Note: For students in traditional computing majors (ECE/CS), this elective can be satisfied by any course with a significant programming component that is used to satisfy their major requirement. The list of courses below are examples of what are most relevant to this track.

For students with little or no prior programming experience or computational background, the Elective Area 1 can be satisfied with:

• Programming and Problem Solving (COMPSCI 94): Programming and problem solving in a specific domain such as robotics, virtual worlds, web programming, biology, genomics, or computer science. Students learn the basics of programming by studying problems in one application area. Not open to students who have taken Computer Science 101, 102, 116, Engineering 103 or Computer Science 201.
• Introduction to Computer Science (COMPSCI 101L): Introduction practices and principles of computer science and programming and their impact on and potential to change the world. Algorithmic, problem-solving, and programming techniques in domains such as art, data visualization, mathematics, natural and social sciences. Programming using high-level languages and design techniques emphasizing abstraction, encapsulation, and problem decomposition. Design, implementation, testing, and analysis of algorithms and programs. No previous programming experience required. Not open to students who have taken Computer Science 102, 116, Engineering 103 or Computer Science 201.
• Foundations of Data Science (COMPSCI 116): Introduction to computer programming and statistical inference in the process of conducting analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. Exploration of data via visualization and descriptive statistics. Creating predictions with techniques from machine learning and optimization. Testing hypotheses and making statistical inferences. Learn basic Python programming skills to organize and manipulate data in tables, and to visualize data effectively. Discussion of social issues surrounding data analysis such as privacy and bias. No prior programming experience or statistics is required.
• Discrete Math for COMPSCI (COMPSCI 230): Mathematical notations, logic, and proof; linear and matrix algebra; graphs, digraphs, trees, representations, and algorithms; counting, permutations, combinations, discrete probability, Markov models; advanced topics from algebraic structures, geometric structures, combinatorial optimization, number theory. Pre/corequisite: Computer Science 201.
• Fundamentals of Electrical and Computer Engineering (ECE 110L): Students learn core ECE concepts, providing a foundation on which subsequent courses build. Concepts include techniques for analyzing linear circuits, semiconductor and photonic devices, frequency representation, filtering, combinational and sequential logic. Central to the course is an extensive design challenge that requires students to integrate knowledge across topics while honing practical design and project management skills. Course culminates in an exciting competition in which teams of robots race to overcome challenging obstacles using sensor data acquisition and processing. Prerequisite/corequisite: (Engineering 103L or Computer Science 201) and (Mathematics 112L or 22 or equivalent).
• Fundamentals of Econometrics & Data Science (ECON 104D): Rigorous introduction to statistical concepts that underpin econometrics. Course emphasizes conceptual understanding, uses mathematics to illustrate ideas, and applies ideas to examples from economics broadly construed. Students analyze data to reinforce understanding. Topics include experimental and non-experimental research designs; modern approaches to summarizing data; random variables, probability, expectations, density and distribution functions; sampling; estimation; inference and hypothesis testing; introduction to linear regression. First course in two-semester econometrics sequence. Prerequisite: Mathematics 21, 106L,111L, 112L, 121, 122, 122L, 202, 202D, 212, or 222.
• Computational Methods in Engineering (EGR 103L): Introduction to computer methods and algorithms for analysis and solution of engineering problems using numerical methods in a workstation environment. Topics include; numerical integration, roots of equations, simultaneous equation solving, finite difference methods, matrix analysis, linear programming, dynamic programming, and heuristic solutions used in engineering practice. This course does not require any prior knowledge of computer programming.
• Microelect Devices & Circuits (EGR 230L): Partial differentiation, multiple integrals, and topics in differential and integral vector calculus, including Green’s theorem, the divergence theorem, and Stokes’s theorem. Intended for students who have had a course in linear algebra. Not open to students who have taken Mathematics 202, 212, or 222. Prerequisite: Mathematics 218-2, 216, 218, or 221.
• Fields and Waves (EGR 270DL): Introduces the concept of fields – a mathematical description of physical quantities that vary from place to place (and potentially from time to time) – and explores the mathematical & physical reasons that oscillatory behavior is so ubiquitous across engineering and physics. Introduces mathematical foundations, followed by specific examples in electrical circuits, electromagnetic waves, quantum mechanics, & acoustics as well as the connection to Fourier analysis methods. Intended to facilitate subsequent study in any area involving wave phenomena, including analog & microwave circuits, electromagnetics, optics, & quantum mechanics. Prerequisite: Physics 152L, Math 212 or 219 or 222, & ECE 110L.
• Focus Program: Mathematics of Data Science (MATH 163FS)†: Introduction to the mathematics and algorithms that are central to a variety of data science applications. Basic mathematical concepts underlying popular data science algorithms will be introduced and students will write code implementing these algorithms. We will discuss the impact of these algorithms on society and ethical implications. Algorithms examined include: Google’s pagerank, principal component analysis for visualizing high dimensional data, hidden Markov models for speech recognition, and classifiers detecting spam emails. Linear algebra and basic probability will be the mathematical focus and there will be a programming component to this class using the R programming language. Open only to students in the Focus Program.
• Probability (MATH 230): Probability models, random variables with discrete and continuous distributions. Independence, joint distributions, conditional distributions. Expectations, functions of random variables, central limit theorem. Prerequisite: Calculus II (Mathematics 22, 112L, 122, or 122L) OR credit for multivariable calculus (Mathematics 202, 212, 219, or 222) OR graduate student standing. Not open to students who have credit for Mathematics 340.
• Introduction to Applied Mathematics: Modeling, Equations and Proofs (MATH 240): The course will consist of 3 or 4 concrete applications, for which precise mathematical questions will be formulated, and a mathematical framework developed that will make it possible to answer these questions. In doing so, we will encounter and explore portions of real analysis, probability, linear algebra, convex analysis, information theory and maybe others. We will also learn how to construct watertight mathematical arguments and explore different proof techniques. Prerequisites: none, beyond high school calculus.
• Logic (PHIL 150): The conditions of effective thinking and clear communication. Examination of the basic principles of deductive reasoning.
• Logic (PHIL 150): The conditions of effective thinking and clear communication. Examination of the basic principles of deductive reasoning.
• Symbolic Logic (PHIL 250): Detailed analysis of deduction and of deductive systems. Open to sophomores by consent of instructor.
• Logic and Its Applications (PHIL 350): Topics in proof theory, model theory, and recursion theory; applications to computer science, formal linguistics, mathematics, and philosophy. Usually taught jointly by faculty members from the departments of computer science, mathematics, and philosophy. Prerequisite: a course in logic or consent of instructor.
• Data Analysis and Statistics Inferences (STA 101L): Introduction to statistics as a science of understanding and analyzing data. Themes include data collection, exploratory analysis, inference, and modeling. Focus on principles underlying quantitative research in social sciences, humanities, and public policy. Research projects teach the process of scientific discovery and synthesis and critical evaluation of research and statistical arguments. Readings give perspective on why in 1950, S. Wilks said, ‘Statistical thinking will one day be as necessary a qualification for efficient citizenship as the ability to read and write.’ See department website for placement information. Not open to students who have taken Statistical Science 100 or above.
• Introduction to Data Science and Statistical Thinking (STA 199): Learn to explore, visualize, and analyze data to understand natural phenomena, investigate patterns, model outcomes, and make predictions, and do so in a reproducible and shareable manner. Gain experience in data wrangling and munging, exploratory data analysis, predictive modeling, and data visualization, and effective communication of results. Work on problems and case studies inspired by and based on real-world questions and data. The course will focus on the R statistical computing language. No statistical or computing background is necessary. Not open to students who have taken a 100-level Statistical Science course, Statistical Science 210, or a Statistical Science course numbered 300 or above.

For students with some programming experience and computational background (which can be obtained from the courses above or through co-curricular offerings):

• Data Structures and Algorithms (COMPSCI 201): Analysis, use, and design of data structures and algorithms using an object-oriented language like Java to solve computational problems. Emphasis on abstraction including interfaces and abstract data types for lists, trees, sets, tables/maps, and graphs. Implementation and evaluation of programming techniques including recursion. Intuitive and rigorous analysis of algorithms. Prerequisite: Computer Science 101 or Engineering 103L, or equivalent.
• Everything Data (COMPSCI 216): Study of data and its acquisition, integration, querying, analysis, and visualization. Concepts and computational tools for working with unstructured, semi-structured, and structured data and databases. Interdisciplinary perspectives of data and its impact crossing science, humanities, policy, and social science. Culminating team project applied to real datasets. Prerequisite: 200-level computer science OR 100-level Statistics OR 200-level Math course, or permission of instructor.
• Python Programming in Mathematics (MATH 260): Introductory programming course in Python providing a foundational background for programming in a mathematical setting. Students will learn the basics of object orientated programming: memory storage and variable scoping, recursion, objects and classes, and basic data structures. A variety of numerical methods will be introduced, with a focus on their practical implementation, through a series of practice modules covering subjects that may include: linear algebra, machine learning, operations research, and genetics. Recommended prerequisite: linear algebra (Mathematics 216, 218, or 221). No programming background is required. Not open to students who have taken Computer Science 201.
• Fundamentals of Data Analysis and Decision Science (EGR/Math 238L): This course provides a mathematically rigorous and broad foundation for key concepts in probability and statistics, as well as the application of probability and statistics to the mathematical modeling of non-deterministic systems. The main motivation of the course is to show how these concepts are fundamental to a variety of current data analysis techniques, and to demonstrate applications of these techniques in situations relevant to all engineering majors. Prerequisite: (Mathematics 216, 218, or 221) and (Engineering 103L, Computer Science 101L, Computer Science 201, or Mathematics 218L.)

Students with more advanced computational backgrounds will be able to take more advanced computational thinking electives, including:

• Signals and Systems (BME 271D): Convolution, deconvolution, Fourier series, Fourier transform, sampling, and the Laplace transform. Continuous and discrete formulations with emphasis on computational and simulation aspects and selected biomedical examples. Prerequisite: Biomedical Engineering 253L or Electrical & Computer Engineering 110L and Mathematics 216, 218, 221, or 356.
