course_summaries.txt

African American Studies (AFRICAM) 101 - Research Methods for African American Studies <Applied>:
	As an introduction to interdisciplinary research methods as they are applied to the study of African American communities, the course will examine theoretical and conceptual issues; techniques for identifying existing research; and sources and methods of social research and data collection. The main focus will be on qualitative methods.

American Studies (AMERSTD) C134 - Information Technology and Society <Applied>:
	This course assesses the role of information technology in the digitalization of society by focusing on the deployment of e-government, e-commerce, e-learning, the digital city, telecommuting, virtual communities, internet time, the virtual office, and the geography of cyber space. The course will also discuss the role of information technology in the governance and economic development of society.

Anthropology (ANTHRO) 169A - Data Analysis and Computational Methods <Applied>:
	This course capitalizes on a successful approach of using definitional formulas to emphasize concepts of statistics, rather than rote memorization in both qualitative and quantitative anthropology. This conceptual approach constantly reminds the students of the logic behind what they are learning. Procedures are taught verbally, numerically, and visually, to reach students with different learning styles.

Anthropology (ANTHRO) 169B - Research Theory and Methods in Socio-Cultural Anthropology <Applied>:
	Introduction to research problems and research design techniques. Will involve local field research on the collection, analysis, and presentation of data. This course requires 15 hours of work per week including class time, outside work and preparation. One section meeting per week will be required.

Anthropology (ANTHRO) 169C - Research Theory and Methods in Linguistic Anthropology <Applied>:
	This course provides an introduction to selected theories and methods in Linguistic Anthropology, with a focus on topics of relevance to ethnographic fieldwork. Readings and lectures are organized into three modules: Linguistic categories and their consequences for thought, the effects of social context on meaning, and the empirical basis of research on language.

Architecture (ARCH) 154 - Design and Computer Analysis of Structure <Applied>:
	Design and analysis of whole structural building systems with the aid of finite element analytical methods. Advanced structural concepts explored in a laboratory environment.

Asian American Studies Program (ASAMST) 165 - Research Methodologies in Asian American Communities <Applied>:
	Approaches to research in the Asian American community with emphasis on the San Francisco Bay Area. Problems of research design, measurement, and data collection, processing ,and analysis will be considered.

Bioengineering (BIO ENG) 131 - Introduction to Computational Molecular and Cell Biology <Applied>:
	Topics include computational approaches and techniques to gene structure and genome annotation, sequence alignment using dynamic programming, protein domain analysis, RNA folding and structure prediction, RNA sequence design for synthetic biology, genetic and biochemical pathways and networks, UNIX and scripting languages, basic probability and information theory. Various "case studies" in these areas are reviewed; web-based computational biology tools will be used by students and programming projects will be given. Computational biology research connections to biotechnology will be explored.

Bioengineering (BIO ENG) 143 - Computational Methods in Biology <Applied>:
	An introduction to biophysical simulation methods and algorithms, including molecular dynamics, Monte Carlo, mathematical optimization, and "non-algorithmic" computation such as neural networks. Various case studies in applying these areas in the areas of protein folding, protein structure prediction, drug docking, and enzymatics will be covered. Core Specialization: Core B (Informatics and Genomics); Core D (Computational Biology); BioE Content: Biological.

Civil and Environmental Engineering (CIV ENG) 193 - Engineering Risk Analysis <Applied>:
	Applications of probability theory and statistics in planning, analysis, and design of civil engineering systems. Development of probabilistic models for risk and reliability evaluation. Occurrence models; extreme value distributions. Analysis of uncertainties. Introduction to Bayesian statistical decision theory and its application in engineering decision-making.

Civil and Environmental Engineering (CIV ENG) 88 - Data Science for Smart Cities <Applied>:
	Cities become more dependent on the data flows that connect infrastructures between themselves, and users to infrastructures. Design and operation of smart, efficient and resilient cities nowadays require data science skills. This course provides an introduction to working with data generated within transportation systems, power grids, communication networks, as well as collected via crowd-sensing and remote sensing technologies, to build demand- and supply-side urban services based on data analytics.

Civil and Environmental Engineering (CIV ENG) 93 - Engineering Data Analysis <Applied>:
	Application of the concepts and methods of probability theory and statistical inference to CEE problems and data; graphical data analysis and sampling; elements of set theory; elements of probability theory; random variables and expectation; simulation; statistical inference. Applications to various CEE problems and real data will be developed by use of MATLAB and existing codes. The course also introduces the student to various domains of uncertainty analysis in CEE.

Cognitive Science (COG SCI) 131 - Computational Models of Cognition <Applied>:
	This course will provide advanced students in cognitive science and computer science with the skills to develop computational models of human cognition, giving insight into how people solve challenging computational problems, as well as how to bring computers closer to human performance. The course will explore three ways in which researchers have attempted to formalize cognition -- symbolic approaches, neural networks, and probability and statistics -- considering the strengths and weaknesses of each.

Cognitive Science (COG SCI) 88 - Data Science and the Mind <Applied>:
	How does the human mind work? We explore this question by analyzing a range of data concerning such topics as human rationality and irrationality, human memory, how objects and events are represented in the mind, and the relation of language and cognition. This class provides young scientists with critical thinking and computing skills that will allow them to work with data in cognitive science and related disciplines.

Cognitive Science (COG SCI) C140 - Quantitative Methods in Linguistics <Applied>:
	An introduction to research using quantitative analysis in linguistics and cognitive science. Students will learn how to use the R programming environment for statistical analysis and data visualization. Also listed as Linguistics C160.

Computer Science (COMPSCI) 186 - Introduction to Database Systems <Foundational>:
	Access methods and file systems to facilitate data access. Hierarchical, network, relational, and object-oriented data models. Query languages for models. Embedding query languages in programming languages. Database services including protection, integrity control, and alternative views of data. High-level interfaces including application generators, browsers, and report writers. Introduction to transaction processing. Database system implementation to be done as term project.

Computer Science (COMPSCI) 188 - Introduction to Artificial Intelligence <Foundational>:
	Ideas and techniques underlying the design of intelligent computer systems. Topics include search, game playing, knowledge representation, inference, planning, reasoning under uncertainty, machine learning, robotics, perception, and language understanding.

Computer Science (COMPSCI) 189 - Introduction to Machine Learning <Foundational>:
	Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.

