single_clustered_courses.txt

Bioengineering (BIO ENG)	143	Computational Methods in Biology	"MATH 53, MATH 54"	4		"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."	Biological Science		Applied	
Bioengineering (BIO ENG)	131	Introduction to Computational Molecular and Cell Biology	"MATH 54, BIO 1A"	4		"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."	Biological Science		Applied	
"Environmental Science, Policy, and Management (ESPM)"	88B	Data Sciences in Ecology and the Environment	DATA 8	2		"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."	Biological Science		Applied	
"Environmental Science, Policy, and Management (ESPM)"	88A	Exploring Geospatial Data	DATA 8	2		"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."	Biological Science		Applied	
"Environmental Science, Policy, and Management (ESPM)"	C177	GIS and Environmental Spatial Data Analysis	None	4		"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."	Biological Science		Applied	
Geography (GEOG)	187	Geographic Information Analysis	None	4		"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."	Biological Science		Applied	
Mathematics (MATH)	127	Mathematical and Computational Methods in Molecular Biology	"MATH 53, MATH 54, MATH 55, STAT 20"	4		"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."	Biological Science		Applied	
Molecular and Cell Biology (MCELLBI)	166	Biophysical Neurobiology	"BIO 1A/1AL, PHYS 8A - PHYS 8B, CHEM 1A, CHEM 3A/3AL - CHEM 3B/3BL"	3		"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."	Biological Science		Applied	
Molecular and Cell Biology (MCELLBI)	137	Computer Simulation in Biology	None	3		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.	Biological Science		Applied	
Molecular and Cell Biology (MCELLBI)	C148	Microbial Genomics and Genetics	None	4		"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."	Biological Science		Applied	
Nutritional Sciences and Toxicology (NUSCTX)	121	Computational Toxicology	"NUSCTX 110, NUSCTX 120"	3		"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."	Biological Science		Applied	
Nutritional Sciences and Toxicology (NUSCTX)	170	Experimental Nutrition Laboratory	"NUSCTX 103, STAT 20"	4		"Basic principles and techniques used in human and animal nutrition research. Students design, execute, and analyze experiments."	Biological Science		Applied	
Public Health (PB HLTH)	141	Introduction to Biostatistics	None	5		"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."	Biological Science		Applied	
Public Health (PB HLTH)	142	Introduction to Probability and Statistics in Biology and Public Health	None	4		"Descriptive statistics, probability, probability distributions, point and interval estimation, hypothesis testing, chi-square, correlation and regression with biomedical applications."	Biological Science		Applied	
Computer Science (COMPSCI)	9C	C for Programmers	CS 9B or CS 61A or E7	2	C	"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		Applied	
Computer Science (COMPSCI)	88	Computational Structures in Data Science	DATA 8	2		"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		Applied	
Computer Science (COMPSCI)	61B	Data Structures	CS 61A or E7	4	Java	"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	Foundational		
Computer Science (COMPSCI)	70	Discrete Mathematics and Probability Theory	Sophomore mathematical maturity	4	LaTeX	"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	Foundational		
Computer Science (COMPSCI)	188	Introduction to Artificial Intelligence	"CS 61A, CS 61B, CS 70 or MATH 55"	4	Python	"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	Foundational		
Computer Science (COMPSCI)	194-16	Introduction to Data Science	"CS 61A, CS 61B, CS 61C, CS 70"	3	"Python, VirtualBox"	"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	Foundational		
Computer Science (COMPSCI)	186	Introduction to Database Systems	"CS 61B, CS 61C"	4		"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	Foundational		
Computer Science (COMPSCI)	189	Introduction to Machine Learning	"MATH 53, MATH 54, CS 70, CS 188"	4	Python	"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	Foundational		
Computer Science (COMPSCI)	9A	Matlab for Programmers	CS 10	2	MATLAB	"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		Applied	
Computer Science (COMPSCI)	61A	Structure and Interpretation of Computer Programs	MATH 1A	4	"Python, Scheme, SQL"	"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	Foundational		
Computer Science (COMPSCI)	61C	Machine Structures	CS 61B	4	"C, Assembly, MIPS"	"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."	Computer Science		Applied	
Engineering (ENGIN)	177	Advanced Programming with MATLAB	"E 7, MATH 53, MATH 54, Upper division standing"	3	"Bash, Git, Python, LaTex"	"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."	Computer Science		Applied	