• Introduction to Computer Systems (COMPSCI 210D): This course provides a programmer’s view of how computer systems execute programs and store information. It examines key computational abstraction levels below modern high-level languages; introduction to C, number and data representations, computer memory, assembly language, memory management, the operating-system process model, high-level machine architecture including the memory hierarchy, and introduction to concurrency. Prerequisite: Computer Science 201. Not open to students who have taken Computer Science 250D.
• Computer Architecture (COMPSCI/ECE 250D): Computer structure, assembly language, instruction execution, addressing techniques, and digital representation of data. Computer system organization, logic design, microprogramming, cache and memory systems, and input/output interfaces. Prerequisite: Computer Science 201.
• Software Design and Implementation (COMPSCI 307/308): Techniques for design and construction of reliable, maintainable, and useful software systems development in teams. Programming paradigms and tools for small to medium projects: revision control, GUI, software engineering, testing, documentation. Prerequisite: Computer Science 201.
• Introduction to Operating Systems (COMPSCI 310/ECE 353): Basic concepts and principles of multiprogrammed operating systems. Processes, interprocess communication, CPU scheduling, mutual exclusion, deadlocks, memory management, I/O devices, file systems, protection mechanisms. Prerequisites: Computer Science 201 and Computer Science / Electrical and Computer Engineering 250D.
• Introduction to Database Systems (COMPSCI 316): Databases and relational database management systems. Data modeling, database design theory, data definition and manipulation languages, storaging and indexing techniques, query processing and optimization, concurrency control and recovery, database programming interfaces. Current research issues including XML, web data management, data integration and dissemination, data mining. Hands-on programming projects and a term project. Prerequisite: Computer Science 201.
• Introduction to the Design and Analysis of Algorithms (COMPSCI 330): Design and analysis of efficient algorithms including sorting, searching, dynamic programming, graph algorithms, fast multiplication, and others; nondeterministic algorithms and computationally hard problems. Prerequisites: Computer Science 201 and 230.
• Mathematical Foundations of Computer Science (COMPSCI 334): An introduction to theoretical computer science including studies of abstract machines, the language hierarchy from regular sets to recursively enumerable sets, noncomputability, and complexity theory. Prerequisites: Computer Science 201 and 230.
• Computer Network Architecture (COMPSCI 356): Introduces students to the fundamentals of computer networks. Focus on layered architecture of the network protocol stack. Case studies drawn from the Internet, combined with practical programming exercises. Concepts include the Internet architecture, HTTP, DNS, P2P, Sockets, TCP/IP, BGP, routing protocols, and wireless/mobile networking and their applications such as how to achieve reliable/secure communications over channels, how to find a good path through a network, how to share network resources among competing entities, how to find an object in the network, and how to build network applications. Prerequisite: CompSci/ECE 250D or CompSci 210D.
• Introduction to Artificial Intelligence (COMPSCI 370): Algorithms and representations used in artificial intelligence. Introduction and implementation of algorithms for search, planning, decision, theory, logic, Bayesian networks, robotics, and machine learning. Prerequisite: Computer Science 201 and one of the following: Computer Science 230, 200-level Mathematics course, or 200-level Statistical Science course.
• Elements of Machine Learning (COMPSCI 371D): Fundamental concepts of supervised machine learning, with sample algorithms and applications. Focuses on how to think about machine learning problems and solutions, rather than on a systematic coverage of techniques. Serves as an introduction to the methods of machine learning. Prerequisite: Mathematics 221, 218, or 216; Mathematics 212; Mathematics 230 or Statistical Science 230; and Computer Science 201.
• Introduction to Computer Vision (COMPSCI 527): Image formation and analysis; feature computation and tracking; image, object, and activity recognition and retrieval; 3D reconstruction from images. Prerequisites: Mathematics 221, 218 or 216; Mathematics 212; Mathematics 230 or Statistical Science 230; Computer Science 101; Computer Science 230.
• Introduction to Algorithms (COMPSCI 531D): Applications include dynamic data structures, graph algorithms, and randomized algorithms. Intractability and NP-completeness. Prerequisite: Computer Science 201 and 230, or equivalent.
• Design and Analysis of Algorithms (COMPSCI 532): Design and analysis of efficient algorithms. Algorithmic paradigms. Applications include sorting, searching, dynamic structures, graph algorithms, and randomized algorithms. Computationally hard problems. NP-completeness. Prerequisites: Computer Science 201 and 330 or equivalent.
• Artificial Intelligence (COMPSCI 570): Design and analysis of algorithms and representations for artificial intelligence problems. Formal analysis of techniques used for search, planning, decision theory, logic, Bayesian networks, robotics, and machine learning. Prerequisite: Computer Science 201 and Computer Science 330.
• Introduction to Natural Language Processing (COMPSCI 572): Introduction to the modern methodologies underlying natural language processing, with a focus on machine learning and deep learning. Topics include language modeling, classification, generative and discriminative models of sequences and trees, and semantics. The course will also cover important NLP applications, such as question-answering, machine translation, and summarization. Prerequisites: undergraduate machine learning (COMPSCI 370 or 371) or statistical inference (STA 250D / MATH 342D), probability (MATH 230 / STA 230), linear algebra (MATH 221, 218 or 216), and programming in Python.
• Theory and Algorithms for Machine Learning (COMPSCI 671): This is an introductory overview course at an advanced level. Covers standard techniques, such as the perceptron algorithm, decision trees, random forests, boosting, support vector machines and reproducing kernel Hilbert spaces, regression, K-means, Gaussian mixture models and EM, neural networks, and multi-armed bandits. Covers introductory statistical learning theory. Recommended prerequisite: linear algebra, probability, analysis or equivalent.
• Introduction to Machine Learning (DESCI 538): This course serves as an introduction to machine learning and natural language processing. The emphasis is on social science applications, text as data, and the connection between theory and empirical work.
• Applied Probably for Statistical Learning (ECE 480): This course discusses topics in Bayesian probability and its application to the foundations of statistical learning. The primary objectives of the course are to provide a mathematically rigorous foundation in Bayesian probability and inference, develop strong intuition for Bayesian constructs, provide a foundation in statistical learning, and to show how Bayesian methods are fundamental to a variety of modern statistical learning techniques. Topics include probabilistic reasoning, Bayesian inference, linear models, mixture models, and model selection. Prerequisite: (Mathematics 216, 218, or 221) and (Statistical Science 130L, Statistical Science 240L, Mathematics 230, Mathematics 340, ECE 380, ECE 555, or EGR 238L) and (EGR 103L, Computer Science 101L, or Computer Science 201).
• Programming, Data Structures, and Algorithms in C++ (ECE 551):
  Students learn to program in C and C++ with coverage of data structures (linked lists, binary trees, hash tables, graphs), Abstract Data Types (Stacks, Queues, Maps, Sets), and algorithms (sorting, graph search, minimal spanning tree). The efficiency of these structures and algorithms is compared via Big-O analysis. Brief coverage of concurrent (multi-threaded) programming. Emphasis is placed on defensive coding, and the use of standard UNIX development tools in preparation for students’ entry into real-world software development jobs. Not open to undergraduates.
• Introduction to Machine Learning (ECE 580): Introduction to core concepts in machine learning and statistical pattern recognition, with a focus on discriminative and generative classifiers (nearest-neighbors, Bayes, logistic regression, linear discriminant, support vector machine, and relevance vector machine). Dimensionality reduction and feature selection. Classifier performance evaluation, bias-variance tradeoff, and cross-validation. Prerequisite: (Mathematics 216, 218D-1, 218D-2, or 221, or ECE 586) and (Computer Science 201 or ECE 551D) and (ECE 480 or ECE 581 or MATH 541 or MATH 730 or MATH 740). Not open to students who have taken Computer Science 671D.
• Statistical Computing (STA 323D): A practical introduction to statistical programming focusing on the R programming language. Students will engage with the programming challenges inherent in the various stages of modern statistical analyses including everything from data collection/aggregation/cleaning to visualization and exploratory analysis to statistical model building and evaluation. This course places an emphasis on modern approaches/best practices for programming including source control, collaborative coding, literate and reproducible programming, and distributed and multicore computing. Prerequisite: Statistical Science 210 and Statistical Science 240L or 230.
• Machine Learning and Data Mining (STA 325): The rapid growth of digitalized data and the computer power available to analyze it has created immense opportunities for both machine learning and data mining. This course introduces machine learning and data mining methods. Topics covered include information retrieval, clustering, classification, modern regression, cross-validation, boosting, and bagging. The course emphasizes the selection of appropriate methods and justification of choice, use of programming for implementation of the method, and the evaluation and effective communication of results in data analysis reports. Prerequisite: Prerequisite: Statistical Science 210 and (Statistical Science 240L or 230 or 231).
• Introduction to Statistical Decision Analysis (STA 340): Quantitative methods for decision making under uncertainty. Probability theory, personal probabilities and utilities, decision trees, ROC curves, sensitivity analysis, dominant strategies, Bayesian networks and influence diagrams, Markov models and time discounting, cost-effectiveness analysis, multi-agent decision making, game theory. Prerequisite: Statistical Science 230 or 231.
• Bayesian Inference and Modern Statistical Methods (STA 360L): Principles of data analysis and advanced statistical modeling. Bayesian inference, prior and posterior distributions, multi-level models, model checking and selection, stochastic simulation by Markov Chain Monte Carlo. Prerequisite: Statistical Science 210 and (Statistical Science 230, 231, or 240L) and (Mathematics 202, 202D, 212, or 222) and (Computer Science 101L, Computer Science 102L, Computer Science 201, or Engineering 103L) and (Mathematics 216, 218, or 221).
• Theory and Methods of Statistical Learning & Inference (STA 432): Estimators and properties (efficiency, consistency, sufficiency); loss functions. Fisher information, asymptotic properties and distributions of estimators. Exponential families. Point and interval estimation, delta method. Neyman-Pearson lemma; likelihood ratio tests; multiple testing; design and the analysis of variance (ANOVA). High-dimensional data; statistical regularization and sparsity; penalty and prior formulations; model selection. Resampling methods; principal component analysis, mixture models. Prerequisite: (Statistical Science 240L, 230, or 231) and (Mathematics 202, 212, 219, or 222). Not open to students with credit for STA 250. Recommended prerequisite: Statistical Science 210, 360, and (Mathematics 221, 218, or 216).
