Note: If you identify another course that you think should count as one of the electives, please reach out to Erica McFarland (erica.mcfarland@duke.edu).

• 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, COMPSCI 210D): 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.
• Logic (PHIL 150): The conditions of effective thinking and clear communication. Examination of the basic principles of deductive reasoning.
• 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.)
• Computer Architecture (COMPSCI 250D/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.
• 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.
• Computer Network Architecture (ECE 356, 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.
• 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.
• 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).
• 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.
• Theory and Methods of Statistical Learning and Inference (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).
• Topological Data Analysis (COMPSCI 434, 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 (COMPSCI 445, STA 465, MATH 465): 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.
• 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 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.
• Introduction to Deep Learning (COMPSCI 675D): 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
• 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.
• 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).
• Business Ethics: The Debate Over Corporate Social Responsibility (ETHICS 270, ICS 271, 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.

• 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)

• 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.
• 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.
• 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.
• 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.
• 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.