Information (INFO)	155	Introduction to High-Level Programming	None	3	Python	"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."	Computer Science		Applied	
Mathematics (MATH)	98	Introduction to MATLAB programming	None	1	MATLAB	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.	Computer Science		Applied	
Economics (ECON)	C142	Applied Econometrics and Public Policy	ECON 140 or ECON 141 or consent of instructor	4		"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/Business		Applied	
Economics (ECON)	141	Econometric Analysis	"ECON 100A - ECON 100B or ECON 101A - ECON 101B, STAT 20 or STAT 21 or STAT 131A, MATH 53, MATH 54"	4	R	"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/Business		Applied	
Economics (ECON)	C175	Economic Demography	ECON 1 or ECON 2	3		"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?"	Economics/Business		Applied	
Economics (ECON)	140	Economic Statistics and Econometrics	"ECON 100A, ECON 100B or ECON 101A, ECON 101B, STAT 20 or STAT 21 or STAT 131A"	4		"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/Business		Applied	
Economics (ECON)	174	Global Poverty and Impact Evaluation	"ECON 100A or ECON 100B, STAT 20"	4		"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/Business		Applied	
Economics (ECON)	C103	Introduction to Mathematical Economics	"MATH 53, MATH 54"	4	FEMLAB	"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/Business		Applied	
Environmental Economics and Policy (ENVECON)	C180	Ecological Economics in Historical Context	None	3		"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."	Economics/Business		Applied	Meta
Environmental Economics and Policy (ENVECON)	140AC	"Economics of Race, Agriculture, and the Environment"	ENVECON 1 or consent of instructor	3		"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."	Economics/Business		Applied	Meta
Environmental Economics and Policy (ENVECON)	131	Globalization and the Natural Environment	ECON 100A or consent of instructor	3		"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."	Economics/Business		Applied	Meta
Environmental Economics and Policy (ENVECON)	C118	Introductory Applied Econometrics	STAT 2	4		"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."	Economics/Business		Applied	
Undergraduate Business Administration (UGBA)	104	Analytic Decision Modeling Using Spreadsheets	"MATH 1B or MATH 16B, STAT 21 "	3		"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."	Economics/Business		Applied	
Undergraduate Business Administration (UGBA)	122	Financial Information Analysis	UBGA 120A	3		"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."	Economics/Business		Applied	
Undergraduate Business Administration (UGBA)	143	Game Theory and Business Decisions	"MATH 1B or MATH 16B, STAT 21 "	3		"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 "	Economics/Business		Applied	
Undergraduate Business Administration (UGBA)	161	Marketing Research: Tools and Techniques for Data Collection and Analysis	UGBA 106	3		"Marketing research objectives; qualitative research, surveys, experiments, sampling, data analysis."	Economics/Business		Applied	
Architecture (ARCH)	154	Design and Computer Analysis of Structure	ARCH 150	3		Design and analysis of whole structural building systems with the aid of finite element analytical methods. Advanced structural concepts explored in a laboratory environment.	Engineering		Applied	
Civil and Environmental Engineering (CIV ENG)	88	Data Science for Smart Cities	DATA 8	2		"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."	Engineering		Applied	
Civil and Environmental Engineering (CIV ENG)	93	Engineering Data Analysis	E 7	3	MATLAB	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.	Engineering		Applied	
Civil and Environmental Engineering (CIV ENG)	193	Engineering Risk Analysis	Upper division standing	3		"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."	Engineering		Applied	
Electrical Engineering (EL ENG)	144	"Fundamental Algorithms for Systems Modeling, Analysis, and Optimization"	"EE20, CS 70"	4	"R, Python"	"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	Foundational	Applied	
Electrical Engineering (EL ENG)	129	Neural and Nonlinear Information Processing	EE 120 or consent of instructor	3		"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."	Engineering		Applied	
Electrical Engineering (EL ENG)	127	Optimization Models in Engineering	MATH 54 or consent of instructor	4		"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."	Engineering		Applied	
Electrical Engineering (EL ENG)	126	Probability and Random Processes	"EE 16A, EE 16B"	4		"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."	Engineering	Foundational		
Engineering (ENGIN)	180A	Computational Engineering Science Modeling and Simulation I/II	"E 7, MATH 128A - MATH 128B, MATH 170A - MATH 170B, Upper division standing"	4		"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	Foundational	Applied	