• Predictive Modeling and Statistical Learning (STA 521L): An introduction to statistical learning methods for prediction and inference. Topics include exploratory data analysis and visualization, linear and generalized linear models, model selection, penalized estimation and shrinkage methods including Lasso, ridge regression and Bayesian regression, regression and classification based on decision trees, Bayesian Model Averaging and ensemble methods, and time permitting, smoothing splines, support vector machines, neural nets, or other advanced topics. The R programming language and applications used throughout. Instructor consent required. Corequisite: Statistical Science 323D or 523L and Statistical Science 360, 601, or 602L.
• Probabilistic Machine Learning (STA 561D): Introduction to concepts in probabilistic machine learning with a focus on discriminative and hierarchical generative models. Topics include directed and undirected graphical models, kernel methods, exact and approximate parameter estimation methods, and structure learning. Prerequisite: Linear algebra, Statistical Science 250 or Statistical Science 611.
• Bayesian Statistical Modeling and Data Analysis (STA 601L): Principles of data analysis and modern statistical modeling. Exploratory data analysis. Introduction to Bayesian inference, prior and posterior distributions, predictive distributions, hierarchical models, model checking and selection, missing data, introduction to stochastic simulation by Markov chain Monte Carlo using a higher-level statistical language such as R or Matlab. Applications drawn from various disciplines. Not recommended for students with credit for Statistical Science 360. Prerequisites for undergrads: Statistical Science 210 and one of 240 or 432.
• Numerical Analysis (STA 612): Error analysis, interpolation and spline approximation, numerical differentiation and integration, solutions of linear systems, nonlinear equations, and ordinary differential equations. Prerequisites: knowledge of an algorithmic programming language, intermediate calculus including some differential equations, and Mathematics 221.
• Mathematical Numerical Analysis (MATH 361S): Development of numerical techniques for accurate, efficient solution of problems in science, engineering, and mathematics through the use of computers. Linear systems, nonlinear equations, optimization, numerical integration, differential equations, simulation of dynamical systems, error analysis. Research project and paper required. Not open to students who have had Computer Science 220 or 520. Prerequisites: Mathematics 212 and 221 and basic knowledge of a programming language (at the level of Computer Science 101), or consent of instructor.
• Statistical Learning and Inference (STA 432/MATH 343): Estimators and properties (efficiency, consistency, sufficiency); loss functions. Fisher information, asymptotic properties, and distributions of estimators. Exponential families. Point and interval estimation, delta method. Neyman-Pearson lemma; likelihood ratio tests; multiple testing; design and the analysis of variance (ANOVA). High-dimensional data; statistical regularization and sparsity; penalty and prior formulations; model selection. Resampling methods; principal component analysis, mixture models. Prerequisite: (Statistical Science 240L, 230, or 231) and (Mathematics 202, 212, 219, or 222). Not open to students with credit for STA 250. Recommended prerequisite: Statistical Science 210, 360, and (Mathematics 221, 218, or 216).
• Introduction to Linear Programming and Game Theory (MATH 375): Fundamental properties of linear programs; linear inequalities and convex sets; primal simplex method, duality; integer programming; two-person and matrix games. Prerequisite: Mathematics 221 or equivalence.
• Advanced Linear Algebra (MATH 403): Topics in linear algebra beyond those in a first course. For example: principal component analysis and other decompositions (singular value, Cholesky, etc.); Perron-Frobenius theory; positive semi-definite matrices; linear programming and more general convexity and optimization; basic simplicial topology; Gerschgorin theory; classical matrix groups. Applications to computer science, statistics, image processing, economics, or other fields of mathematics and science. Prerequisite: Mathematics 212 or 222 and Mathematics 218 or 221.
• Topological Data Analysis (MATH 412): Introduction to topology from a computational viewpoint, with a focus on applications. Themes include basic notions of point-set topology, persistent homology, finding multi-scale topological structure in point cloud data. Algorithmic considerations emphasized. Prerequisite: Mathematics 221 or equivalent.
• Introduction to High Dimensional Data Analysis (MATH 465/COMPSCI 445): Geometry of high dimensional data sets. Linear dimension reduction, principal component analysis, kernel methods. Nonlinear dimension reduction, manifold models. Graphs. Random walks on graphs, diffusions, page rank. Clustering, classification, and regression in high-dimensions. Sparsity. Computational aspects, randomized algorithms. Prerequisite: Mathematics 218 or 221.
• Mathematics of Machine Learning (MATH 466): The course will explore mathematics underlying the practice and theory of various machine learning concepts and algorithms. Kernel methods, deep learning, reinforcement learning, generalization error, stochastic gradient descent, and dimension reduction or data embeddings will be introduced. The interplay between the mathematics and real applications will be a component of the course. Students can take both this course and Mathematics 465 for credit. Recommended prerequisite: Mathematics 230/340 and 218/216/221 and some familiarity with programing, preferably Python.
• Theory and Practice of Algorithms (MATH 560): The mathematical theory of algorithms and graphs and their practical implementations. Examines the foundational mathematical structures for the behavior and analysis of algorithms from a variety of domains, with a particular emphasis on graphs. Students tie theory to practice by writing code to implement algorithms and compare experimentally observed run-times to those predicted by the mathematical theory. Recommended prerequisite: Computer Science 201; or recommended corequisite: ECE 551; or equivalent.
• Numerical Linear Algebra, Optimization and Monte Carlo Simulation (MATH 561): Structured scientific programming in C/C++ and FORTRAN. Floating point arithmetic and interactive graphics for data visualization. Numerical linear algebra, direct and iterative methods for solving linear systems, matrix factorizations, least squares problems and eigenvalue problems. Iterative methods for nonlinear equations and nonlinear systems, Newton’s method. Prerequisite: Mathematics 212 and 221.
• Applied Computational Analysis (MATH 563): Approximation theory: Fourier series, orthogonal polynomials, interpolating polynomials and splines. Numerical differentiation and integration. Numerical methods for ordinary differential equations: finite difference methods for initial and boundary value problems, and stability analysis. Introduction to finite element methods. Prerequisite: Mathematics 561 and familiarity with ODEs at the level of Mathematics 216 or 356.
• Numerical Analysis (MATH 565): Error analysis, interpolation and spline approximation, numerical differentiation and integration, solutions of linear systems, nonlinear equations, and ordinary differential equations. Prerequisites: knowledge of an algorithmic programming language, intermediate calculus including some differential equations, and Mathematics 221.
• Applied Probability for Statistical Learning (ECE 480): This course discusses topics in Bayesian probability and its application to foundations of statistical learning. The primary objectives of the course are to provide a mathematically rigorous foundation in Bayesian probability and inference, develop strong intuition for Bayesian constructs, provide a foundation in statistical learning, and to show how Bayesian methods are fundamental to a variety of modern statistical learning techniques. Topics include probabilistic reasoning, Bayesian inference, linear models, mixture models, and model selection. Prerequisite: (Mathematics 216, 218, or 221) and (Statistical Science 130L, Statistical Science 240L, Mathematics 230, Mathematics 340, ECE 380, ECE 555, or EGR 238L) and (EGR 103L, Computer Science 101L, or Computer Science 201).
• Fundamentals of Computer Systems and Engineering (ECE 550D): Fundamentals of computer systems and engineering for master’s students whose undergraduate background did not cover this material. Topics covered include: Digital logic, assembly programming, computer architecture, memory hierarchies and technologies, IO, hardware implementation in VHDL, operating systems, and networking. Undergraduates may not take this course and should take ECE 250D, 353, and/or 356 instead. Corequisite: ECE 551D.
• Mobile Application Development (ECE 564): Explores the world of mobile application development with focus on needs of engineers. Centered on Apple environment, with the development environment being on OS X and the target environment being an iOS device- iPad, iPhone, iPod Touch or Apple Watch. Real world context focused on the common programming patterns for engineers in academia or business- standalone apps, apps connected to other systems, apps connected to the cloud. Covers fundamentals essential to understanding all aspects of app development. Taught in a team environment. Students required to present their project proposals and deliver an app as a final project. Prerequisite: CompSci 307D or CompSci 308 or ECE 551D or ECE 751D.
• Introduction to Machine Learning (ECE 580): Introduction to core concepts in machine learning and statistical pattern recognition, with a focus on discriminative and generative classifiers (nearest-neighbors, Bayes, logistic regression, linear discriminant, support vector machine, and relevance vector machine). Dimensionality reduction and feature selection. Classifier performance evaluation, bias-variance tradeoff, and cross-validation. Prerequisite: (Mathematics 216, Mathematics 218, Mathematics 221, or ECE 586) and (Computer Science 201 or ECE 551D) and (ECE 480 or ECE 581). Not open to students who have taken Computer Science 671D.
• Deep Learning (ECE 685D): Provides an introduction to the machine learning technique called deep learning or deep neural networks. A focus will be the mathematical formulations of deep networks and an explanation of how these networks can be structured and “learned” from big data. Discussion section covers practical applications, programming, and modern implementation practices. Example code and assignments will be given in Python with heavy utilization of PyTorch (or Tensorflow) package. The course and a project will cover various applications including image classification, text analysis, object detection, etc. Prerequisite: ECE 580, ECE 681, ECE 682D, Statistical Science 561D, or Computer Science 571D.

• The Anthropology of Design and User Experience (CMAC 172): The field of design and the burgeoning field of User Experience (UX) research has recently applied the methods anthropologists have used for over a century. The methods of cultural anthropology are distinctly aligned to ask questions about motivations, beliefs, values, and relationships within cultural systems through direct participant observation, surveys, focus groups, and archival research. Privileging critical listening, empathy, and perspective-taking, we try to discern why people do what they do, and apply these questions to human-centered design.
• Introduction to Digital Feminism (COMPSCI 112S/SOCIOL 217S): The aim of this course is to critically analyze digital culture from a feminist and gender studies perspective. We will address topics related to digital innovation and its history, unpacking, and questioning them through the insights offered by genders studies analytical tools. Subjects such as the rise of the Silicon Valley, gaming culture, social media, algorithms, Artificial Intelligence, extraction of data applied to biotechnology, macroeconomic development of IT platforms and the impact of technology on ecology will be discussed starting from a current event or debate, to which we will give a historical, ethical, sociological, theoretical, literary, or cinematographic perspective.
