, and political thought --primarily from people Indigenous to the land currently known as North America. Laboratory work will heavily involve experimenting with and interpreting mathematical models of biology theory using R, and will also include the gathering of ecological data to use in our theoretical work.

This course provides an in-depth examination of major ecological fields, including ecophysiology, island biogeography, community ecology, and ecosystem ecology. Current ecological research is used to introduce major concepts and methods, foster critical thinking and discussion, and to introduce issues of experimental design and analysis and different approaches to ecology. This course enhances skills that are critical for ecologists including written and oral communication skills, quantitative and programming skills.

The main goal of this course is to introduce students to the social scientific tradition of communication research. Over the course of the semester, students will be responsible for developing an interesting and novel research question and/or hypotheses based on scientific literature and theory. Students will learn how to critically evaluate empirical research and employ the scientific method to investigate issues and questions that arise within the study of human communication. Students will become familiar with survey research, experimentation, and techniques for data analysis.

This course introduces the student to the techniques of artificial intelligence. Students learn strategies for uninformed and informed (heuristic) search, knowledge representation, problem-solving, and machine learning. Additional topics may include motion planning, probabilistic reasoning, natural language understanding, and philosophical implications.

The management of data is one of the classical problems throughout the history of computing. This course centers around the fundamental concepts and theory that underpin the relational data model, which addresses numerous problems that plague data management, including data independence, consistency, information loss, and access performance. Course topics include the relational data model, database languages (e.g., SQL), relational database theory, database design (by decomposition), query execution, and considerations that affect system performance. Students design database schemas that effectively model an organization’s information requirements and write programs that require database integration. Students also gain insight through the analysis and implementation of influential data structures and algorithms that are commonly used in modern relational database systems.

This course concerns application of statistical theory to the analysis of economic questions. Students learn the tools of regression analysis and apply them in a major empirical project.

This course uses tools from economics and psychology to address individual decisions which are hard to account for with traditional, rational economic theory. Using both theoretical and laboratory methods, students explore topics involving both bounded rationality and bounded self-interest. These topics include the influence of altruism, trust, and emotion in economic decisions and alternative explanations for ’irrational decisions’: choice anomalies, bias in risk attitudes, and heuristics. Students participate in and develop controlled experiments to examine these issues empirically.

This course introduces students to the theory and practice of laboratory methods in economics. The course explores and identifies the range of issues in economics to which experimental methods have been applied. In addition, the course focuses on the principles of experimental design, as applied to these issues. Along the way, students participate in a range of classroom experiments which illustrate key ideas.

This second course in econometrics explores more advanced techniques for addressing empirical questions in the social sciences. The course emphasizes applied methods for both observational and quasi-experimental data. Students develop an independent empirical research project applying the skills they have acquired.

This course covers the fundamentals of conducting statistical analyses, with particular emphasis on regression analysis and linear models. Students learn to use sophisticated computer software as a tool to analyze and interpret data.

This course covers advanced methods in applied statistics, beyond those of MATH 260. The analyses will be conducted using R, so students entering the course should already have a working knowledge of R. Topics may include generalized linear models, Bayesian statistics, time series analysis, categorical data analysis, and/or statistical graphics.

This course provides an introduction to the standard topics of probability theory, including probability spaces, random variables and expectations, discrete and continuous distributions, generating functions, independence, sampling distributions, laws of large numbers, and the central limit theorem. The course emphasizes modeling real-world phenomena throughout.

This course focuses on social, economic, legal, and ethical issues that arise from the collection, analysis, and use of large data sets, especially when these processes are automated or embedded within artificial intelligence systems. The course explores the design of ethical algorithms by considering questions like the following: what kinds of biases are ethically problematic and how can they be avoided? what are the effects of automation on jobs and inequality? what are the privacy considerations that arise when collecting and using data? what is the ethical significance of transparency in automation? who owns data sets and who has the right to access information? who is responsible for actions that result from artificial intelligence systems? In thinking about these complex questions, students consider specific case studies of controversial uses of data and algorithms in fields such as medicine, biotechnology, military, advertising, social media, finance, transportation, and criminal justice, among others. In addition to relevant ethical theories, students are introduced to philosophical, legal, and scientific theories that play a central role in debates regarding the ethics of data and artificial intelligence. Readings are drawn from a number of classic and contemporary texts in philosophy, science and technology studies, law, public policy, and the emerging fields of "data ethics" and "robot ethics".

This course covers experimental and quasi-experimental design, the design of social surveys, and techniques of data analysis appropriate for each type of design. Individual student research projects are required.