Computer Science (COMPSCI) 194-16 - Introduction to Data Science <Foundational>:
	Organizations use their data for decision support and to build data-intensive products and services. The collection of skills required by organizations to support these functions has been grouped under the term Data Science. This course will attempt to articulate the expected output of Data Scientists and then equip the students with the ability to deliver against these expectations. The assignments will involve web programming, statistics, and the ability to manipulate data sets with code.

Computer Science (COMPSCI) 61A - Structure and Interpretation of Computer Programs <Foundational>:
	Introduction to programming and computer science. This course exposes students to techniques of abstraction at several levels: (a) within a programming language, using higher-order functions, manifest types, data-directed programming, and message-passing; (b) between programming languages, using functional and rule-based languages as examples. It also relates these techniques to the practical problems of implementation of languages and algorithms on a von Neumann machine. There are several significant programming projects.

Computer Science (COMPSCI) 61B - Data Structures <Foundational>:
	Fundamental dynamic data structures, including linear lists, queues, trees, and other linked structures; arrays strings, and hash tables. Storage management. Elementary principles of software engineering. Abstract data types. Algorithms for sorting and searching. Introduction to the Java programming language.

Computer Science (COMPSCI) 70 - Discrete Mathematics and Probability Theory <Foundational>:
	Logic, infinity, and induction; applications include undecidability and stable marriage problem. Modular arithmetic and GCDs; applications include primality testing and cryptography. Polynomials; examples include error correcting codes and interpolation. Probability including sample spaces, independence, random variables, law of large numbers; examples include load balancing, existence arguments, Bayesian inference.

Computer Science (COMPSCI) 88 - Computational Structures in Data Science <Applied>:
	Introduction to computer science in the context of data science. This course provides a formal and rigorous introduction to the programming topics that appear in Foundations of Data Science, expands the repertoire of computational concepts, and exposes students to techniques of abstraction at several levels, including layers of software and machines from a programmers?__ point of view. It provides an understanding of the structures that underlie the programs, algorithms, and languages used in data science and other settings. It focuses on paradigms for controlling program complexity, such as functional programming, object-oriented programming, and declarative programming. Mastery of a particular programming language is a valuable side effect of studying these general techniques. It provides practical experience with composing larger computational systems through several significant programming projects. Students coming out of this course will be prepared to develop substantial applications and for further studies in Computer Science, including CS61B. Treatment is CS is designed for a wide range of interests.

Computer Science (COMPSCI) 9A - Matlab for Programmers <Applied>:
	Introduction to the constructs in the Matlab programming language, aimed at students who already know how to program. Array and matrix operations, functions and function handles, control flow, plotting and image manipulation, cell arrays and structures, and the Symbolic Mathematics toolbox.

Computer Science (COMPSCI) 9C - C for Programmers <Applied>:
	Self-paced course in the C programming language for students who already know how to program. Computation, input and output, flow of control, functions, arrays, and pointers, linked structures, use of dynamic storage, and implementation of abstract data types.

Computer Science (COMPSCI) C8 - Foundations of Data Science <Foundational>:
	Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.

Computer Science (COMSCI) 61C - Machine Structures <Applied>:
	The internal organization and operation of digital computers. Machine architecture, support for high-level languages (logic, arithmetic, instruction sequencing) and operating systems (I/O, interrupts, memory management, process switching). Elements of computer logic design. Tradeoffs involved in fundamental architectural design decisions.

Course Dept Course # - Course Name <Foundational>:
	Description

Demography (DEMOG) 110 - Introduction to Population Analysis <Applied>:
	Measures and methods of Demography. Life tables, fertility and nuptiality measures, age pyramids, population projection, measures of fertility control.

Demography (DEMOG) 5 - Fundamentals of Population Science <Applied>:
	This course provides an accessible introduction to the social science of demography. The course is organized around cases in which population issues raise policy or ethical dilemmas (example: China's one child policy). Through these cases, students will learn how demographers use models and data to acquire knowledge about population. Throughout the course, students will also learn to read, interpret, evaluate, and produce tabular and graphical representations of population data.

Demography (DEMOG) C126 - Social Consequences of Population Dynamics <Applied>:
	Introduction to population issues and the field of demography, with emphasis on historical patterns of population growth and change during the industrial era. Topics covered include the demographic transition, resource issues, economic development, the environment, population control, family planning, birth control, family and gender, aging, intergenerational transfers, and international migration.

Demography (DEMOG) C164 - Impact of Government Policies on Poor Children and Families <Applied>:
	Examination of the impact of policies of state intervention and public benefit programs on poor children and families. Introduction to child and family policy, and study of specific issue areas, such as income transfer programs, housing, health care, and child abuse.

Demography (DEMOG) C175 - Economic Demography <Applied>:
	A general introduction to economic demography, addressing the following kinds of questions: What are the economic consequences of immigration to the U.S.? Will industrial nations be able to afford the health and pension costs of the aging populations? How has the size of the baby boom affected its economic well being? Why has fertility been high in Third World countries? In industrial countries, why is marriage postponed, divorce high, fertility so low, and extramarital fertility rising? What are the economic and environmental consequences of rapid population growth?

Earth and Planetary Science (EPS) 104 - Mathematical Methods in Geophysics <Applied>:
	Linear systems. Linear inverse problems, least squares; generalized inverse, resolution; Fourier series, integral transforms; time series analysis, spherical harmonics; partial differntial equations of geophysics; functions of a complex variable; probability and significance tests, maximum likelihood methods. Intended for students in geophysics and other physical sciences.

Earth and Planetary Science (EPS) 109 - Computer Simulations in Earth and Planetary Sciences <Applied>:
	Introduction to modern computer simulation methods and their application to selected Earth and Planetary Science problems. In hands-on computer labs, students will learn about numerical algorithms, learn to program and modify provided programs, and display the solution graphically. This is an introductory course and no programming experience is required. Examples include fractals in geophysics, properties of materials at high pressure, celestial mechanics, and diffusion processes in the Earth. Topics range from ordinary and partial differential equations to molecular dynamics and Monte Carlo simulations.

Economics (ECON) 140 - Economic Statistics and Econometrics <Applied>:
	Introduction to problems of observation, estimation, and hypothesis testing in economics. This course covers the linear regression model and its application to empirical problems in economics.

Economics (ECON) 141 - Econometric Analysis <Applied>:
	Introduction to problems of observation, estimation, and hypothesis testing in economics. This course covers the statistical theory for the linear regression model and its variants, with examples from empirical economics.