• Race, Gender, Class, & Computing (COMPSCI 240): This course explores the diversity, equity, and inclusion (DEI) challenges in computing through an introduction to and analysis of identity as a social construct, its impact on computing departments and organizations, and the resulting impact of technology on various identities.
• Technical and Social Analysis of Information and the Internet (COMPSCI 342): The development of technical and social standards governing the Internet and information technology in general. The role of software as it relates to law, patents, intellectual property, and IETF (Internet Engineering Task Force) standards. Written analysis of issues from a technical perspective with an emphasis on the role of software and on how standards relate to social and ethical issues. Current events as a driver for writing in traditional and online formats related to technology and policy. Open only to students with declared Computer Science major. Prerequisite: Computer Science 201.
• Race, Genomics and Society (CULANTH 261D): The field of genetics has been at the forefront of discourse concerning the concept of “race” in humans. This course explores human origins, human variation, human identity, and human health through a broad range of enduring and emerging themes and challenging questions related to race and genetics (and now, genomics) on a global scale. Students will acquire knowledge and skills required for integrative analyses of the relevant scientific, ethical, legal, societal, cultural, and psychosocial issues. Open to students at all levels from any discipline in the arts, humanities, and sciences (natural, social, formal, and applied).
• Amazon.com and the Cybereconomy (CULANTH 273): This course will introduce students to the complexities and controversies around the meteoric growth of the digital economy, with a focus on the biggest company of them all, Amazon. We will examine questions that range from labor conditions and consumerism to data harvesting, algorithmic marketing, and monopoly concerns. By drawing on insights from cultural anthropology, economics, history, and other disciplines, the course will give students a new understanding of how e-commerce is changing the structure of our economy, society, and everyday lives. Students will do an individual research project on some aspect of Amazon for a final project.
• Global Apple: Life and Death and the Digital Revolution (CULANTH 360S): Examination of the Apple Corporation’s development from a Silicon Valley garage operation to a company with unprecedented global reach; the Cult of Steve Jobs, the Apple Launch and use the design and development of the Apple Store; labor and environmental struggles over Apple supply chain and production processes, from cobalt mining in Africa to Foxconn factories in China; migrant worker suicide and poetry as forms of protest in China; e-waste villages and digital rubbish; everyday uses of Apple technology and the ethics of consuming Apple products.
• The Googlization of Knowledge: Information, Ethics and Technology (ISS 112/CULANTH 112): Google has altered the way we see the world and ourselves. Its biases, valuing popularity over accuracy, affect how we value information and navigate news and ideas. This course examines information from different angles within the context of social justice, open access to information, and how the Internet and Google affect our lives. Themes include knowledge as a public good, Internet policies, data and visual literacies, social media, and artificial intelligence. Hands-on work researching how technology affects the access, understanding, and reliability of information in students’ lives. Analysis, discussions, and reflection assignments with ongoing application to team-based projects.
• Introduction to Cybersecurity Perspectives (CYBERSEC 500): Introduction to Cybersecurity Perspectives will introduce or re-acquaint the students with the cybersecurity challenges organizations face today, providing an overview of the domains, concepts and elements needed to provide the foundation for a well-performing cyber organization.
• Cybersecurity and Interdisciplinary Law/Ethics/Policy/Privacy Considerations (CYBERSEC 502): This course will introduce students to the legal, regulatory and policy topics that relate to cybersecurity, privacy and emerging technologies, and will provide (1) an overview of today’s threat landscape, the legal frameworks governing data breaches, cybercrime and cyberwarfare; (2) an examination of data privacy laws and the issues surrounding governments’ collection of personal data; and (3) an exploration of the impact emerging technologies have on regulatory agencies and public and private policies.
• NEUROETHICS (ETHICS 269):Focus on emerging ethical controversies concurrent with advances in neuroscience. Background material covered: concepts and methods in neuroscience; theories of ethics and morality from philosophy, law, and other fields. Ethical topics covered: biological bases of morality; emotions and decision making; neuroeconomics and neuromarketing; pathologies of mind and behavior; volition and legal culpability. Course format: combined lectures, discussion, interactive activities, with case studies and real-world examples (e.g., neuroimaging as legal evidence). Prior coursework in neuroscience and/or ethical inquiry recommended.
• Social Movements and Social Media (ISS 323S/VMS 323S): Examines uses and abuses of social media by social movements. Interested in a broader historical study of mediating technologies and oppositional public sphere, course considers the uses of cameras, phones, cassette players, radio, and social media platforms, but also books, bodies, art, fashion, and automobiles as oppositional technologies. Studies political and ethical uses of technologies in social unrest. Investigates impact of technologies on social movements and social transformations in contemporary history. Student driven case studies will highlight contemporary engagement with social media by networked social movements.
• Cyber Law: Law, Language and Computers (LINGUIST 498): Cyber law refers to the legal principles that govern the creation, use of computers, software, and computer networks, or that relate to the transfer, use, and storage of electronic information. In this course we will analyze the key legal principles concerning: ownership of the designs of integrated circuits and computer software; crimes involving the use of computers; protection of electronic data, with particular concern for the protection of privacy interests; freedom of expression on the internet. There will be several over-arching meta-themes in this course, and other related themes. This course is designed for students with little or no familiarity with the American legal system.
• Introduction to Digital Culture (Media Theory, Politics, Aesthetics) (LIT 304S): What is digital culture today? In the first two decades of the 00s, digital culture has become more directly related to the emergence of social media platforms (from Youtube to Instagram, from Snapchat to Tiktok). Digital culture is now shaped by artificial intelligence. We make new friends through dating apps and by becoming followers. We know that biases of race, class, gender and sexuality are embedded in everyday search algorithms. This course welcomes students to participate in these emerging discussions and experiment with new ideas that are shaping digital culture today.
• Business Ethics: The Debate Over Corporate Social Responsibility (PHIL 270): Debates about obligations of firms and business leaders over and above legal obligations. Examination of foundations and implications of corporate governance, corporate law, and the theory of the firm. Evaluation of challenges by supporters of stakeholder theory and corporate social responsibility.
• From Siri to Skynet: Our Complex Relationships with Technology (SCISOC 197FS): From mobile phones to driverless cars, modern high-tech devices have human-facing elements that shape our relationships with technology. Some integrate seamlessly into our daily lives, others frustrate us, and some simply captivate us. Students will investigate the intersection between people and technology to better understand how design can influence performance, safety, and user satisfaction. Topics include design principles; user experience concepts; and an overview of human strengths and limitations influencing interactions with technology. Case studies will include various technologies, including emerging systems such as brain-computer interfaces, robotics, and artificial intelligence. Open only to students in Focus program. Department consent required.
• Digital Intelligence I: Ethics of Emerging Technology (SCISOC 585-01): The Digital Intelligence course helps students navigate and understand and analyze the ethical and social impact of emerging technologies through an applied ethical lens. In a flipped-classroom format, students will watch asynchronous videos on a weekly basis featuring leading technology, ethics, and policy experts as they discuss relevant and timely topics such as algorithmic bias, the impact of social media on democracy, and privacy in the digital age. Students will meet weekly in small discussion groups to work through case studies and to critically engage with a practical ethics approach to the topics presented in the video and additional assigned material.
• Digital Intelligence II: Ethics of Emerging Technology (SCISOC 585-01): The Digital Intelligence course helps students navigate and understand and analyze the ethical and social impact of emerging technologies through an applied ethical lens. In a flipped-classroom format, students will watch asynchronous videos on a weekly basis featuring leading technology, ethics, and policy experts as they discuss relevant and timely topics such as algorithmic bias, the impact of social media on democracy, and privacy in the digital age. Students will meet weekly in small discussion groups to work through case studies and to critically engage with a practical ethics approach to the topics presented in the video and additional assigned material.

• Science Fiction Film (ARTHIST 238):Science fiction film from the 1950s to the present. From talking apes to mind control, forbidden planets to genetic dystopias, alien invasions to travel in time and space, an exploration of classic films in the genre with attention to how the films imagine the relationships among science, politics, and society over time. Attention to visual as well as literary story telling.
• Science & the Media (BIOETHIC 510S-01): Those who write about science, health and related policy must make complex, nuanced ideas understandable to the nonscientist in ways that are engaging and entertaining, even if the topic is far outside the reader’s frame of reference. Course examines different modes of science writing, the demands of each and considers different outlets for publication and their editorial parameters. Students interview practitioners of the craft. Written assignments include annotations of readings and original narratives about science and scientists. Course considers ways in which narrative writing can inform and affect policy. Prerequisites: a 200-level science course and/or permission of the instructor.
• Introduction to Digital Humanities (CMAC 222D/ISS 222D): Digital approaches to humanistic research and its expression, across disciplines and fields. Critical approaches to the digital turn in contemporary culture; theoretical approaches to digital creation and digital remediation of analog sources. Topics include aesthetics, cultural impact, opportunities for global circulation. Critical contextualization around access, authorship, diversity and inclusion, media effects, and evaluation. Exercises in text analysis, digital mapping, data visualization, databases, networks, online archives and exhibitions, immersive media, situated within broader cultural debates on digital cultures, and on best practices for interdisciplinary collaboration.
• Fundamentals of Web-Based Multimedia Communications (CMAC 240S): Multimedia information systems, including presentation media, hypermedia, graphics, animation, sound, video, and integrated authoring techniques; underlying technologies that make them possible. Practice in the design innovation, programming, and assessment of web-based digital multimedia information systems. Intended for students in non-technical disciplines. Engineering or Computer Science students should take Engineering 206 or Computer Science 408.
• Global Stories, Local Issues (CULANTH 223S): What stories are there to tell about often overlooked objects and people and places? How can we research and share those stories with generosity and integrity? In every corner of our lives—the stickers on our computers, the plates at a local restaurant, the wood in our guitars—there is a story to be told that connects our individual experiences to broader, often global, phenomena. Participants will learn and use methods of ethnography and archival research to connect their experiences and their observations about a place, community, or thing to larger stories about culture and society, and they will practice writing about their research in engaging and broadly accessible ways.
• Games and Culture (CULANTH 440S): Examines analog and computer games from a cultural perspective. Explores how prevailing culture and values affect game design, popularity, and experience. how games affect those areas of culture, such as imagining disaster, utopia, and dystopia. Explores role-playing and identity, ethics, group behavior, competition, politics, gender, race, and aesthetics.
