In this class, students will learn foundations of practical machine learning: upon completion, students will be able to understand and apply supervised and unsupervised learning in Python to build predictive models on real world datasets. Techniques covered include (but not limited to): preprocessing, dimensionality reduction, clustering, feature engineering and model evaluation. Models covered include: generalized linear models, tree-based models, bayesian models, support vector machines, and neural networks. All learning objectives are achieved through active application of these techniques to real world datasets.

Combines theory and practical information for students to learn traditional and contemporary organizational and leadership theories and apply them to their experience in organizations particularly non-profit institutions, non-governmental organizations. The course will focus on personal and inter-personal development such as self-awareness, stress and problem solving, interpersonal skills such as supportive communication, power and influence, motivation and conflict management: group skills such as delegation and team building; and leadership.

During the first semester, students will identify a community problem or issue, research and analyze the issue in consultation with colleagues and community stakeholders, and design a project. A preliminary design will be submitted at the end of the first semester.

This course explores the idea and ideals of entrepreneurship, the creating of value through individual initiative, creativity and innovation. The idea of entrepreneurship is of significant relevance in the highly dynamic and competitive 21st century global economy. It is an idea that is important for students to understand and to critically consider and apply. Encourages the development of multiple views of entrepreneurship, and uses a broad, not just business, approach to the study as it engages students in the subject matter. Open to all majors.