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3.88
Fall 2025
This course covers the practice of data science practice, 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.
3.88
1.63
3.89
Fall 2025
Exposes students to the emerging field of Data Science, its domain areas, and popular applications. Topics include analytical methods, ethical issues associated with the field, engineering, and systems necessary to support data-related work, and design principles commonly seen in data communications and human center design. Students learn from leaders in the field through a series of guest lectures and work through discussion examples.
4.10
1.60
3.89
Fall 2025
Will expose student to fundamental coding languages in data science. Python and R will be the primary focus of the course. Popular packages such as pandas and tidyverse will be covered in depth. Additionally, project management skills such as Git and Github will be covered.
4.83
4.00
3.89
Fall 2025
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 foundation knowledge in terms of computational environments. The content will lay the groundwork for more advanced Systems Domain courses in the major.
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3.89
Spring 2025
In this course, students will learn how to trace the "causal chains" from such actions/inactions to various ecosystem, social, and economic outcomes and to measure and value those outcomes. We will consider the philosophical/ethical underpinnings of the Ecosystem Services framework, use computer mapping and other software tools for evaluation, and review current applications of the framework by private and public sector entities.
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3.89
Spring 2025
Fundamentals of data mining and machine learning within a common statistical framework. Topics include boosting, ensembles, Support Vector Machines, model-based clustering, forecasting, neural networks, recommender systems, market basket analysis, and network centrality.
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3.90
Fall 2025
Learning tools and concepts for computing on big data. Learn how to use Spark for large-scale analytics and machine learning. Spark is an open-source, general-purpose computing framework that is scalable and blazingly fast. Fundamental data types and concepts will be covered (e.g., resilient distributed datasets, DataFrames) along with Tools for data processing, storage, and retrieval, including Amazon Web Services (AWS).
4.50
1.00
3.90
Fall 2025
This course introduces students to key business topics relevant to high technology companies. Students will learn how to understand and interpret financial statements and frame financial decisions, including building a business case. The course will explore typical organizational structures and the roles of business functions. Students will be introduced to business models and other concepts in marketing and business strategy.
4.22
3.00
3.91
Fall 2025
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.
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3.91
Fall 2025
Each student or small group will develop a project, be matched with a Global Studies faculty mentor, identify relevant community groups, and spend the semester working on that project. Students will discuss ideas, formulate plans, identify tactics, and engage with important social justice literatures. Importantly, the course will engage with the project of activism itself, which has the potential to replicate systems of inequality.
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