• Art Activism & Everyday Technology (DANCE 301): In the last decade, smartphones and social media have been utilized to expose societal inequity and injustice. This course explores the intersection of arts activism and digital activism, both of which sprout from a desire to effect change. In Arts Activism & Everyday Technology, students integrate their social media savvy and passion for exerting influence into a formalized generative art practice (using dance, film, photography, drama, music, visual art, creative writing, and digital media design) addressing a topical issue. Students study past and present artist-activists, research an area of interest and use their smartphones to create a culminating interdisciplinary digital exhibition.
• Documentary and Policy: How Documentary Influences Policy (DOCST 272S): Examines documentaries as catalysts for change in local, state, and federal laws and regulations, with special attention to relationships between film and organizations with political influence. Looks at how documentaries have altered public sentiment and political outcomes. Uses case studies of documentary films (essay-style, journalistic, information-driven films; narrative, story-driven films; propaganda; art films; and hybrids of all the above). Explores the question of how a film achieves influence: for example, with a high-profile theatrical and/or television release, by utilization as an educational tool, or by ‘going viral’ to become part of a public conversation.
• Social Marketing: From Literary Celebrities to Instagram Influencers (ENGLISH 253):
  Typical Duke students spend hours each day using social media. You’ve surely heard the platforms described as “revolutionary,” and you’ve also heard them described as “time wasters.” What you probably haven’t thought about is how similar they are to previous “revolutionary” communications technologies like novels, newspapers, and even language itself. This course explores ways in which studying the masters of previous “social” media technologies—the Shakespeares, Whitmans, and Eliots of the world—can help us understand how influencers on digital social media leverage the same platforms you use every day to market themselves, build their brands, and grow their audiences.
• Artificial Intelligence in Literature and Film (END 358S/SCISOC 358S):
  Who is the most famous virtual assistant in the world? Siri (unveiled 2011), Alexa (2013), or is it, just maybe, the Magic Mirror from Snow White (1937)?  Artificial intelligence powers many of today’s cutting-edge technologies, but the inspiration for Siri, the Metaverse, and cyberspace comes from myths, short stories, novels, and fairy tales.  The word “robot” was first coined by the Czech playwright Karel Capek and can be translated as “serf” or “slave”.  This course begins with the premise that new AI technologies are shaped not just by science and data, but also by fiction, myth, and, now more than ever, hype.
• Science and the Modern World (HISTORY 106): This course surveys the history of science from the sixteenth century through the present day. It addresses science not just as a body of knowledge and methods but as a cultural activity that has shaped and been shaped by modern global history. Topics will range across physical sciences, life sciences, earth and environmental sciences, and social sciences. This course takes a global perspective, with emphasis on parallels, differences, and interconnections among ways of knowing nature in different places and times, as well as the role of specific materials, environments, technologies, and practical problems in the development of modern science.
• Fundamentals of Web-Based Multimedia Communications (ISS 240L): Laboratory version of Information Science + Studies 240. Multimedia information systems, including presentation media, hypermedia, graphics, animation, sound, video, and integrated authoring techniques; underlying technologies that make them possible. Practice in the design innovation, programming, and assessment of web-based digital multimedia information systems. Intended for students in non-technical disciplines. Engineering or Computer Science students should take Engineering 206 or Computer Science 408.
• Fundamentals of Web-Based Multimedia Communications (ISS 240S): Multimedia information systems, including presentation media, hypermedia, graphics, animation, sound, video, and integrated authoring techniques; underlying technologies that make them possible. Practice in the design innovation, programming, and assessment of web-based digital multimedia information systems. Intended for students in non-technical disciplines. Engineering or Computer Science students should take Engineering 206 or Computer Science 408.
• What was the Digital? Media, Culture, and Technology (Lit 190S-02): Introduction to the study of literature and other forms of cultural expression, such as film. Different introductory approaches will be used in each section (for example, a systematic account of literary genres, a historical survey of ideas and forms of fiction, concepts of authorship and subjectivity, or of literary meaning and interpretation).
• Virtual Realities: Collective Dreams from Plato to Cyberspace (LIT 265/GERMAN 266): What is “virtual reality”? If something is real, isn’t it also always actual, and if virtual, only almost or nearly real? What strange, hybrid no-mans-land lies midway between truth and illusion, and how can we learn to navigate inside this space? The puzzle is an old one, even if the technology we call VR is new. In this discussion-intensive course, we will read, watch, and play our way through some of the most powerful attempts to understand humanity’s penchant for collective dreaming: from Plato’s allegory of the cave, to the immersive spectacles of baroque theater, to the ghostly realms of gothic literature and modern film, to the invention of cyberspace and parallel universe games.
• Watchdog News and Storytelling: Changing Forms of Accountability Journalism (PJMS 374S): Focus on evolving styles of explanatory reporting and investigative journalism. Practice fundamental research and writing techniques that journalists use to reveal complex issues and hold powerful institutions and people accountable. Identify sources, develop interviewing skills, and tap public records. Analyze stories that can serve as engaging models for your assignments, such as fact-checks, solutions-focused articles, and first-person accounts that turn the reporting process into a narrative device. Learn about editorial rules and writing conventions, including their ethical underpinnings and the role of objective methods. Talk with guest journalists about their experiences.
• Journalism in the Age of Data (PJMS 375): Teaches the tools and techniques used by investigative journalists to acquire and analyze data in order to discover story ideas and draw and evaluate conclusions about politicians, public policy, broader behavior of public institutions. Students should have basic familiarity with journalism concepts, but no specific technical or mathematical skills required.
• Political Communication in a Changing Media Environment (POISCI 239S): Examination of interaction between citizens, media and political actors in today’s fragmented information environment. Topics include evolution of political communication and media, emergence of new communication technologies, changes in campaign communication strategy, nature of news, theories of attitude formation and change, and role of political communications in campaigns and elections. Focus on implications of changing information environment for political communication strategies and for citizen knowledge and engagement in democratic process.
• Combatting Hate in a Digital Age (PUBPOL 290)
• News as a Moral Battleground (ETHICS 259): Ethical inquiry into journalism and its effect on public discourse. Issues include accuracy, transparency, conflicts of interest and fairness. Topics include coverage of national security, government secrecy, plagiarism/fabrication, and trade-offs of anonymous sourcing.
• Public Speaking: Policy Advocacy and Communication (PUBPOL 384): Theoretical and practical understanding of the elements of effective advocacy, especially as applied to policy issues. Focus on oral communication (both formal public speaking and interactive exchange), written exposition, and presentation skills. Emphasis on the human dimensions of the communication process-voice and body behavior, audience evaluation, focus, control and self-awareness. Identifies techniques for minimizing communication distraction, developing confidence in presentation situations, and analyzing informational requirements. Does not apply toward public policy studies major. This course is open to students in their junior or senior year.
• Visualizing Society (SOCIOL 179FS) †: This class will teach you how to use modern, widely used tools to create insightful, beautiful, reproducible visualizations of social science data. We will also put the theory and practice of visualization into context throughout the semester. By that I mean that we will think about different ways of looking at social science data, about where data comes from in the first place, and the implications of choosing to represent it in different ways. Open only to students in the Focus Program. Department consent required.
• Social Movements and Social Media (VMS 190FS/LIST 190FS)

• Virtual Museums: Theories and Methods of Twenty-First Century Museums (ARTHIST 305L): The future of museums will be one of immateriality and interaction. Course focuses on how the “Internet of Things,” augmented reality technologies, new data analyses of artifacts will transform missions, roles, and goals of museums and collections. Core of course will be digital lab sessions focused on virtual reconstruction of lost heritage—e.g., museums and sites destroyed and damaged by ISIS and other conflicts in Iraq and the Middle East (Hatra, Nineveh, Nimrud, Baghdad).
• Mapping History with Geographic Information Systems (ARTHIST 315): Beginner/intermediate Geographic Information System (GIS) course designed to help students learn how to investigate history spatially. Emphasizes perspectives, procedures, and tools that are relevant to applications of GIS in Art History and Humanistic disciplines. Designed as a hybrid lecture/lab format in which direct instruction is supplemented by hands on learning labs using ArcGIS software and real-world spatial data. The main skills students will gain are: integration of spatial and tabular data, geoprocessing, data visualization, creating features, editing features, vector and raster integration, spatial analysis, georeferencing.
• Visualizing Cities: Representing Urban Landscapes, Cultures, and Environments (ARTHIST 382): Visualizing cities in theory and practice. Exploring digital and visual representation of landscapes, structures, environments, history, culture, architecture, events, and populations. Change over time, cultural heritage, possible futures, and alternate pasts from historical, cultural, documentary, and scientific evidence and archives. The idea of the city as a conceptual category and metaphor. Ubiquitous computing in urban environments as a medium for interaction. Global cities and diaspora. Visual imagery and written accounts. Use of mapping, imaging, 3D, augmented reality, games. Individual and group research and production of visualizing cities projects. Topics and temporal foci vary.
• Introduction to Digital Archaeology (ARTHIST 547L): Course studies the radical changes that new methodologies and technologies have wrought in archaeology. Remote sensing technologies, digital tools, virtual reality systems for data recording, documentation, simulation and communication of archaeological data have profoundly changed archaeological field operations. Course surveys the state of the art in: techniques of digital recording and digital documentation; GIS and remote sensing; international case studies in digital archaeology; virtual reality and virtual simulation; Web and digital publications.
• Proseminar I: Interdisciplinary Digital Humanities (ARTHIST 580S): Multimodal interdisciplinary digital humanities in theory and practice. Research, cultural heritage applications, public outreach. Theoretical and critical perspectives on humanities texts, data, images and other media; archives and exhibitions; visualization; museums; digital mapping and timelines; immersive and interactive media systems; apps and installations. Project-based critique, hands-on exercises, project management, and reflective writing. Interaction with Smith Media Labs projects and collaborators. Attention to digital divides, access and equity issues, global media contexts, sustainability, evaluation best practices, and obsolescence/EOL considerations for digital projects.