Economics (ECON) 174 - Global Poverty and Impact Evaluation <Applied>:
	Rather than simply describing the causes and symptoms of global poverty, this course will explore the variety of tools available for rigorously measuring the impact of development programs. Through weekly case studies of field research, the course will cover impact evaluation theory and methods. The course will culminate with a final project in which each student will design an impact evaluation of a policy or intervention.

Economics (ECON) C103 - Introduction to Mathematical Economics <Applied>:
	Selected topics illustrating the application of mathematics to economic theory. This course is intended for upper-division students in Mathematics, Statistics, the Physical Sciences, and Engineering, and for economics majors with adequate mathematical preparation. No economic background is required.

Economics (ECON) C142 - Applied Econometrics and Public Policy <Applied>:
	This course focuses on the sensible application of econometric methods to empirical problems in economics and public policy analysis. It provides background on issues that arise when analyzing non-experimental social science data and a guide for tools that are useful for empirical research. By the end of the course, students will have an understanding of the types of research designs that can lead to convincing analysis and be comfortable working with large scale data sets.

Economics (ECON) C175 - Economic Demography <Applied>:
	A general introduction to economic demography, addressing the following kinds of questions: What are the economic consequences of immigration to the U.S.? Will industrial nations be able to afford the health and pension costs of the aging populations? How has the size of the baby boom affected its economic well being? Why has fertility been high in Third World countries? In industrial countries, why is marriage postponed, divorce high, fertility so low, and extramarital fertility rising? What are the economic and environmental consequences of rapid population growth?

Electrical Engineering (EL ENG) 126 - Probability and Random Processes <Foundational>:
	This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains.

Electrical Engineering (EL ENG) 127 - Optimization Models in Engineering <Applied>:
	This course offers an introduction to optimization models and their applications, ranging from machine learning and statistics to decision-making and control, with emphasis on numerically tractable problems, such as linear or constrained least-squares optimization.

Electrical Engineering (EL ENG) 129 - Neural and Nonlinear Information Processing <Applied>:
	Principles of massively parallel real-time computation, optimization, and information processing via nonlinear dynamics and analog VLSI neural networks, applications selected from image processing, pattern recognition, feature extraction, motion detection, data compression, secure communication, bionic eye, auto waves, and Turing patterns.

Electrical Engineering (EL ENG) 144 - Fundamental Algorithms for Systems Modeling, Analysis, and Optimization <Foundational>:
	The modeling, analysis, and optimization of complex systems requires a range of algorithms and design software. This course reviews the fundamental techniques underlying the design methodology for complex systems, using integrated circuit design as example. Topics include design flows, discrete and continuous models and algorithms, and strategies for implementing algorithms efficiently and correctly in software. Laboratory assignments and a class project will expose students to state-of-the-art tools.

Engineering (ENGIN) 117 - Methods of Engineering Analysis <Applied>:
	Methods of theoretical engineering analysis; techniques for analyzing partial differential equations and the use of special functions related to engineering systems. Sponsoring Department: Mechanical Engineering.

Engineering (ENGIN) 120 - Principles of Engineering Economics <Applied>:
	Economic analysis for engineering decision making: Capital flows, effect of time and interest rate. Different methods of evaluation of alternatives. Minimum-cost life and replacement analysis. Depreciation and taxes. Uncertainty; preference under risk; decision analysis. Capital sources and their effects. Economic studies.

Engineering (ENGIN) 170 - Introduction to Modeling and Simulation <Foundational>:
	Introduces concepts of computational modeling and simulation, using multidisciplinary projects drawn from biology, chemistry, applied mathematics, and physics, and all areas of engineering. Models progress sequentially through problem statement, mathematical model, approximations and analytic solution, discrete model, object-oriented model, implementation and simulation, visualization, comparison to analysis, experiment and observation. Includes a broad survey of simulation techniques.

Engineering (ENGIN) 177 - Advanced Programming with MATLAB <Applied>:
	The course builds an understanding, demonstrates engineering uses, and provides hand-on experience for object-oriented programming as well as exposes a practical knowledge of advanced features available in MATLAB. The course will begin with a brief review of basic MATLAB features and quickly move to class organization and functionality. The introduced concepts are reinforced by examining the advanced graphical features of MATLAB. The material will also include the effective use of programs written in C and FORTRAN, and will cover SIMULINK, a MATLAB toolbox providing for an effective ways of model simulations. Throughout the course, the emphasis will be placed on examples and homework assignments from engineering disciplines.

Engineering (ENGIN) 180A - Computational Engineering Science Modeling and Simulation I/II <Foundational>:
	This course sequence focuses on the concepts of computational modeling and simulation. Concepts are illustrated with projects drawn from the multidisciplinary areas of computational engineering science. Areas covered span biology, chemistry, applied mathematics, and physics, as well as all areas of engineering. Models will progress sequentially through problem statement, mathematical model, approximations and analytic solution, discrete model, object-oriented model, implementation and simulation, visualization, and comparison to analysis, experimentation and observation.

Engineering (ENGIN) 7 - Introduction to Computer Programming for Scientists and Engineers <Applied>:
	Elements of procedural and object-oriented programming. Induction, iteration, and recursion. Real functions and floating-point computations for engineering analysis. Introduction to data structures. Representative examples are drawn from mathematics, science, and engineering. The course uses the MATLAB programming language. Sponsoring departments: Civil and Environmental Engineering and Mechanical Engineering.

Environmental Design (ENV DES) 100 - The City: Theories and Methods in Urban Studies <Applied>:
	This course is concerned with the study of cities. Focusing on great cities around the world - from Chicago to Los Angeles, from Rio to Shanghai, from Vienna to Cairo it covers of historical and contemporary patterns of urbanization and urbanism. Through these case studies, it introduces the key ideas, debates, and research genres of the interdisciplinary field of urban studies. In other words, this is simultaneously a "great cities" and "great theories" course. Its purpose is to train students in critical analysis of the socio-spatial formations of their lived world.

Environmental Design (ENV DES) 4B - Global Cities <Applied>:
	This study of cities is more important than ever; for the first time in history more people live in urban than rural areas, and cities will account for all of the world's population growth for at least the next half-century. We will explore the challenges facing global cities in the 21st Century and expose students to some of the key texts, theories, and methods of inquiry that shape the built environment, from the human scale of home and community to the regional scale of the megacity.