Engineering (ENGIN)	7	Introduction to Computer Programming for Scientists and Engineers	MATH 1B	4	MATLAB	"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."	Engineering		Applied	
Engineering (ENGIN)	170	Introduction to Modeling and Simulation	"E 7, MATH 53, MATH 54, Upper division standing"	4	R	"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	Foundational		
Engineering (ENGIN)	117	Methods of Engineering Analysis	"MATH 53, MATH 54"	3	Python	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		Applied	
Engineering (ENGIN)	120	Principles of Engineering Economics	Upper division standing	3		"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		Applied	
Industrial Engineering and Operations Research (IND ENG)	115	Databases	None	3	"MS Access, SQL"	"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."	Engineering	Foundational		
Industrial Engineering and Operations Research (IND ENG)	131	Discrete Event Simulation	"IEOR 161, IEOR 165, IEOR 172 or STAT 134."	3		"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."	Engineering		Applied	
Industrial Engineering and Operations Research (IND ENG)	165	"Engineering Statistics, Quality Control, Forecasting"	IEOR 172 or STAT 134	3	"R, Python, or MATLAB"	"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."	Engineering		Applied	
Industrial Engineering and Operations Research (IND ENG)	162	Linear Programming and Network Flows	"MATH 53, MATH 54"	3		"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."	Engineering		Applied	
Industrial Engineering and Operations Research (IND ENG)	130	Methods of Manufacturing Improvement	"IEOR 172 or STAT 134, MATH 54"	3		"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."	Engineering		Applied	
Industrial Engineering and Operations Research (IND ENG)	172	Probability and Risk Analysis for Engineers	MATH 1A - MATH 1B or MATH 16A - MATH 16B	3		"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."	Engineering		Applied	
Mechanical Engineering (MEC ENG)	120	Computational Biomechanics Across Multiple Scales	ME C85	3		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.)	Engineering		Applied	
Mechanical Engineering (MEC ENG)	C180	Engineering Analysis Using the Finite Element Method	"E 7 or CS 61A, MATH 53, MATH 54, Upper division standing"	3		"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. "	Engineering		Applied	
Mechanical Engineering (MEC ENG)	102A	Introduction to Mechanical Systems for Mechatronics	"E 10, E 28, ENG R1A, MEC ENG C85 or CIV ENG C30, EE 40 or EE 100"	4		"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."	Engineering		Applied	
Nuclear Engineering (NUC ENG)	130	Analytical Methods for Non-proliferation	NUC ENG 101	4		"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."	Engineering		Applied	
Nuclear Engineering (NUC ENG)	175	Methods of Risk Analysis	Upper division standing	3		"Methodological approaches for the quantification of technological risk and risk based decision making. Probabilistic safety assessment, human health risks, environmental and ecological risk analysis."	Engineering		Applied	
Interdisciplinary Studies Field Major (ISF)	C145	GIS and Environmental Spatial Data Analysis	None	4		"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."	Environmental Science		Applied	
African American Studies (AFRICAM)	101	Research Methods for African American Studies	STAT 2 or STAT 20	5		"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."	Humanities		Applied	
American Studies (AMERSTD)	C134	Information Technology and Society	None	4		"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."	Humanities			Meta
Asian American Studies Program (ASAMST)	165	Research Methodologies in Asian American Communities	ASAMST 20A or 20B	4		"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."	Humanities		Applied	
History (HIST)	88	How Does History Count?	DATA 8	2		"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."	Humanities		Applied	
Mass Communications (MASSCOM)	130	Research Methods in Media Studies	MASSCOM 10	4		"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. "	Humanities		Applied	
Media Studies (MEDIAST)	130	Research Methods in Media Studies	MASSCOM 10	4		"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."	Humanities		Applied	
Middle Eastern Studies (M E STU)	102	Scope and Methods of Research in Middle Eastern Studies	Upper division standing	4		"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."	Humanities		Applied	
Native American Studies (NATAMST)	110	Theories and Methods in Native American Studies	NATAMST 71	4		"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."	Humanities		Applied	
Philosophy (PHILOS)	100	Philosophical Methods	"PHILOS 2, 4, 25A, 25B."	4		"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."	Humanities			Meta
Philosophy (PHILOS)	141	Philosophy and Game Theory	One course in philosophy.	4		"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."	Humanities		Applied	
