An introduction to software design and development concepts, methods, and tools using a statically-typed object-oriented programming language such as Java. Topics from: version control, unit testing, refactoring, object-oriented design and development, design patterns, advanced IDE usage, regular expressions, and reflection. Representation of floating-point numbers and introduction to numerical computation.

The application of logic and proof techniques to Computer Science. Mathematical induction; correctness proofs for iterative and recursive algorithms; recurrence equations and their solutions; introduction to automata and formal languages.

Systems of linear equations, matrix algebra, real vector spaces, subspaces, span, linear dependence and independence, bases, rank, inner products, orthogonality, orthogonal complements, Gram-Schmidt, linear transformations, determinants, Cramer's rule, eigenvalues, eigenvectors, eigenspaces, diagonalization.

An introduction to probability using simulation and mathematical frameworks, with emphasis on the probability needed for computer science applications and more advanced study in statistical practice. Topics covered include probability spaces, random variables, discrete and continuous probability distributions, probability mass, density, and distribution functions, expectation and variance, independence, conditional probability, the law of large numbers, the central limit theorem, sampling distributions. Computer simulation will be taught and used extensively for calculations and to guide the theoretical development.

Introduction to digital mapping and spatial analysis using geographic information systems (GIS). Students learn how to use GIS software to find, edit, analyze and map geographic data to create their own maps, analyze geographic problems and use techniques that can be applied to a variety of subject areas.

An introduction to the field of computer science combining the tools and techniques of programming (using the Python programming language) with rigorous mathematical analysis and reasoning. Topics include: data representations; program control flow (conditionals, loops, exceptions, functions); mathematical logic and formal proof; algorithms and running time analysis; software engineering principles (formal specification and design, testing and verification). Prior programming experience is not required to succeed in this course.

A continuation of CSC110Y1 to extend principles of programming and mathematical analysis to further topics in computer science. Topics include: object-oriented programming (design principles, encapsulation, composition and inheritance); binary representation of numbers; recursion and mathematical induction; abstract data types and data structures (stacks, queues, linked lists, trees, graphs); the limitations of computation.

A conceptual approach for students with a serious interest in mathematics. Attention is given to computational aspects as well as theoretical foundations and problem solving techniques. Review of Trigonometry. Limits and continuity, mean value theorem, inverse function theorem, differentiation, integration, fundamental theorem of calculus, elementary transcendental functions, Taylor's theorem, sequence and series, power series. Applications.

Innovation has always been a key driver of economic growth, population health, and societal success. Transformative change has historically been linked to major innovations such as urban sanitation, pasteurization, the printing press and the industrial revolution. Currently, the opportunity to enhance life chances worldwide relies on innovating for the poor, social innovation, and the ability to harness scientific and technological knowledge. What precisely is innovation? When does innovation happen? Who benefits from innovation? How can innovation be fostered, and how do innovations spread? Relying on major global transformations and country-specific case studies (for example, South Korea, Taiwan, Israel and India), this course examines the drivers of innovation, the political, social, economic, and scientific and technological factors that are critical to promoting innovation and addressing current global challenges, and the consequences of innovation.

Governing public goods has been an age-old concern for social scientists and policymakers alike. This is not surprising since the provision of global public goods is riddled by problems of collective action. In this course, we focus on how to implement solutions through states, markets and communities. The first objective is to familiarize students with the concept of global public goods, the different mechanisms that can provide these goods and the challenges that emerge from lacking incentives to secure their provision. To this end, the course will introduce theories from sociology, political science, philosophy, and history to help us understand different types of governance mechanisms and how they may be used to scale global solutions. Theories can help us explain the tensions between cooperating for the public good at the expense of sacrificing individual goals, or why certain areas of our lives, like the Internet, seem to produce public goods without any formal mechanism of cooperation. The second objective is to use the class and subject of study as an arena to model and practice the kind of learning that is expected of university students. The main skills that the course will help students target and develop are: research (finding, evaluating and assimilating reliable information); writing (developing ideas into logically written arguments); and critical analysis of arguments presented in the readings and debated in class (this includes identifying the key assumptions that are implicit in different theories as well as inherent in our own positions on various questions related to governance).

This course teaches students how to conduct analytically rigorous social science research to improve their insights into complex global problems and devise innovative solutions to address them. A unique feature of this class is that students have the opportunity to learn by doing. Students work hands-on in one of several labs dealing with some of the most intractable global problems of our time in the areas of the environment, health, digital governance, security and the gap between rich and poor. By the end of this course students will be able to: (1) Experience and practice the process of social science research, (2) Narrow the scope of a complex global challenge into a manageable research problem that can be tackled over the course of the semester, (3) Improve their knowledge and insights on a particular global problem, (4) Develop a viable, compelling and implementable solution that can help address an aspect of a global problem, (5) Pitch a solution in a compelling way to a jury of experts in global affairs. Students will work with their peers in small seminar classes and lab group settings, mentored by graduate teaching assistants specializing in global affairs, expert faculty and senior policy advisors dedicated to addressing global issues in the fields of the environment, health, security, digital governance and political economy.

A brief introductory survey of psychology as both a biological and social science. Topics will include physiological, learning, perceptual, motivational, cognitive, developmental, personality, abnormal, and social psychology.