• Historical and Cultural Visualization Proseminar 2 (ARTHIST 581S): 2D and 3D imaging, modeling; raster and vector graphics sources, laser scanners, photogrammetric software, basic database structures. Digital mapping and GIS. Presentation strategies and best practices for the web (standards-compliant HTML/CSS/Javascript), multimedia (audio/video/animation), scholarly annotation, intellectual property. Theoretical, ethical issues in field of new media and digital humanities. Epistemological issues re: mediation and visualization, ethics of intellectual property, politics of geospatial visualization, digital materiality, affordances of new media narrativity. Instructor consent required.
• Digital Imaging (ARTSVIS 206): Photoshop and Illustrator used to introduce single and serial images for print and web output. Consent of instructor required.
• Interactive Graphics: Digital Code (ARTVIS 242L): Introduction to interactive graphics programming for artists. Explores object-oriented programming via the Processing programming environment as well as historical and theoretical appreciation of interactivity and computer graphics as artistic media. Combines discussions of key concepts from the readings with hands-on Processing projects and critiques. No previous programming experience or prerequisites required. Enrollment limited to 15 students.
• (Neosentience) Body as Electrochemical Computer (ARTVIS 510S): Weekly discussions/lectures related to different disciplinary understandings of the body, exploring new computational and aesthetic paradigms for brain/mind/body/ environment relations, and working towards articulating bridging languages enabling researchers to talk across disciplines. Students required to participate in ongoing discussion, develop particular aspects of research and write a major research paper.
• Computing on the Genome (BIOLOGY 208FS): This course will provide an introduction to key concepts in the genome sciences, using tools and concepts from computational biology and bioinformatics. Topics to be covered include genome structure, function, variation, and evolution. Students will learn computational and statistical methods for describing and quantifying various aspects of genome biology and will apply these tools to real world data. Recommended prerequisite: familiarity with molecular biology concepts such as DNA replication, transcription, and translation. No prior programming experience is required. Open only to Focus Program students.
• Genetic Engineering and Biotechnology (BIOLOGY 417S): Applications of recombinant DNA in medicine and in agriculture. Topics include diagnosis of genetic diseases, gene therapy, drugs for AIDS and cancer, DNA fingerprinting, cloning of mammals, phytoremediation, crop improvement, and pharmaceutical protein production in transgenic plants and animals. Social and environmental impacts of biotechnology. Recommended prerequisite: Biology 201L, 203L, or 220, or lab experience or consent of instructor.
• Computational Neuroengineering (BME 503): This course introduces students to the fundamentals of computational modeling of neurons and neuronal circuits and the decoding of information from populations of spike trains. Topics include: integrate and fire neurons, spike response models, homogeneous and inhomogeneous Poisson processes, neural circuits, Weiner (optimal) adaptive filters, neural networks for classification, population vector coding and decoding. Programming assignments and projects will be carried out using MATLAB. Prerequisites: Biomedical Engineering 301L or equivalent.
• Digital Image Processing (BME 544): Introduction to the theory and methods for digital image sampling, enhancement, visualization, reconstruction, and analysis with emphasis on medical applications. Course Outline: #1: Introduction, history, and applications of image processing. #2: Spatial domain image enhancement. #3: Fourier domain image enhancement. #4: Image registration. #5: Inverse problems (denoising, deblurring, interpolation, and super-resolution). #6: Wavelets and compressive sensing. #7: Biological image processing. Undergraduate courses on signals and systems, probability and statistics recommended; knowledge of Matlab required. Prerequisites: Biomedical Engineering 271 or Electrical and Computer Engineering 280L or consent of the instructor. Instructor consent required.
• Medical Software Design (BME 547): Software is critical in many medical devices, including device control, feedback and signal processing. This course focuses on software development skills that are ubiquitous in the medical device industry, including software version control, unit testing, fault tolerance, continuous integration testing and documentation. Experience will be gained in Python and JavaScript. The course will be structured around a project, done in small student groups, to build an Internet-connected medical device that measures and processes a biosignal, sends it to a web server, and makes those data accessible to a web client/mobile application. Prerequisite: Biomedical Engineering 271, Biomedical Engineering 271A, or graduate student standing.
• Computational Biology Seminar (CBB 510S): A weekly series of seminars on topics in computational biology presented by invited speakers, Duke faculty and CBB doctoral and certificate students. This course is required for all first and second year CBB students. In addition, all certificate students must register and receive credit for the seminar for four semesters.
• Genome Tools and Technologies (CBB 520)
• Introduction to the Finite Element Method (CEE 530): Investigation of the finite element method as a numerical technique for solving linear ordinary and partial differential equations, using rod and beam theory, heat conduction, elastostatics and dynamics, and advective/diffusive transport as sample systems. Emphasis is placed on the formulation and programming of finite element models, along with the critical evaluation of results. Topics include Galerkin and weighted residual approaches, virtual work principles, discretization, element design and evaluation, mixed formulations, and transient analysis. Prerequisites: a working knowledge of ordinary and partial differential equations, numerical methods, and programming in FORTRAN or MATLAB.
• Foundations of Game Design (CMAC 125L): Exploration of the theory and practice of game design with a focus on critical play, game decomposition, and iterative design. Students explore a range of non-digital games to discover how design elements combine to form meaningful systems of play. Readings, discussions, and hands-on design exercises prepare students as they design, develop, and document meaningful games in a collaborative environment. Programming experience is not required.
• Intro IOS Mobile Programing (COMPSCI 207): This class explores the world of mobile applications development based on Apple’s iOS operating system and Swift programming language. The class will work on Mac computers running Xcode, the integrated development environment, to develop applications for iPhone/iPad devices. The class covers fundamentals essential to understanding all aspects of app development from concept to deployment on the App Store. Students required to present their project proposals and deliver a fully functional mobile application as a final project. Prerequisite: Computer Science 201. Computer Science 250 preferred.
• Introduction to Computational Genomics (COMPSCI 260): A computational perspective on the analysis of genomic and genome-scale information. Focus on exploration and analysis of large genomic sequences, but also attention to issues in structural and functional genomics. Topics include genome sequence assembly, local and global alignment, gene, and motif finding, protein threading and folding, and the clustering and classification of genes and tissues using gene expression data. Students to learn computational approaches to genomics as well as to develop practical experience with handling, analyzing, and visualizing information at a genome-scale.
• Algorithms in the Real World (COMPSCI 333): Design and implementation of modern algorithms. Stresses application and project-based development of algorithmic techniques. Emphasis on algorithmic ideas that have had substantial impact in the real world, including approximation, randomization, hashing, streaming, spectral techniques, optimization, and search. Project-driven: Several homework assignments as well as a larger student-driven course project researching, designing, and implementing algorithms for a substantive problem with real world applications. Prerequisite: Computer Science 201 and Computer Science 230 or equivalent.
• Computer Security (COMPSCI 351): Cryptographic primitives including private key cryptography, and public key cryptography. Software security including buffer overflows, SQL injection, Web-based attacks, and viruses. Network security including TCP and DNS. Topics in applied cryptography including digital currency, searchable encryption, secure multiparty computation, secret sharing, homomorphic encryption, Zero knowledge proofs. Anonymity. Prerequisite: Computer Science 250D.
• Edge Computing (COMPSCI 564): A seminar-format examination of design principles and recent advances in edge computing, a distributed networked system architecture that places computing and storage at multiple locations between the user and the cloud. The class covers edge computing platforms, edge-adapted algorithms, and the use of edge in mobile and Internet of Things systems and applications. The class focuses on in-depth examinations of key scientific advances in the field. Students complete and present a research-based project, individual or team-based. Prerequisite: ECE/COMPSCI 356 or ECE/COMPSCI 350L or ECE 353/COMPSCI 310 or Graduate Standing.
• Computer Security (COMPSCI 581): Principles of securing the creation, storage, and transmission of data and ensuring its integrity, confidentiality, and availability. Topics include access control and authentication in distributed systems; cryptography and cryptographic protocols (mainly key exchange protocols); user authentication; software vulnerabilities and software engineering to reduce vulnerabilities; firewalls and related technologies; technologies to support online privacy; and selected advanced topics. Prerequisite: Computer Science 201 and 230 and (210 or 250).
• Cryptography (COMPSCI 582): Introduction to the design and analysis of cryptographic algorithms. Topics include basics of abstract algebra and number theory; symmetric and asymmetric encryption algorithms; cryptographic hash functions; message authentication codes; digital signature schemes; elliptic curve algorithms; side-channel attacks; and selected advanced topics. Prerequisite: COMPSCI 230 or equivalent or graduate standing.
• Cybersecurity Risk Management (CYBERSEC 503): Understanding and measuring risk is fundamental to protect an organization or enterprise from real and potential cybersecurity threats. Students will learn and apply various modeling techniques used to identify and quantify risk and explore how they are used to determine the value and criteria for managing risk. Risk management concepts and standards, including essential elements, effective governance, appetite for risk, and the need to develop appropriate policies and procedures to mitigate risk, will be explored across different industries and environments.
• Security Incident Detection, Response, and Resilience (CYBERSEC 510): Current and emerging technologies and processes to monitor, detect and respond to security incidents in systems, networks, and clouds will be covered including automation and analytics. Best practices for developing effective incident response plans, including regulatory and legal considerations, will be studied. Also studied is how to build resilience into development, manufacturing, or other business processes in the case of an incident.
• Applying Machine Learning to Advance Cybersecurity (CYBERSEC 520): The use of machine learning and AI is becoming more prevalent for collecting and analyzing data as its consolidation increases in value. Cyberattacks seek to steal, deny access, misrepresent (such as deepfakes), or compromise the privacy of information. Students will explore the power of machine learning and AI’s use in enhancing Cybersecurity tools across the NIST Framework and in detecting and exploiting vulnerabilities in timeframes and ways heretofore unthinkable.
• The Human Element in Cybersecurity (CYBERSEC 531): This course will examine the challenges associated with humans using, managing, and manipulating socio-technical systems with cybersecurity vulnerabilities. Technology and policy defenses and mitigations will be explored as well as societal, ethical, and legal implications of cybersecurity interventions.
