classified_courses.csv

Type of Course,ID,Department,Course #,Course Name,Units,Prerequisites,Tools,Description,Applied,Meta,Tool
Method,1.0,African American Studies (AFRICAM),101,Research Methods for African American Studies,5.0,STAT 2 or STAT 20,,"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.",True,False,False
Method,2.0,American Studies (AMERSTD),C134,Information Technology and Society,4.0,None,,"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. ",False,True,False
Core,3.0,Anthropology (ANTHRO),169A,Data Analysis and Computational Methods,4.0,ANTHRO 2,,"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.",True,False,False
Method,4.0,Anthropology (ANTHRO),135,Paleoethnobotany: Archaeological Methods and Laboratory Techniques,4.0,ANTHRO 2,,"An introduction to the basic approaches and techniques in archaeobotanical analysis. A series of different data types and their unique approaches will be discussed, including phytoliths, pollen, and DNA, with an emphasis on macrofloral remains. Laboratory study will include the major classes of plant remains likely to be encountered in archaeological sites. Discussion will emphasize the use of plant remains to answer archaeological questions, rather than study the plant remains for their own sake. Microscope work and computing will be included.",True,False,False
Method,5.0,Anthropology (ANTHRO),169B,Research Theory and Methods in Socio-Cultural Anthropology,5.0,ANTHRO 3,,"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.",True,True,False
Method,6.0,Anthropology (ANTHRO),169C,Research Theory and Methods in Linguistic Anthropology,4.0,None,,"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.",True,False,False
Method,7.0,Architecture (ARCH),154,Design and Computer Analysis of Structure,3.0,ARCH 150,,Design and analysis of whole structural building systems with the aid of finite element analytical methods. Advanced structural concepts explored in a laboratory environment.,True,False,False
Method,8.0,Asian American Studies Program (ASAMST),165,Research Methodologies in Asian American Communities,4.0,ASAMST 20A or 20B,,"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.",True,False,False
Core,9.0,Bioengineering (BIO ENG),143,Computational Methods in Biology,4.0,"MATH 53, MATH 54",,"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.",True,False,False
Method,10.0,Bioengineering (BIO ENG),131,Introduction to Computational Molecular and Cell Biology,4.0,"MATH 54, BIO 1A",,"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.",True,False,False
Core,11.0,Civil and Environmental Engineering (CIV ENG),93,Engineering Data Analysis,3.0,E 7,,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.,True,False,False
Method,12.0,Civil and Environmental Engineering (CIV ENG),193,Engineering Risk Analysis,3.0,Upper division standing,,"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.",True,False,False
Method,13.0,Civil and Environmental Engineering (CIV ENG),88,Data Science for Smart Cities,2.0,DATA 8,,,False,True,True
Method,14.0,Cognitive Science (COG SCI),131,Computational Models of Cognition,4.0,"MATH 1A, MATH 1B, CS 70 or Math 55, COG SCI 1, CS 61A",,"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.",True,False,False
Method,15.0,Cognitive Science (COG SCI),c140,Quantitative Methods in Linguistics,4.0,LINGUIS 100 or graduate student standing,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.,True,False,False
Method,16.0,Cognitive Science (COG SCI),88,Data Science and the Mind,2.0,DATA 8,,,False,True,True
Core,17.0,Computer Science (COMPSCI),194-16,Introduction to Data Science,3.0,"CS 61A, CS 61B, CS 61C, CS 70","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.",False,False,True
Core,18.0,Computer Science (COMPSCI),189,Introduction to Machine Learning,4.0,"MATH 53, MATH 54, CS 70, CS 188",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.",False,False,True
Core,19.0,Computer Science (COMPSCI),186,Introduction to Database Systems,4.0,"CS 61B, CS 61C","SQL, Spark","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.",False,False,True
Core,20.0,Computer Science (COMPSCI),C8,Foundations of Data Science,4.0,None,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.",False,False,True
Core,21.0,Computer Science (COMPSCI),188,Introduction to Artificial Intelligence,4.0,"CS 61A, CS 61B, CS 70 or MATH 55",,"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.",True,False,False
Method,22.0,Computer Science (COMPSCI),9A,Matlab for Programmers,2.0,CS 10,,"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.",False,False,True
Method,23.0,Computer Science (COMPSCI),9C,C for Programmers,2.0,CS 9B or CS 61A or E7,"Scheme, SQL, Python","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. ",False,False,True
Method,24.0,Computer Science (COMPSCI),61A,The Structure and Interpretation of Computer Programs,4.0,MATH 1A,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. ",False,False,True
Method,25.0,Computer Science (COMPSCI),61B,Data Structures,4.0,CS 61A or E7,,"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. ",False,False,True
