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Applied Computation Courses
Kevin A. Rader,
Dr Margo Levine,
Data Science 1 is the first half of a one-year introduction to data science. The course will focus on the analysis of messy, real life data to perform predictions using statistical and machine learning methods. Material covered will integrate the five key facets of an investigation using data: (1) data collection - data wrangling, cleaning, and sampling to get a suitable data set; (2) data management - accessing data quickly and reliably; (3) exploratory data analysis – generating hypotheses and building intuition; (4) prediction or statistical learning; and (5) communication – summarizing results through visualization, stories, and interpretable summaries. Part one of a two part series. The curriculum for this course builds throughout the academic year. Students are strongly encouraged to enroll in both the fall and spring course within the same academic year. Part one of a two part series.
Data Science 2 is the second half of a one-year introduction to data science. Building upon the material in Data Science 1, the course introduces advanced methods for data wrangling, data visualization, and statistical modeling and prediction. Topics include big data and database management, interactive visualizations, nonlinear statistical models, and deep learning. Part two of a two part series. The curriculum for this course builds throughout the academic year. Students are strongly encouraged to enroll in both the fall and spring course within the same academic year. Part two of a two part series.
In this course, we shall familiarize with the main computational methods which permit to simulate and analyze the behavior of a wide range of problems involving fluids, solids, soft matter, electromagnetic and quantum systems, as well as the dynamics of (some) biological and social systems. Special attention will be paid to the modelling/programming techniques involved in the simulation of complex systems, as well as to methods to analyze simulation data and extract knowledge therefrom.
This course will teach theoretical background and practical applications of modern computational methods used to understand and design properties of advanced functional materials. Topics will include classical potentials and quantum first-principles energy models, density functional theory methods, Monte Carlo sampling and molecular dynamics simulations of phase transitions and free energies, fluctuations and transport properties, and machine learning approaches. Examples will be based on rational design of industrially relevant materials for energy conversion and storage, electronic and magnetic devices, and nanotechnology.
The CSE capstone project is intended to integrate and apply the skills and ideas CSE students acquire in their core courses and electives. By requiring students to complete a substantial and challenging collaborative project, the capstone course will prepare students for the professional world and ensure that they are trained to conduct research. There will be no homework or lectures. Students will be dealing with real-world problems, messy data sets, and the chance to work on an end-to-end solution to a problem using computational methods.
This course, centered on the Institute for Applied Computation Science (IACS) seminar series, will provide broad exposure to cutting-edge topics, applications, and unifying concepts in Computational Science & Engineering. Students will read, present and discuss journal articles related to IACS talks, attend the seminars and meet with visiting speakers. Possible topics to be covered include scientific visualization, computational approaches to disease, mathematical neuroscience, computational archeology, and computational finance.