Mathematics (MATH)	191	Experimental Courses in Mathematics	Consent of instructor	1		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/Statistics		Applied	
Mathematics (MATH)	54	Linear Algebra and Differential Equations	MATH 1B	4		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/Statistics	Foundational		
Mathematics (MATH)	170	Mathematical Methods for Optimization	"MATH 53, MATH 54"	4	MATLAB	"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/Statistics	Foundational	Applied	
Mathematics (MATH)	128A	Numerical Analysis	"MATH 53, MATH 54"	4		"Programming for numerical calculations, round-off error, approximation and interpolation, numerical quadrature, and solution of ordinary differential equations. Practice on the computer."	Mathematics/Statistics	Foundational		
Public Health (PB HLTH)	145	Statistical Analysis of Continuous Outcome Data	PB HLTH 142	4		"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."	Mathematics/Statistics		Applied	
Statistics (STAT)	133	Concepts of Computing with Data	None	3	R	"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."	Mathematics/Statistics	Foundational		
Statistics (STAT)	134	Concepts of Probability	"MATH 1A, MATH 1B"	4		"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."	Mathematics/Statistics	Foundational		
Statistics (STAT)	135	Concepts of Statistics	"STAT 134, MATH 54, STAT 133"	4		"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."	Mathematics/Statistics	Foundational		
Statistics (STAT)	89A	Introduction to Matrices and Graphs in Data Science	DATA 8	2		"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."	Mathematics/Statistics		Applied	
Statistics (STAT)	131A	Introduction to Probability and Statistics for Life Scientists	MATH 1A or MATH 16A	4		"Ideas for estimation and hypothesis testing basic to applications, including an introduction to probability. Linear estimation and normal regression theory."	Mathematics/Statistics		Applied	
Statistics (STAT)	151A	Linear Modeling: Theory and Applications	"STAT 102 or STAT 135, STAT 133"	4		"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."	Mathematics/Statistics		Applied	
Statistics (STAT)	154	Modern Statistical Prediction and Machine Learning	"MATH 53, MATH  54, STAT 135, MATH 55 "	4	R or Python	"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."	Mathematics/Statistics	Foundational		
Statistics (STAT)	88	Probability and Mathematical Statistics in Data Science	DATA 8	2		"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."	Mathematics/Statistics		Applied	
Statistics (STAT)	159	Reproducible and Collaborative Data Science	"STAT 133, STAT 134, STAT 135"	4	"Bash, Git, Python, and LaTeX"	"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."	Mathematics/Statistics	Foundational		
Statistics (STAT)	152	Sampling Surveys	"STAT 101 or STAT 134, STAT 133, STAT 135"	4		"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."	Mathematics/Statistics	Foundational		
Statistics (STAT)	158	The Design and Analysis of Experiments	"STAT 134, STAT 135, STAT 133"	4		"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."	Mathematics/Statistics	Foundational		
Statistics (STAT)	153	Time Series	"STAT 101, STAT 134, STAT 133 or STAT 135"	4		"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."	Mathematics/Statistics	Foundational	Applied	
Computer Science (COMPSCI)	C8	Foundations of Data Science	None	4	Python (Jupyter notebooks)	"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."	nan	Foundational	Applied	Meta
Earth and Planetary Science (EPS)	109	Computer Simulations in Earth and Planetary Sciences	MATH 1A	4	COMSOL	"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."	Physical Science		Applied	
Earth and Planetary Science (EPS)	104	Mathematical Methods in Geophysics	"MATH 53, MATH 54"	4	MATLAB	"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."	Physical Science		Applied	
Physics (PHYSICS)	77	Introduction to Computational Techniques in Physics	"MATH 1A, PHYS 7A"	2		"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. "	Physical Science		Applied	
Cognitive Science (COG SCI)	131	Computational Models of Cognition	"MATH 1A, MATH 1B, CS 70 or MATH 55, COG SCI 1, CS 61A"	4		"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."	Social Science		Applied	
Cognitive Science (COG SCI)	88	Data Science and the Mind	DATA 8	2		"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."	Social Science		Applied	
Cognitive Science (COG SCI)	C140	Quantitative Methods in Linguistics	LINGUIS 100 or graduate student standing	4	R	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.	Social Science		Applied	
Demography (DEMOG)	C175	Economic Demography	ECON 1 or ECON 2	3	MATLAB	"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?"	Social Science		Applied	
Demography (DEMOG)	5	Fundamentals of Population Science	None	3		"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."	Social Science		Applied	
