In addition to the courses below, I occasionally teach courses in the sociology of science, science and technology policy, social networks, and creativity and innovation.

INTEG 440: “Big Data” and Social Science

We are living in an age where digital information is being produced at an unprecedented rate. This explosion of digital data has the potential to revolutionize the way we learn about the world, and how we conduct research related to urgent social and political problems. This course focuses primarily on the knowledge and skills necessary for doing high-quality research with digital data. The course is divided into four core sections: (1) a research-oriented introduction to the programming language Python, (2) collecting, cleaning, and combining digital datasets, (3) analyzing digital datasets using tools from machine learning, text analysis, and social network analysis, and (4) privacy and confidentiality. There will be an emphasis on good research design throughout the course. Previous courses in research methods and / or statistics are an asset, but are not required. I assume no previous knowledge or experience of computer programming.

Course Syllabus

INTEG 340: Research Design & Methods

This course provides an introduction to empirical research design and methods with a focus on applications in the social sciences and related fields. You will learn about core issues in research design (e.g. sampling) that transcend specific approaches, and about a variety of techniques for collecting and analyzing quantitative and qualitative data. The course will cover both abstract and practical issues related to methodology and decision making in empirical research. You will have the opportunity to develop and workshop proposals that you may wish to pursue in your senior honors thesis. By the end of the course you will be a more informed consumer and have a basic set of skills for designing and implementing your own empirical research projects. Most importantly, you will have a foundation for future learning about research design and methods.

Course Syllabus

INTEG 120: The Art & Science of Learning

There are three sets of related learning objectives in this course. First, you will be introduced to state-of-the-art scientific research on learning and cognitive adaptability, and you will begin using this research to become more intentional learners and knowledge integrators. Second, you will learn about how the mind processes information, and will begin developing new habits to facilitate clear and critical thinking in complex, messy, and stressful circumstances. Finally, you will learn how to see the social networks you are embedded in, and how they shape what you think, know, and do. You will use this knowledge to identify opportunities and develop strategies for learning, and for the integration and synthesis of different types of knowledge.

INTEG 420 A/B: Senior Honours Project

The Senior Research Project is the two-term culmination of the Knowledge Integration degree. It provides you with the opportunity to engage in an original research or design project supervised by an expert (or experts) in your chosen field. Traditionally, INTEG 420 A (taken in the fall) is focused on designing your project, conducting literature reviews, applying for ethics clearance, and making serious progress on any empirical research (if your project requires empirical work). INTEG 420 B (taken in the winter) is focused on actually writing the actual thesis and disseminating the work through a conference paper and poster.

INTEG 441: Communicating with Data

Visualizing data is central to scientific research, and is increasingly prominent in science communication and journalism. This course provides an introduction to the history, principles, and techniques of data visualization and information design, with an emphasis on data used by social scientists, data scientists, and policy analysts. Students will learn how to create effective visualizations for a variety of different audiences and purposes, including making sense of new datasets and communicating evidence to others.

I assume that students in this class will have a minimal background in research methods (i.e. one undergraduate research methods class), but little to no programming experience. While some programming experience is advantageous, you will acquire the necessary skills in this course.

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