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Course Listing

Systems Development for Computational Science

APCOMP 207
2022 Fall

Fabian Wermelinger
Tuesday, Thursday
2:15pm to 3:30pm

This is a project-based course emphasizing designing, building, testing, maintaining and modifying software for scientific computing and data sciences. The class is focusing on a thorough introduction of the Python programming language with discussion of core concepts in object oriented programming as well as essential data structures useful in most programming tasks. Students will work in groups on a semester long project that combines the subjects discussed in class and applies them to the topic of automatic differentiation. Students will further learn how to work with SQL databases and how to integrate them in Python using SQLite3 and Pandas. After completion of this course, students will be able to adapt basic tools and techniques to design complex software systems aimed at solving computational and data processing problems in academic and industrial environments.

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Data Science 1: Introduction to Data Science

APCOMP 209A
2022 Fall

Pavlos Protopapas, Natesh Pillai
Monday, Wednesday, Friday
9:45am to 11:00am

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.

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Data Science 2: Advanced Topics in Data Science

APCOMP 209B
2023 Spring

Pavlos Protopapas, Mark Glickman
Monday, Wednesday, Friday
9:45am to 11:00am

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 statistical modeling, representation, and prediction. Topics include multiple deep learning architectures such as CNNs, RNNs, transformers, language models, autoencoders, and generative models as well as basic Bayesian methods, and unsupervised learning. 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.

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Critical Thinking in Data Science

APCOMP 221
2023 Spring

Michael Smith
Tuesday, Thursday
9:45am to 11:00am

This course examines the wide-ranging impact data science has on the world and how to think critically about issues of fairness, privacy, ethics, and bias while building algorithms and predictive models that get deployed in the form of products, policy and scientific research. Topics will include algorithmic accountability and discriminatory algorithms, black box algorithms, data privacy and security, ethical frameworks; and experimental and product design. We will work through case studies in a variety of contexts including media, tech and sharing economy platforms; medicine and public health; data science for social good, and politics. We will look at the underlying machine learning algorithms, statistical models, code and data. Threads of history, philosophy, business models and strategy; and regulatory and policy issues will be woven throughout the course.

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Computational Design of Materials

APCOMP 275
2023 Spring

Boris Kozinsky
Tuesday, Thursday
10:30am to 11:45am

This course covers theoretical background and practical hands-on applications of modern computational atomistic methods used to understand and design properties of advanced functional materials. Topics include classical interatomic potentials and machine learning methods, quantum first-principles electronic structure models based on wave functions and density functional theory, Monte Carlo sampling and molecular dynamics simulations of phase transitions and free energies, fluctuations and transport properties. Applications include atomistic and electronic effects in materials for energy conversion and storage, catalysis, alloys, polymers, and low-dimensional materials.

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Computational Science and Engineering Capstone Project

APCOMP 297R
2022 Fall

Weiwei Pan
Wednesday
12:45pm to 3:30pm

The capstone course is intended to provide students with an opportunity to work in groups of 3-4 on a real-world project. Students will develop novel ideas while applying and enhancing skills they have acquired from 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 additional homework. There will be several mini-lectures, focusing on supplemental skills such as technical writing, public speaking, reading research papers, using version control software, identifying biases, etc. Since the projects concern real-world projects, datasets will likely be messy, and there is a focus on effectively communicating your progress to both the staff and partner organization.

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Computational Science and Engineering Capstone Project

APCOMP 297R
2023 Spring

Pavlos Protopapas
Wednesday
12:45pm to 3:30pm

The capstone course is intended to provide students with an opportunity to work in groups of 3-4 on a real-world project. Students will develop novel ideas while applying and enhancing skills they have acquired from 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 additional homework. There will be several mini-lectures, focusing on supplemental skills such as technical writing, public speaking, reading research papers, using version control software, identifying biases, etc. Since the projects concern real-world projects, datasets will likely be messy, and there is a focus on effectively communicating your progress to both the staff and partner organization.

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Interdisciplinary Seminar in Applied Computation

APCOMP 298R
2022 Fall

Weiwei Pan
Friday
2:15pm to 3:30pm

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.

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Interdisciplinary Seminar in Applied Computation

APCOMP 298R
2023 Spring

Daniel Weinstock
Wednesday, Friday
2:15pm to 3:30pm

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.

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Special Topics in Applied Computation

APCOMP 299R
2022 Fall

Daniel Weinstock

Supervision of experimental or theoretical research on acceptable applied computation problems and supervision of reading on topics not covered by regular courses of instruction.

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Special Topics in Applied Computation

APCOMP 299R
2023 Spring

Daniel Weinstock

Supervision of experimental or theoretical research on acceptable applied computation problems and supervision of reading on topics not covered by regular courses of instruction.

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