Environmental Economics and Policy (ENVECON) 131 - Globalization and the Natural Environment <Applied>:
	An examination of the environmental effects of globalization. How has increased international trade, the integration of factor markets, and the adoption of international agreements affected the environment? Case studies include the environmental impact of GATT/WTO and NAFTA. Multi-disciplinary approach examines the actual laws and institutions and the economic theories of globalization, in addition to the empirical evidence of globalization's environmental effects.

Environmental Economics and Policy (ENVECON) 140AC - Economics of Race, Agriculture, and the Environment <Applied>:
	This course examines whether and how economic processes explain shifting formations of race and differential experiences among racial groups in U.S. agricultural and environmental systems. It approaches economic processes as organizing dynamics of racial differentiation and integration, and uses comparative experience among different racial and ethnic groups as sources of evidence against which economic theories of differentiation and integration can be tested.

Environmental Economics and Policy (ENVECON) C118 - Introductory Applied Econometrics <Applied>:
	Formulation of a research hypothesis and definition of an empirical strategy. Regression analysis with cross-sectional and time-series data; econometric methods for the analysis of qualitative information; hypothesis testing. The techniques of statistical and econometric analysis are developed through applications to a set of case studies and real data in the fields of environmental, resource, and international development economics. Students learn the use of a statistical software for economic data analysis. Also listed as International and Area Studies C118.

Environmental Economics and Policy (ENVECON) C180 - Ecological Economics in Historical Context <Applied>:
	Economists through history have explored economic and environmental interactions, physical limits to growth, what constitutes the good life, and how economic justice can be assured. Yet economists continue to use measures and models that simplify these issues and promote bad outcomes. Ecological economics responds to this tension between the desire for simplicity and the multiple perspectives needed to understand complexity in order to move toward sustainable, fulfilling, just economies.

Environmental Science, Policy, and Management (ESPM) 100 - Introduction to the Methods of Environmental Science <Applied>:
	Introduction to basic methods used in environmental research by biological, physical, and social scientists; designed to teach skills necessary to conduct independent thesis research in the required senior seminar, 196A-196B/196L. Topics include development of research questions, sampling methods, experimental design, statistical analysis, scientific writing and graphics, and introductions to special techniques for characterizing environmental conditions and features. This course is the prerequisite to 196A.

Environmental Science, Policy, and Management (ESPM) 173 - Introduction to Ecological Data Analysis <Applied>:
	Introduces concepts and methods for practical analysis of data from ecology and related disciplines. Topics include data summaries, distributions, and probability; comparison of data groups using t-tests and analysis of variance; comparison of multi-factor groups using analysis of variance; evaluation of continuous relationships between variables using regression and correlation; and a glimpse at more advanced topics. In computer laboratories, students put concepts into practice and interpret results.

Environmental Science, Policy, and Management (ESPM) 88A - Exploring Geospatial Data <Applied>:
	From interactive web maps to spatial data analysis, digital geographic data and information are becoming an important part of the data science landscape. Almost everything happens somewhere that can be mapped on the surface of the earth. In many cases the where matters as much to an analysis as the what and the why. Geospatial data analysis allows a researcher to consider location explicitly. This course provides an introduction to working with digital geographic data, or geospatial data. We will explore concepts of geospatial data representation, methods for acquisition, processing and analysis, and techniques for creating compelling geovisualizations. No prior knowledge is assumed or expected.

Environmental Science, Policy, and Management (ESPM) 88B - Data Sciences in Ecology and the Environment <Applied>:
	Many of the greatest challenges we face today come from understanding and interacting with the natural world: from global climate change to the sudden collapse of fisheries and forests, from the spread of disease and invasive species to the unknown wealth of medical, cultural, and technological value we derive from nature. Advances in satellites and micro-sensors, computation, informatics and the Internet have made available unprecedented amounts of data about the natural world, and with it, new challenges of sifting, processing and synthesizing large and diverse sources of information. In this course, students will apply methods and understanding they gain in the Foundations course to real-world ecological and environmental data sets. Through this hands-on approach, students will learn more about issues in the natural world while also developing the practical skills for working with heterogeneous real-world data encountered in all areas of data science.

Environmental Science, Policy, and Management (ESPM) C177 - GIS and Environmental Spatial Data Analysis <Applied>:
	This course offers an introduction to spatial data analysis. It integrates ArcGIS analysis with spatial statistical analysis for the study of pattern and process applicable to a wide variety of fields. Major topics covered include: spatial sampling, processing data with ARC Info, exploratory GIS analysis, spatial decomposition, spatial point patterns and Ripley's K function, spatial autocorrelation, geostatistics, spatially weighted regression, spatial autoregression, generalized linear models and generalized linear mixed models.

Geography (GEOG) 187 - Geographic Information Analysis <Applied>:
	A spatial analytic approach to digital mapping and GIS. Given that recording the geolocation of scientific, business and social data is now routine, the question of what we can learn from the spatial aspect of data arises. This class looks at challenges in analyzing spatial data, particularly scale and spatial dependence. Various methods are considered such as hotspot detection, interpolation, and map overlay. The emphasis throughout is hands on and practical rather than theoretical.

History (HIST) 88 - How Does History Count? <Applied>:
	We will explore how historical data becomes historical evidence and how recent technological advances affect long-established practices, such as close attention to historical context and contingency. Will the advent of fast computing and big data make history count more or lead to unprecedented insights into the study of change over time? During our weekly discussions, we will apply what we learn in lectures and labs to the analysis of selected historical sources and get an understanding of constructing historical datasets. We will also consider scholarly debates over quantitative evidence and historical argument.

Industrial Engineering and Operations Research (IND ENG) 115 - Databases <Foundational>:
	Design and implementation of databases, with an emphasis on industrial and commercial applications. Relational algebra, SQL, normalization. Students work in teams with local companies on a database design project. WWW design and queries.

Industrial Engineering and Operations Research (IND ENG) 130 - Methods of Manufacturing Improvement <Applied>:
	Analytical techniques for the improvement of manufacturing performance along the dimensions of productivity, quality, customer service, and throughput. Techniques for yield analysis, process control, inspection sampling, equipment efficiency analysis, cycle time reduction, and on-time delivery improvement. Applications on semiconductor manufacturing or other industrial settings.

Industrial Engineering and Operations Research (IND ENG) 131 - Discrete Event Simulation <Foundational>:
	Free communication has changed the world, including the expectations and work and play. The class begins with the two data revolutions--the first about passively collected clicks on the web, the second about actively contributed data, as platforms like Facebook empower individuals to contribute a variety of quantitative and qualitative data (transactions, social relations, attention gestures, intention, location, and more.) With active student participation, we explore the far-reaching implications of the consumer data revolution for individuals, communities, business, and society.