• Archiving And Visualizing Asia (DOCST 476S):Engages students in the practices and theories of archiving, documenting and curating marginal histories. Hands-on research in the archives of Duke’s Rubenstein Special Collections and elsewhere. Examines histories of movements and encounters between the ‘West’ and ‘Asia.’ Teaches original archival research and documentary methods through guided excavations in digital, audiovisual, and material resources. Directed readings and special guest lectures guide students on how to think critically on the theories and praxis of knowledge production, collection, documentation, circulation, and consumption. Students curate projects for final research assignment.
• Introduction to Robotics and Automation (ECE 383): Fundamental notions in robotics, basic configurations of manipulator arm design, coordinate transformations, control functions, and robot programming. Applications of artificial intelligence, machine vision, force/torque, touch and other sensory subsystems. Design for automatic assembly concepts, tools, and techniques. Application of automated and robotic assembly costs, benefits, and economic justification. Selected laboratory and programming assignments. Prerequisites: ECE 280L or EGR 224L.
• Wearable and Ubiquitous Computing Systems Design (ECE 469): Design, implement, and evaluate wearable and ubiquitous computing systems. Topics include challenges and constraints in wearable and ubiquitous computing, input/output devices, human-computer interaction, embedded systems, prototyping, machine learning with focus on activity and affect recognition, applications with focus on healthcare, ethics, and social impact; project management and planning as students work on a semester-long team-based multidisciplinary project. Prerequisite: ECE 350L, ECE 230L, 250D, 270DL, and 280L and (Math 353 or 356) and (Math 230 or ECE 555 or ECE 380 or StatSci 240L or EGR 238L or Math 340) and (Physics 152L or 26) and (Chem 101DL or 20 or 21).
• Introduction to Digital Communication Systems (ECE 483): Introduction to the design and analysis of modern digital communication systems. Communication channel characterization. Baseband and passband modulation techniques. Optimal demodulation techniques with performance comparisons. Key information-theoretic concepts including entropy and channel capacity. Channel-coding techniques based on block, convolutional and Trellis codes. Equalization techniques. Applications to design of digital telephone modems, compact discs and digital wireless communication systems. Prerequisite: ECE 280L and one of (Statistical Science 130L or Statistical Science 240L or Mathematics 230 or or Mathematics 340 or ECE 380 or ECE 555 or Engineering 238L).
• Wireless Networking and Mobile Computing (ECE 556): Theory, design, and implementation of mobile wireless networking systems. Fundamentals of wireless networking and key research challenges. Students review pertinent journal papers. Significant, semester-long research project. Networking protocols (Physical and MAC, multi-hop routing, wireless TCP, applications), mobility management, security, and sensor networking. Prerequisites: Electrical and Computer Engineering 356 or Computer Science 310.
• Computer and Information Security (ECE 560): An intense trip through many facets of computer and information security. Includes discussion and practical exercises in risk management, threat modeling, applied cryptography, malicious software, network security, intrusion detection and prevention, software and OS security, auditing and forensics, reverse engineering, and social engineering. Includes many hands-on security assignments. Prerequisite: Computer Science 310, ECE 353, or ECE 650.
• Mobile Application Development (ECE 564): Explores the world of mobile application development with focus on needs of engineers. Centered on Apple environment, with the development environment being on OS X and the target environment being an iOS device- iPad, iPhone, iPod Touch or Apple Watch. Real world context focused on the common programming patterns for engineers in academia or business- standalone apps, apps connected to other systems, apps connected to the cloud. Covers fundamentals essential to understanding all aspects of app development. Taught in a team environment. Students required to present their project proposals and deliver an app as a final project. Prerequisite: CompSci 307D or CompSci 308 or ECE 651.
• Econometrics and Data Science (ECON 204D): This course builds on the foundation laid in 104. Develops skills necessary to analyze and interpret real world data using modern data science methods to provide a toolkit to be sophisticated consumers and producers of empirical research in econ as well as other fields in the social, health and life sciences. Mastery of the material provides knowledge of econometric and data science methods to think critically about the quality of evidence about how individuals behave, markets work, firms make money or societies operate. Prerequisite: (Economics 21 and 22, 23 and 24, 101, 101D, or 201D) and (Economics 104D, Statistical Science 111 or Statistical Science 432/Mathematics 343).
• Crypto: A New Paradigm in Economics (ECON 390S)
• Engineering Design & Communication (EGR 101L):Students work in a team to learn and apply the engineering design process to solve an open-ended, client-based problem drawn from a community partner. In this class, students learn to apply the engineering design process to meet the needs of a client, iteratively prototype using tools and materials appropriate to the solution, work collaboratively on a team, and communicate the critical steps in the design process in written, oral, and visual formats. First-year Pratt students only. Trinity first-year students may take the course with instructor consent.
• Fundamental Microelectronic Devices (EGR 330L):Introduction to semiconductor materials and their corresponding electronic devices and circuits. In lab, students will perform photolithography and characterize devices and circuits. Lecture will cover: underlying physics of semiconductor materials; operation of semiconductor devices, including diodes and transistors (MOSFETs); and application of MOSFETs into digital circuits. Students will understand basic operation of semiconductor devices in a way that is foundational for the expansive semiconductor industry. Prerequisite: (Engineering 103L or Computer Science 201) and (Physics 152L or 26) and (Electrical and Computer Engineering 110L or Biomedical Engineering 253L).
• Digital Systems (EGR 350L):Design and implementation of combinational and sequential digital systems with special attention to digital computers. The use of computer-aided design tools, hardware description languages, and programmable logic chips to facilitate larger and higher performance designs will be stressed. Laboratory exercises and group design projects will reinforce the various design techniques discussed in class. Prerequisite: Electrical and Computer Engineering 250D or Computer Science 250.
• Control Systems (EGR 382L):Model dynamic systems, characterize time and frequency domain response with respect to particular inputs. Characterize systems in terms of rise-time, settling-time, bandwidth. Identify the difference between stable and unstable system. Apply feedback control to modify response of dynamic systems. Develop methods of designing compensators for single-input, single-output, and multiple-input, multiple-output dynamic systems. Introduces optimal control theory, the linear quadratic regulator problem, the linear quadratic Gaussian problem. Gain a physical understanding of role of feedback control in modifying the dynamics of a system. Prerequisite: (Engineering 224L or ECE 280L) and Mathematics 216. Not open to students who have taken ECE 382.
• Fundamentals of Geographic Information Systems and Geospatial Analysis (ENVIRON 559)
  Fundamental aspects of geographic information systems and satellite remote sensing for environmental applications. Covers concepts of geographic data development, cartography, image processing, and spatial analysis. Gateway into more advanced training in geospatial analysis curriculum. Consent of instructor required.
• Software Engineering for FinTech (FINTECH 512): This course focuses on moving from small-to-medium software projects, to the design ideas required for larger scale, maintainable code. We will start with core design principles, which we will see manifest in a variety of the forms through the course of the semester. We will see these ideas emerge from smaller scale design at the start of the semester to large scale system architecture at the end. Testing will also be an important topic throughout.
• Secure Software Development (FINTECH 514): This course is about minimizing risk when creating software and will focus on the fundamental structure of a Secure Development Life Cycle (SDLC), the advantages and challenges of cryptography, then explore automated testing solutions. Students will learn to effectively manage risk in the process of creating software. Hands-on experience with specific technologies prepares students to make informed decisions about the design, architecture, and implementation of software. Assignments use automated vulnerability hunting tools. Students will learn the risk profile of the target software project, and an understanding of how these tools add value to the overall secure development life cycle.
• Design and Testing of Algorithmic Trading Systems (FINTECH 533): This course introduces students to the tools, concepts, and workflow used by industry to craft algorithmic trading systems, as well as the financial concepts involved. Using the Python Dash framework, students will build simple but powerful trading apps that fetch data, pass trade orders, and evaluate performance metrics. Students will gain exposure to modern Python data analytics packages, GitHub Actions, market data feeds, web scraping, and trade execution system APIs. The course assumes an entry-level understanding of Python and finance and is intended for students who wish to take their skills to the next level.
• Robo-Advising: the Future of Investing? (FINTECH 536): Robo-Advice brings investment services to a wider audience at lower costs compared to human advisors. Students will construct a very basic advisor using the Python programming language. This will be a short experiential case study with an open-source Python code. Student teams will develop a comprehensive venture capital investment memorandum for a real-world Robo-Advising startup. Teams will analyze the Robo-Advisor’s market environment, including the financial services industry, wealth management segments, competitors, and channels; and, internal company characteristics, such as business strategy, asset allocation and portfolio composition, cost of customer acquisition, and financials.
• Blockchain (FINTECH 564): Blockchain technology is being embraced in finance and other industries as an encryption base for all types of applications. This course explores the history, current environment, and near-term outlook of financial innovation (FinTech), focusing on applications of Blockchain technology. Topics range from digital stores of value to documents and transactions. Students will learn to formulate an accurate image and deep practical understanding of the capabilities and limitations of various blockchain techniques. Students will gain hands on experience creating a simple Blockchain contract and will be able to converse on a practical basis about what Blockchain can and cannot do.
• Advanced Blockchain – Smart Contacts and Solidity Coding (FINTECH 565): This course follows the basic blockchain course to provide students hands on experience and instruction in Solidity coding via a number of exercises and programming assignments. These provide a basis from which students will be introduced to the details of smart contracts and the application of the coding skills acquired to develop and deploy these programs. Deployment will be primarily via public blockchains using developer functions. Prerequisite: Financial Technology 564.
• Introduction to Global Health Data Science (GLHLTH 298L): Rigorous introduction to health data science using current applications in biomedical research, epidemiology, and health policy. Use modern statistical software to conduct reproducible data exploration, visualization, and analysis. Interpret and translate results for interdisciplinary researchers. Critically evaluate data-based claims, decisions, and policies. Includes exploratory data analysis, visualization, basics of probability and inference, predictive modeling, and classification. This course focuses on the R computing language. No statistical or computing background is necessary. Not open to students who have taken a 100-level Statistical Science course, Statistical Science 210, or a Statistical Science course numbered 300 or above.
• Designing Ethical Tech (I&E 511): Digital technology is not power-neutral; designed by humans, technological devices and systems are encoded with conscious and unconscious biases. In this learner-centered, problem-focused, and project-based course, students will investigate the relationship between how technology is designed and the impact a design has on reinforcing–and sometimes subverting–oppressive power structures in society such as racism and sexism. Students will learn about open design, an equity-centered innovation methodology, and, working in teams, apply it to create a prototype that addresses the problem of unethical technology development.
