Covers the practice of data science, including communication, exploratory data analysis, and visualization. Also covered are the selection of algorithms to suit the problem to be solved, user needs, and data. Case studies will explore the impact of data science across different domains.

This course takes an ethnographically informed approach to the question of how to understand corruption by examining practices of and perspectives on corruption from across the globe - including the so-called Global North. It aims to encourage students to 1) critically assess assumptions at the heart of international anti-corruption discourses; 2) examine tensions between global discourses of corruption and local practices; 3) compare and contrast corruption between different localities.

Introduction to core data science concepts and skills, including computing environments, visualization, modeling, and bias analysis. Think like a Data Scientist as you engage through lectures, discussions, labs, and guest talks while applying learning in a guided semester-long project. Concludes with an independent project to reinforce and extend skills.

Students will learn the fundamentals of product management. Topics include identifying unmet needs, understanding markets, implementing product development frameworks and processes, building businesses, and working with multi-functional teams. The application of these concepts to different phases of the product lifecycle will be explored. Students will build technical, professional, and soft skills necessary for success in product management. Prerequisite: EBUS 1800 and enrolled in the Engineering Business Minor or Entrepreneurship Minor - Tech Concentration and 3rd or 4th year standing

Comprehensive introduction to predictive modeling, a cornerstone of data science and machine learning. Learn the fundamental concepts, techniques, and tools used to build models while emphasizing both theoretical understanding and practical applications. The topics include we will cover are an in-depth analysis of linear models and different variants, their extension to generalized linear models, and an introduction to nonparametric regression.

This course will center on exposing students to contemporary pipelines for data analysis through a series of steadily escalating use cases. The course will begin with simple local database construction such as SQLite and evolve to cloud base systems such as AWS or Google Cloud. This progression will include topics such as data lakes and other non-SQL applications as appropriate.

This course exposes students to foundational knowledge in each of the four high level domain areas of data science (Value, Design, Analytics, Systems). This includes an emphasis on ethical issues surrounding the field of data science and how these issues originate and extend into society more broadly.

The data science project course will allow students to take the knowledge gained in each of the four required courses and apply them to a data driven problem. Students will work in groups and can either choose a project provided by SDS faculty or can propose a project for approval. Upon completion of the course students will be required to present their results and publish project content to an open forum.

This course covers social, economic, political, and cultural dimensions of inequality both within and between countries. We will discuss how systems like slavery, colonialism, and capitalism have entrenched unequal power relations across the globe; how structures of inequality are produced, legitimated, and reproduced at national and international scales; and how individuals experience and negotiate these structures.

Critique models and adapt them to a variety of data sets. Gain a deeper understanding of core ML concepts. Build towards neural networks (latent index models, more complex linear models with non-linear transformations of the data). Compare new methods to kNN, clustering, linear models from ML1 to discuss performance differences as complex and predictive power increases. How mathematical concepts are present in the models presented.