Industrial Engineering and Operations Research (IND ENG) 162 - Linear Programming and Network Flows <Applied>:
	This course addresses modeling and algorithms for optimization of linear constrained optimization problems. The simplex method; theorems of duality; complementary slackness. Applications in production planning and resource allocation. Graph and network problems as linear programs with integer solutions. Algorithms for selected network flow problems. Transportation and logistics problems. Dynamic programming and its role in applications to shortest paths, project management and equipment replacement.

Industrial Engineering and Operations Research (IND ENG) 165 - Engineering Statistics, Quality Control, Forecasting <Applied>:
	This course will introduce students to basic statistical techniques such as parameter estimation, hypothesis testing, regression analysis, analysis of variance. Specific applications in forecasting and quality control will be considered in detail.

Industrial Engineering and Operations Research (IND ENG) 172 - Probability and Risk Analysis for Engineers <Applied>:
	This course will introduce students to basic statistical techniques such as parameter estimation, hypothesis testing, regression analysis, analysis of variance. Specific applications in forecasting and quality control will be considered in detail.

Information (INFO) 155 - Introduction to High-Level Programming <Applied>:
	An introduction to high-level computer programming languages with emphasis on strings, modules, functions and objects; sequential and event-based programming. Uses the PYTHON language.

Information (INFO) 88 - Data and Ethics <Meta>:
	This course provides an introduction to critical and ethical issues surrounding data and society. It blends social and historical perspectives on data with ethics, policy, and case examples from Facebook's Emotional Contagion experiment to search engine algorithms to self-driving cars to help students develop a workable understanding of current ethical issues in data science. Ethical and policy-related concepts addressed include: research ethics; privacy and surveillance; data and discrimination; and the black box of algorithms. Importantly, these issues will be addressed throughout the lifecycle of data from collection to storage to analysis and application.

Interdisciplinary Studies Field Major (ISF) C145 - GIS and Environmental Spatial Data Analysis <Applied>:
	This course offers an introduction to spatial data analysis. It integrates ArcGIS analysis with spatial statistical analysis for the study of pattern and process applicable to a wide variety of fields. Major topics covered include: spatial sampling, processing data with ARC Info, exploratory GIS analysis, spatial decomposition, spatial point patterns and Ripley's K function, spatial autocorrelation, geostatistics, spatially weighted regression, spatial autoregression, generalized linear models and generalized linear mixed models. accompany the formation of multicultural Europe. This involves (1) an examination of the traditional concepts of nationhood and citizenship, and (2)a study of the Europeanization of culture. Also listed as History C176 and Geography C152.

International and Area Studies (IAS) 102 - Scope and Methods of Research in International and Area Studies <Applied>:
	Required prerequisite for all students intending to enroll in Development Studies H195 and Political Economy of Industrial Societies H195. Introduction to interdisciplinary research strategies for the collection, interpretation, and analysis of data. Course integrates the study of the fundamental theories of social science with the practical techniques of social science research methods.

Letters and Science (L & S) 88-1 - Health, Human Behavior, and Data <Applied>:
	Humans, especially we older ones, are obsessed with good health and longevity, and we are willing to pay for it. As a nation, Americans spend 17% of their incomes on health care, and that share has generally been rising above and beyond what one would expect based on aging of the population alone. In an era when the longevity gap between rich and poor may be widening, we are keenly interested in understanding and preventing health inequalities by improving the health of the disadvantaged. But what external elements and human behaviors produce good health? What kinds of influences reduce health? Is there a difference between activities that we observe healthy people engaging in and activities that actually improve health? The gold standard for disentangling cause and effect in medicine is the randomized controlled trial. But we suspect that many social and behavioral phenomena are important for population health but are never administered in specific dosages to randomly selected treatment and control groups. In this first year connector course, we will examine and discuss measures of human health and longevity alongside arrays of measurable influences on health, identifying the key questions traditionally addressed in health sciences and exploring the current frontier. We will develop broad knowledge of the metrics, methods, and challenges, and we will apply them toward understanding of current issues in health policy.

Letters and Science (L & S) 88-2 - Literature and Data <Applied>:
	From our off-hand Tweets to the well-wrought urn of poetry, text functions both as a device for communication and a way of examining the world around us. We use text to lay out our thoughts in argumentative essays, speeches, and novels that have power of influence at the grand-scale of politics and at the personal scale of our selves. However, vast reams of this text lie apparently beyond our reach: it would be as difficult to sit down and read every blog post from a given day as it would be to read every novel in the library. Data science opens new avenues to read at previously untold scale, but if we did read every novel, would that change which ones we thought were important? Would we have to learn a different kind of reading all together? In this course, we will apply methods learned in Foundations of Data Science to sets of literary texts in order to expand our reading practices. This humanities-oriented approach will require us to think about the limits of both new and traditional reading methods and how we make arguments based on data.

Linguistics (LINGUIS) 140 - Introduction to Field Methods <Applied>:
	Training in the discrimination and transcription of the sounds of a particular language. Methods and practice in collecting and processing data from a particular language.

Linguistics (LINGUIS) 141 - Empiricism and Linguistics <Applied>:
	This course considers the status of linguistics as a scientific field of inquiry. Methodological approaches and the type of information that serve as data in linguistics are surveyed and placed in the context of other social science methodology and data. Throughout the course, the practice of linguistics as the science of language, its successes and weaknesses, are placed in the context of thought on the philosophy of science. Students design and carry out projects using subject methodologies (introspection, corpus, statistical, fieldwork, experimental).

Linguistics (LINGUIS) 158 - Computational Linguistics <Applied>:
	A survey of computational areas and methods in linguistics. Topics include the Chomsky hierarchy, finite-state transducers, context-free grammars, parsing, unification, two-level phonology, computational morphology, human sentence processing, garden path sentences, lexical access, ambiguity, connectionism, probabilistic algorithms, computational semantics, and computational reconstruction.

Mass Communications (MASSCOM) 130 - Research Methods in Media Studies <Applied>:
	This course is intended to familiarize students with some of the primary research methods used to study mass media texts and audiences (and the relationship between the two). Because the field of media studies has historical roots in both the social sciences and humanities, the course will cover both quantitative and qualitative approaches to communications research. Course readings will describe research methods, offer examples of research projects and findings, and present critiques of research studies and methods. 

