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This course emphasizes practical techniques for working with large-scale data and introduces tools and techniques for managing, visualizing, and making sense of data through the use of statistical software. Topics include: descriptive statistics, confidence intervals, regression, and machine learning.

This course will build on concepts established in MATH 1311 to cover accurate and effective visualization and communication of data to both technically trained audiences and to the wider public. Students will learn to organize diverse data types for efficient static, dynamic, and interactive visual presentations to effectively communicate key messages in multiple formats. Students will learn approaches to high throughput report generation and content updating, with principles of open data and maintaining audit trails from presentation back to source. The course will cover common pitfalls or distortions of data presentation, and principles of visual grammar and accessibility for diverse users.

This course will build on MATH 1311 to cover high throughput acquisition and management of data. The course will use diverse data types and formats to illustrate conceptual challenges across disciplines. Technical aspects will include evolving approaches to script-based web scraping, file formats and conversions, data mergers and tidying, meta-data organization and capture. In parallel, the course will cover theoretical and ethical aspects of data curation and access policies, development of best practices for research data management and case studies in secure management of sensitive or private data.(

This capstone course will give upper-year students the opportunity to work on data of interest to them, captured either through direct measurement or through access to open data sets, in order to develop management and analysis competencies applicable to their disciplinary area(s). In this integrated seminar and laboratory, students will regularly present their individual works in progress and discuss specialized advanced topics in data science, as relevant both to broad experiential applications, and to their specific projects. The course will allow students to deeply engage with a self-determined data-handling project within their discipline, cultivate a transferrable skill set, and work in an interdisciplinary, collaborative environment to experience a breadth of different applications and perspectives on data science.

This course develops basic skills in data collection, analysis, and presentation. It introduces basic statistical and hypothesis testing procedures, along with relevant software.

This course introduces statistical tools for handling data generated in uncontrolled environments and the techniques involved in their use. Topics include estimation and inference of single- and multi-variable regression models, large sample techniques, dummy variables, heteroskedasticity, and an introduction to times series.

This course provides a broad survey of computer science and an introduction to programming. Topics include: origins of computers, data representation and storage, Boolean algebra, digital logic gates, computer architecture, assemblers and compilers, operating systems, networks and the Internet, theories of computation, and artificial intelligence.

This is a second course in the concepts and techniques of probability and statistics. The course covers a selection of topics from analysis of variance, linear and nonlinear regression, correlation estimation and prediction, independence, Wilcoxon and goodness-of-fit tests and includes data analysis using statistical software. Examples come from a wide variety of sources and disciplines.