• Games and Culture: Gateway to the Study of Games (ISS 188FS): Examines analog and computer games from a cultural perspective. Explores how prevailing culture and values affect game design, popularity, and experience. how games affect those areas of culture, such as imagining disaster, utopia and dystopia. Explores role-playing and identity, ethics, group behavior, competition, politics, gender, race, and aesthetics.
• Experimental Interface Design (ISS 198): Class explores issues surrounding embodied approaches to interface design. Articulates methodology for generating new forms of human/computer interface; includes workshops, discussions, student presentations, critiques and group brainstorming sessions. Content related to biomimetics; haptic body knowledge; multi-modal sensing; physical computing; physical | digital relationships; networked relations; the potentials of virtual space and different qualities of space, both visual and sonic. Database potentials discussed and explored in service of developing new approaches to interface. Instructor consent required.
• Advanced Data Visualization (ISS 313L/STA 313L): This course is all about the art and science of visualizing data. Learn about the what (types of visualizations, tools to produce them), the how (start with a design, pre-process the data, map it to graphical attributes, make strategic decisions about visual encoding, post-process for readability and visual appeal), and the why (the theory behind the grammar of graphics). Evaluate the clarity, effectiveness, and honesty of visualization choices and improve (your and others’) visualizations through an iterative design process. Discuss the role of statistical graphics in modeling and inference. Do it all in R, reproducibly, and using a variety of modern data visualization packages. Prerequisite: Prerequisite: Statistical Science 198L or (198L-1 and 198L-2) or 199L or (199L-1 and 199L-2) or 210L.
• Mapping History with GIS (ISS 315): Beginner/intermediate Geographic Information System (GIS) course designed to help students learn how to investigate history spatially. Emphasizes perspectives, procedures, and tools that are relevant to applications of GIS in Art History and Humanistic disciplines. Designed as a hybrid lecture/lab format in which direct instruction is supplemented by hands on learning labs using ArcGIS software and real-world spatial data. The main skills students will gain are: integration of spatial and tabular data, geoprocessing, data visualization, creating features, editing features, vector and raster integration, spatial analysis, georeferencing.
• Computational Approaches to Human Language (LINGUIST 211): This course will explore a range of techniques designed to help machines perform tasks involving human language. We will cover both rules-based and machine learning approaches for morphological, syntactic, semantic, co-reference, and discourse processing. We will also touch on issues involved in natural language understanding, such as cognitive and linguistic phenomena and applications that can benefit from natural language processing such as question answering, machine translation, and spoken language understanding.
• Topological Data Analysis (MATH 412/COMPSCI 434): Introduction to topology from a computational view-point, with a focus on applications. Themes include: basic notions of point-set topology, persistent homology, finding multi-scale topological structure in point cloud data. Algorithmic considerations emphasized. Prerequisite: Mathematics 221 or equivalent.
• Mathematical Finance (MATH 581): An introduction to the basic concepts of mathematical finance. Topics include modeling security price behavior, Brownian and geometric Brownian motion, mean variance analysis and the efficient frontier, expected utility maximization, Ito’s formula and stochastic differential equations, the Black-Scholes equation and option pricing formula. Prerequisites: Mathematics 212 (or 222), 221, and 230 (or 340), or consent of instructor.
• Financial Derivatives (MATH 582): A rigorous introduction to financial derivatives with applications. Topics include: binomial trees and geometric Brownian motion; European options, American options, forwards, and futures; put-call parity; the Black-Scholes-Merton pricing formula and its derivations; Delta and Gamma hedging; implied volatility; Merton jump-diffusion model; Heston model; GARCH(1,1) model. Prerequisites: Math 212 (or 222) and Math 230 (or 340) or consent of instructor.
• Introduction to Algorithmic Trading – Financial Data and Modeling (MATH 585): In this course on the complexity of financial data and the challenges in modeling them students will learn a variety of financial data sets, perform research and analysis on these data, and develop mathematical models for profitable trading and investment strategies. Includes group projects designing algorithms in a live trading environment based on financial/mathematical theories. Industry guests will discuss real-world practices. Prerequisites: Linear Algebra (e.g., MATH 216, 218), Probability (e.g., MATH/STA 230, MATH 340/STA 231), Programing, preferably in Python (e.g., MATH 281L/260L). Preferred, but not required: Finance (e.g., MATH 581/ECON 673) and Linear Regression (e.g., STA 210/MATH 238L).
• Quant Methods for Bio-Data (MATH 590-02)
• Journalism in the Age of Data (PJMS 375): Teaches the tools and techniques used by investigative journalists to acquire and analyze data to discover story ideas and draw and evaluate conclusions about politicians, public policy, broader behavior of public institutions. Students should have basic familiarity with journalism concepts, but no specific technical or mathematical skills required.
• Introduction to Machine Learning and Legislative Behavior (POLSCI 189FS) †: Our goal as social scientists is to build models of the world and provide advice to policy makers. Given that human actors are often strategic and the games they play are complex, building and testing these models is difficult and distinct from common examples of machine learning. A task that is often used to motivate introductions to machine learning is teaching a model to recognize hand-written characters using MNIST data (https://www.tensorflow.org/datasets/catalog/mnist). Our task is harder: we must build models that involve forecasting human behavior ranging from votes in a legislature to changes in stock prices.
• Cybersecurity and Health Data Policy (PUBPOL 552S/SCISOC 552S): In recent years health data has expanded beyond just clinical and pharmaceutical research data to also include a broad set of information from which health observations can be inferred. This health data landscape change has caused concern that existing health privacy and cybersecurity policy frameworks like HIPAA may need modification. This class will use interactive exercises to analyze the issues of how best to optimize health data public policy for the innovative and ethical use of data to enable better health outcomes and lower costs.
• Identity, Action and Emotion (SOCIOL 176FS) †: Uses mathematical models to describe how people import cultural meanings into social interactions. Explains how people maintain identities in role relationships and group interactions. Explores a theory of how people perform normal institutional roles, respond to odd situations, and try to feel good about themselves. Uses computer simulations to model self, identity, and emotional processes. Involves reading academic literature, collecting evidence, giving research presentations, and writing a research proposal. Teaches how to think scientifically about routine and unexpected parts of everyday life.
• Data Analytics and Visualization for Business (SOCIOLOGY 223): This course gives students hands-on experience working with and analyzing data. The overarching objective is to learn to use basic statistics and quantitative modeling to understand the large amount of data that are available today.
• Data Visualization for Social Science (SOCIOLOGY 232): This course introduces modern methods and tools for the visualization of social-scientific data. The course has a theoretical and practical element. We will explore the theory and history of efforts to visualize social data, and society more generally, examining the nature and politics of data generation and consumption, and about the implications of choosing to represent it in different ways. Practically, we will learn how to use R and related tools to produce insightful, beautiful, reproducible data visualizations.
• Quantitative Analysis of Sociological Data (SOCIOL 333): Introduction to quantitative analysis in sociological research, including principles of research design and the use of empirical evidence, particularly from social surveys. Descriptive and inferential statistics, contingency table analysis, and regression analysis. Emphasis on analysis of data, interpretation and presentation of results. Not open to students who have taken another 100-level (or above) statistics course. Course restricted to first and second Sociology majors.
• Data Science and Society (SOCIOL 367S): Interdisciplinary field of computational social science, drawing from sociology, computer science, and related disciplines. Obtain skills to automate collection of social science data from new sources (Twitter, Facebook, Google, etc.), classify unstructured data into discrete variables, analyze them using a combination of techniques that includes screen-scraping, natural language processing and machine learning. Complex ethical and legal issues that arise when working with these novel sources of data. Students develop their imagination about new questions that can be asked with these new data sources. Reading and reproducing exemplary studies produced by computational social scientists.
• Regression Analysis (STA 210L): Extensive study of regression modeling. Multiple regression, weighted least squares, logistic regression, log-linear models, analysis of variance, model diagnostics and selection. Emphasis on applications. Examples drawn from a variety of fields. Prerequisite: 100-level Statistical Science course or Statistical Science 230 or Statistical Science 240L. Interested students with a different background should discuss and seek instructor consent.
• Case Studies in the Practice of Statistics (STA 440L): Students apply statistical analysis skills to in-depth data analysis projects ranging across diverse application areas including but not limited to energy, environmental sustainability, global health, information and culture, brain sciences, and social networks. Students practice cutting-edge statistical methods and communicate their results both technically and non-technically via presentations and written reports. Prerequisite: Statistical Science 360.
• Constructing Immersive Virtual Worlds (CMAC 270S): Theory, practice, and creation of 3D virtual worlds. Hands-on design and development of online collaborative simulation environments. Introduction to graphics workflow for creating virtual world media assets. Critical exploration of state-of-the-art virtual world technologies; 3D graphics, chat, voice, video, and mixed reality systems. Topics include: history/culture of virtual worlds, identity and avatars; behavioral norms; self-organizing cultures; user-generated content, virtual world economies; architectural scalability.
• User Experience and User Interface Design and Development (ARTSVIS 307): How do we build knowledge about computational, aesthetic, product and spatial experience? What tools and methods enable our work in the design of these interactions? This course applies methods and technologies found in the User Experience (UX) and User Interface (UI) disciplines to analyze, document, design and prototype a number of spatial and product interactions.
• 3D Modeling and Animation (ARTSVIS 351): Basic concepts of 3D modeling and animation; fundamentals of computer geometry; knowledge of basic tools of 3D software (Maya); introduction to modeling, animation, texturing, lighting, and rendering; combination of these techniques in a final project. Recommended prerequisite: Visual and Media Studies 206 or 396.
• Videogame Design and Critique (CMAC 355S): Surveys history, art, technology, narrative, ethics, and design of interactive computer games. Games as systems of rules, games of emergence and progression, state machines. Flow, systems of pleasure, goals, rewards, reinforcement schedules, fictional and narrative elements of game worlds. Responses to immersive & interactive media; experience of gamified systems in diverse contexts. Social, artistic, and cultural effects of games: impact, harm, benefits. Team design and development of game-design storyboards and stand-alone games. Interplay between narrative, graphics, rule systems, and artificial intelligence in the creation of interactive games. Programming experience not required.