Mathematics (MATH) 127 - Mathematical and Computational Methods in Molecular Biology <Applied>:
	Introduction to mathematical and computational problems arising in the context of molecular biology. Theory and applications of combinatorics, probability, statistics, geometry, and topology to problems ranging from sequence determination to structure analysis.

Mathematics (MATH) 128A - Numerical Analysis <Foundational>:
	Programming for numerical calculations, round-off error, approximation and interpolation, numerical quadrature, and solution of ordinary differential equations. Practice on the computer.

Mathematics (MATH) 170 - Mathematical Methods for Optimization <Foundational>:
	The modeling, analysis, and optimization of complex systems requires a range of algorithms and design software. This course reviews the fundamental techniques underlying the design methodology for complex systems, using integrated circuit design as example. Topics include design flows, discrete and continuous models and algorithms, and strategies for implementing algorithms efficiently and correctly in software. Laboratory assignments and a class project will expose students to state-of-the-art tools.

Mathematics (MATH) 191 - Experimental Courses in Mathematics <Applied>:
	The topics to be covered and the method of instruction to be used will be announced at the beginning of each semester that such courses are offered.

Mathematics (MATH) 54 - Linear Algebra and Differential Equations <Foundational>:
	Basic linear algebra; matrix arithmetic and determinants. Vector spaces; inner product as spaces. Eigenvalues and eigenvectors; linear transformations. Homogeneous ordinary differential equations; first-order differential equations with constant coefficients. Fourier series and partial differential equations.

Mathematics (MATH) 98 - Introduction to MATLAB programming <Applied>:
	A 1 unit P/NP class related to Math 128A and MATH 170 that covers basic programming in MATLAB. No prior programming experience is needed. See schedule for topics. Students from MATH 170 are encouraged to enroll in class.

Mechanical Engineering (MEC ENG) 102A - Introduction to Mechanical Systems for Mechatronics <Applied>:
	The objectives of this course are to introduce students to modern experimental techniques for mechanical engineering, and to improve students' written and oral communication skills. Students will be provided exposure to, and experience with, a variety of sensors used in mechatronic systems including sensors to measure temperature, displacement, velocity, acceleration and strain. The role of error and uncertainty in measurements and analysis will be examined. Students will also be provided exposure to, and experience with, using commercial software for data acquisition and analysis. The role and limitations of spectral analysis of digital data will be discussed.

Mechanical Engineering (MEC ENG) 120 - Computational Biomechanics Across Multiple Scales <Applied>:
	This course applies the methods of computational modeling and continuum mechanics to biomedical phenomena spanning various length scales ranging from molecular to cellular to tissue and organ levels. The course is intended for upper level undergraduate students who have been exposed to undergraduate continuum mechanics (statics and strength of materials.)

Mechanical Engineering (MEC ENG) C180 - Engineering Analysis Using the Finite Element Method <Applied>:
	This is an introductory course on the finite element method and is intended for seniors in engineering and applied science disciplines. The course covers the basic topics of finite element technology, including domain discretization, polynomial interpolation, application of boundary conditions, assembly of global arrays, and solution of the resulting algebraic systems. Finite element formulations for several important field equations are introduced using both direct and integral approaches. Particular emphasis is placed on computer simulation and analysis of realistic engineering problems from solid and fluid mechanics, heat transfer, and electromagnetism. 

Media Studies (MEDIAST) 130 - Research Methods in Media Studies <Applied>:
	This course is intended to familiarize students with some of the primary research methods used to study mass media texts and audiences (and the relationship between the two). Because the field of media studies has historical roots in both the social sciences and humanities, the course will cover both quantitative and qualitative approaches to communications research. Course readings will describe research methods, offer examples of research projects and findings, and present critiques of research studies and methods. Course assignments will involve designing and conducting a series of sample projects on a single topic of the student's choosing in order to gain a fuller understanding of various research methods and their limitations and strengths. There are five separate research projects on the syllabus; students must complete the first project and may conduct any three of the remaining four projects. Students must present and discuss their research findings for one project to the class.

Middle Eastern Studies (M E STU) 102 - Scope and Methods of Research in Middle Eastern Studies <Applied>:
	Required for all students majoring in Middle Eastern Studies, open to all students in International and Area Studies Teaching Program focusing on the Middle East interdisciplinary research strategies for the collection, interpretation, and analysis of data. Course integrates the study of the fundamental theories of social science, with the practical techniques of social science research methods.

Molecular and Cell Biology (MCELLBI) 137 - Computer Simulation in Biology <Applied>:
	Modeling and computer simulation of dynamic biological processes using special graphical interfaces requiring very little mathematical or computer experience. Models are drawn from the current literature to teach concepts and technique. The later part of the course is a workshop for student-selected individual projects. Computer work may be done at home or in the university laboratory.

Molecular and Cell Biology (MCELLBI) 166 - Biophysical Neurobiology <Applied>:
	Electrochemistry and ion transport phenomena, equivalent circuits, excitability, action potentials, voltage clamp and the Hodgkin-Huxley model. Biophysical properties of ion channels. Statistical and electrophysiological models of synaptic transmission, Quantitative models for dendritic structure and neuronal morphogenesis. Sensory transduction, cellular networks as computational devices, information processing and transfer.

Molecular and Cell Biology (MCELLBI) C148 - Microbial Genomics and Genetics <Applied>:
	Course emphasizes bacterial and archaeal genetics and comparative genomics. Genetics and genomic methods used to dissect metabolic and development processes in bacteria, archaea, and selected microbial eukaryotes. Genetic mechanisms integrated with genomic information to address integration and diversity of microbial processes. Introduction to the use of computational tools for a comparative analysis of microbial genomes and determining relationships among bacteria, archaea, and microbial eukaryotes.

Native American Studies (NATAMST) 110 - Theories and Methods in Native American Studies <Applied>:
	Overview of literary theory and criticism, historiography, and social sciences theories and methods useful in the study of Native American literature, history and contemporary tribal groups. Course will develop skills of information gathering and development of theories that structure information.