Demography (DEMOG)	C164	Impact of Government Policies on Poor Children and Families	None	4	MATLAB	"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."	Social Science		Applied	
Demography (DEMOG)	110	Introduction to Population Analysis	None	3		"Measures and methods of Demography. Life tables, fertility and nuptiality measures, age pyramids, population projection, measures of fertility control."	Social Science		Applied	
Demography (DEMOG)	C126	Social Consequences of Population Dynamics	SOC 1 or SOC 3 or SOC 3AC or consent of instructor	4		"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."	Social Science			Meta
Environmental Design (ENV DES)	4B	Global Cities	None	3		"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."	Social Science		Applied	
Environmental Design (ENV DES)	100	The City: Theories and Methods in Urban Studies	None	4		"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."	Social Science			Meta
"Environmental Science, Policy, and Management (ESPM)"	173	Introduction to Ecological Data Analysis	None	3		"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."	Social Science		Applied	
"Environmental Science, Policy, and Management (ESPM)"	100	Introduction to the Methods of Environmental Science	"Completion of upper division statistics, declared Environmental Sciences majors."	4		"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."""	Social Science		Applied	
Information (INFO)	88	Data and Ethics	DATA 8	2		"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."	Social Science			Meta
International and Area Studies (IAS)	102	Scope and Methods of Research in International and Area Studies	DS H195 in following semester	4		"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."	Social Science		Applied	
Letters and Science (L & S)	88-1	"Health, Human Behavior, and Data"	DATA 8	2		"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."	Social Science		Applied	
Letters and Science (L & S)	88-2	Literature and Data	DATA 8	2	Python (Jupyter Notebook)	"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."	Social Science		Applied	
Linguistics (LINGUIS)	158	Computational Linguistics	"LINGUIS 120, CS 61A"	3		"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."	Social Science		Applied	
Linguistics (LINGUIS)	141	Empiricism and Linguistics	LINGUIS 5 or LINGUIS 100	3		"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)."	Social Science		Applied	
Linguistics (LINGUIS)	140	Introduction to Field Methods	"LINGUIS 110, LINGUIS 115"	3		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.	Social Science		Applied	
Psychology (PSYCH)	10	Research and Data Analysis in Psychology	"PSYCH 1, MATH 10A - MATH 10B or STAT 2"	4	R	"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."	Social Science		Applied	
Psychology (PSYCH)	101	Research and Data Analysis in Psychology	"PSYCH 1, MATH 10A - MATH 10B or STAT 2"	4	R	"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."	Social Science		Applied	
Public Health (PB HLTH)	140	Introduction to Risk and Demographic Statistics	MATH 1A - MATH 1B	4		"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."	Social Science		Applied	
Public Health (PB HLTH)	144A	Introduction to SAS Programming	PB HLTH 142	2	SAS	"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."	Social Science		Applied	
Public Health (PB HLTH)	144B	Introduction to SAS Programming	PB HLTH 142	2	SAS	"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."	Social Science		Applied	
Sociology (SOCIOL)	108	Advanced Methods: In-depth Interviewing	SOC 5	4		"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."	Social Science		Applied	
Sociology (SOCIOL)	5	Evaluation of Evidence	None	4		"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. "	Social Science		Applied	
Sociology (SOCIOL)	106	Quantitative Sociological Methods	SOC 5	4		"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."	Social Science		Applied	
Sociology (SOCIOL)	105	Research Design and Sociological Methods	SOC 5	5		"Problems of research design, measurement, and data collection, processing, and analysis will be considered. Attention will be given to both qualitative and quantitative studies."	Social Science		Applied	
Sociology (SOCIOL)	7	The Power of Numbers: Quantitative Data in Social Sciences	"SOC 1, SOC 3, SOC 3AC or consent of instructor."	4		"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."	Social Science		Applied	
Anthropology (ANTHRO)	169A	Data Analysis and Computational Methods	ANTHRO 2	4		"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."	Social Sciences		Applied	
Anthropology (ANTHRO)	169C	Research Theory and Methods in Linguistic Anthropology	None	4		"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."	Social Sciences		Applied	
Anthropology (ANTHRO)	169B	Research Theory and Methods in Socio-Cultural Anthropology	ANTHRO 3	5		"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."	Social Sciences		Applied