Nuclear Engineering (NUC ENG) 130 - Analytical Methods for Non-proliferation <Applied>:
	Use of nuclear measurement techniques to detect clandestine movement and/or possession of nuclear materials by third parties. Nuclear detection, forensics, signatures, and active and passive interrogation methodologies will be explored. Techniques currently deployed for arms control and treaty verification will be discussed. Emphasis will be placed on common elements of detection technology from the viewpoint of resolution of threat signatures from false positives due to naturally occurring radioactive material. Laboratory will involve experiments conducted in the Nucleonics Laboratory featuring passive and active neutron signals, gamma ray detection, fission neutron multiplicity, and U and Pu isotopic identification and age determination. Students should be familiar with alpha, beta, gamma, and neutron radiation and basic concepts of nuclear fission.

Nuclear Engineering (NUC ENG) 175 - Methods of Risk Analysis <Applied>:
	Methodological approaches for the quantification of technological risk and risk based decision making. Probabilistic safety assessment, human health risks, environmental and ecological risk analysis.

Nutritional Sciences and Toxicology (NUSCTX) 121 - Computational Toxicology <Applied>:
	Introducing the use of bioinformatics tools useful in linking the molecular structure of chemicals to the toxicity they induce in biological systems. Discussions on the highly interactive process of collecting, organizing, and assimilating chemistry and toxicology information - and the use of computer programs to visualize, browse, and interpret this information to discover chemical structure-toxicity correlations. The importance of these concepts in drug discovery and development and food safety will be emphasized.

Nutritional Sciences and Toxicology (NUSCTX) 170 - Experimental Nutrition Laboratory <Applied>:
	Basic principles and techniques used in human and animal nutrition research. Students design, execute, and analyze experiments.

Philosophy (PHILOS) 100 - Philosophical Methods <Applied>:
	The course is designed to acquaint students with the techniques of philosophical reasoning through detailed study of selected philosophical texts and through extensive training in philosophical writing, based on those texts.

Philosophy (PHILOS) 141 - Philosophy and Game Theory <Applied>:
	An exploration of how game theory and rational choice theory shed light on traditional philosophical problems; and of new paradoxes and problems introduced by these theories.

Physics (PHYSICS) 77 - Introduction to Computational Techniques in Physics <Applied>:
	Introductory scientific programming in Python with examples from physics. Topics include: visualization, statistics and probability, regression, numerical integration, simulation, data modeling, function approximation, and algebraic systems. Recommended for freshman physics majors. 

Psychology (PSYCH) 10 - Research and Data Analysis in Psychology <Applied>:
	The class covers research design, statistical reasoning, and statistical methods appropriate for psychological research. Topics covered in research design include the scientific method, experimental versus correlational designs, controls and placebos, within and between subject designs and temporal or sequence effects. Topics covered in statistics include descriptive versus inferential statistics, linear regression and correlation and univariate statistical tests: t-test, one way and two-way ANOVA, chi-square test. The class also introduces non-parametric tests and modeling. Prospective Psychology majors need to take this course to be admitted to the major.

Psychology (PSYCH) 101 - Research and Data Analysis in Psychology <Applied>:
	The course will concentrate on hypothesis formulation and testing, tests of significance, analysis of variance (one-way analysis), simple correlation, simple regression, and nonparametric statistics such as chi-square and Mann-Whitney U tests. Majors intending to be in the honors program must complete 101 by the end of their junior year.

Public Health (PB HLTH) 140 - Introduction to Risk and Demographic Statistics <Applied>:
	Statistical and evaluation methods in studies of human mortality, morbidity, and natality. History of statistical terminology and notation, critical appraisal of registry and census data, measurement of risk and introduction to life tables. Computational systems and the analysis of mass data.

Public Health (PB HLTH) 141 - Introduction to Biostatistics <Applied>:
	An intensive introductory course in statistical methods used in applied research. Emphasis on principles of statistical reasoning, underlying assumptions, and careful interpretation of results. Topics covered: descriptive statistics, graphical displays of data, introduction to probability, expectations and variance of ramdom variables, confidence intervals and tests for means, differences of means, proportions, differences of proportions, chi-square tests for categorical variables, regression and multiple regression, an introduction to analysis of variance. Statistical software will be used to supplement hand calculation. Students who successfully complete Public Health 141 are prepared to continue their biostatistics course work in 200-level courses. With the approval of their degree program, MPH students may use Public Health 141 to fulfill the biostatistics course requirement (contact program manager for approval). Public Health 141 also fulfills the biostatistics course requirement for the Public Health Undergraduate Major.

Public Health (PB HLTH) 142 - Introduction to Probability and Statistics in Biology and Public Health <Applied>:
	Descriptive statistics, probability, probability distributions, point and interval estimation, hypothesis testing, chi-square, correlation and regression with biomedical applications.

Public Health (PB HLTH) 144A - Introduction to SAS Programming <Applied>:
	This course is intended to serve as an introduction to the SAS programming language for Windows in an applied, workshop environment. Emphasis is on data management and programming in a public health research setting. Topics include SAS language to compute, recode, label, and format variables as well as sort, subset, concatenate, and merge data sets. SAS statistical procedures will be used to compute univariate and bivariate summary statistics and tests, simple linear models,graphical plots, and statistical output data sets.

Public Health (PB HLTH) 144B - Introduction to SAS Programming <Applied>:
	Topics include data step flow control, looping and automated processing, implicit and explicit arrays, data simulation strategies, data set reconfiguration, use of SAS Macro variables, and writing simple SAS Macro programs.

Public Health (PB HLTH) 145 - Statistical Analysis of Continuous Outcome Data <Applied>:
	Regression models for continuous outcome data: least squares estimates and their properties, interpreting coefficients, prediction, comparing models, checking model assumptions, transformations, outliers, and influential points. Categorical explanatory variables: interaction and analysis of covariance, correlation and partial correlation. Appropriate graphical methods and statistical computing. Analysis of variance for one- and two-factor models: F tests, assumption checking, multiple comparisons. Random effects models and variance components. Introduction to repeated measures models.

Sociology (SOCIOL) 105 - Research Design and Sociological Methods <Applied>:
	Problems of research design, measurement, and data collection, processing, and analysis will be considered. Attention will be given to both qualitative and quantitative studies.

Sociology (SOCIOL) 106 - Quantitative Sociological Methods <Applied>:
	This course will cover more technical issues in quantitative research methods, and will include, according to discretion of instructor, a practicum in data collection and/or analysis. Recommended for students interested in graduate work in sociology or research careers.

Sociology (SOCIOL) 108 - Advanced Methods: In-depth Interviewing <Applied>:
	Scientists regularly gather data through observation. Sociologists can go a step further and ask the objects of their studies about their lives and thoughts. This upper-level course teaches students how to engage in scientific research using question-based data. It involves a mix of classroom and hands-on learning, culminating in an independent research paper.

Sociology (SOCIOL) 5 - Evaluation of Evidence <Applied>:
	A review of methodological problems in assessing data relating to social life. Topics to be covered include: posing a sociological problem, gaining access to data, measuring, establishing correlation and causal connection among data, and relating data to theoretical context. 

Sociology (SOCIOL) 7 - The Power of Numbers: Quantitative Data in Social Sciences <Applied>:
	This course will provide students with a set of skills to understand, evaluate, use, and produce quantitative data about the social world. It is intended specifically for social science majors, and focuses on social science questions. Students will learn to: produce basic graphs, find good-quality and relevant data on the web, manipulate data in a spreadsheet, including producing pivot tables, understand and calculate basic statistical measures of central tendency, variation, and correlation, understand and apply basic concepts of sampling and selection, and recognize an impossible statistic.

Statistics (STAT) 131A - Introduction to Probability and Statistics for Life Scientists <Foundational>:
	Ideas for estimation and hypothesis testing basic to applications, including an introduction to probability. Linear estimation and normal regression theory.

Statistics (STAT) 133 - Concepts of Computing with Data <Foundational>:
	An introduction to computationally intensive applied statistics. Topics will include organization and use of databases, visualization and graphics, statistical learning and data mining, model validation procedures, and the presentation of results.

Statistics (STAT) 134 - Concepts of Probability <Foundational>:
	An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.

Statistics (STAT) 135 - Concepts of Statistics <Foundational>:
	A comprehensive survey course in statistical theory and methodology. Topics include descriptive statistics, maximum likelihood estimation, non-parametric methods, introduction to optimality, goodness-of-fit tests, analysis of variance, bootstrap and computer-intensive methods and least squares estimation. The laboratory includes computer-based data-analytic applications to science and engineering.

Statistics (STAT) 151A - Linear Modeling: Theory and Applications <Applied>:
	A coordinated treatment of linear and generalized linear models and their application. Linear regression, analysis of variance and covariance, random effects, design and analysis of experiments, quality improvement, log-linear models for discrete multivariate data, model selection, robustness, graphical techniques, productive use of computers, in-depth case studies.

Statistics (STAT) 152 - Sampling Surveys <Foundational>:
	Theory and practice of sampling from finite populations. Simple random, stratified, cluster, and double sampling. Sampling with unequal probabilities. Properties of various estimators including ratio, regression, and difference estimators. Error estimation for complex samples.

Statistics (STAT) 153 - Time Series <Foundational>:
	An introduction to time series analysis in the time domain and spectral domain. Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra.

Statistics (STAT) 154 - Modern Statistical Prediction and Machine Learning <Foundational>:
	Theory and practice of statistical prediction. Contemporary methods as extensions of classical methods. Topics: optimal prediction rules, the curse of dimensionality, empirical risk, linear regression and classification, basis expansions, regularization, splines, the bootstrap, model selection, classification and regression trees, boosting, support vector machines. Computational efficiency versus predictive performance. Emphasis on experience with real data and assessing statistical assumptions.

Statistics (STAT) 158 - The Design and Analysis of Experiments <Foundational>:
	An introduction to the design and analysis of experiments. This course covers planning, conducting, and analyzing statistically designed experiments with an emphasis on hands-on experience. Standard designs studied include factorial designs, block designs, latin square designs, and repeated measures designs. Other topics covered include the principles of design, randomization, ANOVA, response surface methodoloy, and computer experiments.

Statistics (STAT) 159 - Reproducible and Collaborative Data Science <Foundational>:
	A project-based introduction to statistical data analysis. Through case studies, computer laboratories, and a term project, students will learn practical techniques and tools for producing statistically sound and appropriate, reproducible, and verifiable computational answers to scientific questions. Course emphasizes version control, testing, process automation, code review, and collaborative programming. Software tools may include Bash, Git, Python, and LaTeX.

Statistics (STAT) 20 - Introduction to Probability and Statistics <Foundational>:
	For students with mathematical background who wish to acquire basic concepts. Relative frequencies, discrete probability, random variables, expectation. Testing hypotheses. Estimation. Illustrations from various fields.

Statistics (STAT) 88 - Probability and Mathematical Statistics in Data Science <Foundational>:
	In this connector course we will state precisely and prove results discovered in DS8 through working with data. Topics include: total variation distance between discrete distributions; the mean, standard deviation, and tail bounds; correlation, and the derivation of the regression equation; probabilities, random variables, and the Central Limit Theorem; probabilistic models; symmetries in random permutations; prior and posterior distributions, and Bayes rule.

Statistics (STAT) 89A - Introduction to Matrices and Graphs in Data Science <Foundational>:
	This connector will cover introductory topics in the mathematics of data science, focusing on discrete probability and linear algebra and the connections between them that are useful in modern theory and practice. We will focus on matrices and graphs as popular mathematical structures with which to model data. For examples, as models for term-document corpora, high-dimensional regression problems, ranking/classification of web data, adjacency properties of social network data, etc.

Undergraduate Business Administration (UGBA) 104 - Analytic Decision Modeling Using Spreadsheets <Applied>:
	This course provides an introduction to several quantitative methods used to facilitate complex decision-making in business, with applications in many different industries, at different levels in the organization, and with different scopes of decisions. The power of the methods covered in this class is further enhanced by implementing them in spreadsheet software, which allows complex problems to be approached and solved in a straightforward and understandable manner.

Undergraduate Business Administration (UGBA) 122 - Financial Information Analysis <Applied>:
	This course is designed to: 1) develop basic skills in financial statement analysis; 2) teach students to identify the relevant financial data used in a variety of decision contexts, such as equity valuation, forecasting firm-level economic variables, distress prediction and credit analysis; 3) help students appreciate the factors that influence the outcome of the financial reporting process, such as the incentives of reporting parties, regulatory rules, and a firm's competitive environment.

Undergraduate Business Administration (UGBA) 143 - Game Theory and Business Decisions <Applied>:
	This course provides an introduction to game theory and decision analysis. Game theory is concerned with strategic interactions among players (multi-player games), and decision analysis is concerned 

Undergraduate Business Administration (UGBA) 161 - Marketing Research: Tools and Techniques for Data Collection and Analysis <Applied>:
	Marketing research objectives; qualitative research, surveys, experiments, sampling, data analysis.