Method,26.0,Computer Science (COMPSCI),61C,Machine Structures,4.0,CS 61B,,"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.",False,False,True
Method,27.0,Computer Science (COMPSCI),9D,Scheme and Functional Programming for Programmers,2.0,CS 61A,,"Measures and methods of Demography. Life tables, fertility and nuptiality measures, age pyramids, population projection, measures of fertility control.",False,False,True
Method,28.0,Computer Science (COMPSCI),9E,Productive Use of the Unix Environment,2.0,None,,"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.",False,False,True
Method,29.0,Computer Science (COMPSCI),9F,C++ for Programmers,2.0,CS 61A,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.",False,False,True
Method,30.0,Computer Science (COMPSCI),9G,Java for Programmers,2.0,CS 61A,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?",False,False,True
Method,31.0,Computer Science (COMPSCI),9H,Python for Programmers,2.0,CS 61A,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.",False,False,True
Method,32.0,Computer Science (COMPSCI),88,Computational Structures in Data Science,2.0,DATA 8,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.",True,False,True
Method,33.0,Computer Science (COMPSCI),10,The Beauty and Joy of Computing,4.0,None,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.",False,False,True
Method,34.0,Demography (DEMOG),5,Fundamentals of Population Science,3.0,None,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.",True,False,False
Method,35.0,Demography (DEMOG),110,Introduction to Population Analysis,3.0,None,,"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.",True,False,False
Method,36.0,Demography (DEMOG),C126,Social Consequences of Population Dynamics,4.0,SOC 1 or SOC 3 or SOC 3AC or consent of instructor,,"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.",False,True,False
Method,37.0,Demography (DEMOG),C164,Impact of Government Policies on Poor Children and Families,4.0,None,,"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.",False,True,False
Method,38.0,Demography (DEMOG),C175,Economic Demography,3.0,ECON 1 or ECON 2,,"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?",True,True,False
Method,39.0,Earth and Planetary Science (EPS),104,Mathematical Methods in Geophysics,4.0,"MATH 53, MATH 54","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.",True,False,False
Method,40.0,Earth and Planetary Science (EPS),109,Computer Simulations in Earth and Planetary Sciences,4.0,MATH 1A ,,"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.",False,True,False
Core,41.0,Economics (ECON),141,Econometric Analysis,4.0,"ECON 100A - ECON 100B or ECON 101A - ECON 101B, STAT 20 or STAT 21 or STAT 131A, MATH 53, MATH 54",,"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.",True,False,False
Method,42.0,Economics (ECON),C103,Introduction to Mathematical Economics,4.0,"MATH 53, MATH 54",,"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.",True,False,False
Method,43.0,Economics (ECON),140,Economic Statistics and Econometrics,4.0,"ECON 100A, ECON 100B or ECON 101A, ECON 101B, STAT 20 or STAT 21 or STAT 131A",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.",True,False,False
Method,44.0,Economics (ECON),C142,Applied Econometrics and Public Policy,4.0,ECON 140 or ECON 141 or consent of instructor,"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.",True,True,False
Method,45.0,Economics (ECON),174,Global Poverty and Impact Evaluation,4.0,"ECON 100A or ECON 100B, STAT 20",,"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.",False,True,False
Method,46.0,Economics (ECON),C175,Economic Demography,3.0,ECON 1 or ECON 2,,"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.",True,True,False
Core,47.0,Electrical Engineering (EL ENG),144,"Fundamental Algorithms for Systems Modeling, Analysis, and Optimization",4.0,"EE20, CS 70",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.,True,False,True
Method,48.0,Electrical Engineering (EL ENG),126,Probability and Random Processes,4.0,"EE 16A, EE 16B",,"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.",False,False,True
Method,49.0,Electrical Engineering (EL ENG),127,Optimization Models in Engineering,4.0,MATH 54 or consent of instructor,,"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.",True,False,False
Method,50.0,Electrical Engineering (EL ENG),129,Neural and Nonlinear Information Processing,3.0,EE 120 or consent of instructor,,"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.",False,False,True
Core,51.0,Engineering (ENGIN),170,Introduction to Modeling and Simulation,4.0,"E 7, MATH 53, MATH 54, Upper division standing",,"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.",False,True,False
Core,52.0,Engineering (ENGIN),177,Advanced Programming with MATLAB,3.0,"E 7, MATH 53, MATH 54, Upper division standing",,"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.",False,False,True
Core,53.0,Engineering (ENGIN),180A,Computational Engineering Science Modeling and Simulation I/II,4.0,"Senior/graduate standing in engineering or applied sciences, 7 or 77, Mathematics 128A-128B; 170A-170B recommended. 180A must be taken prior to taking 180B.",,"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.",True,True,False
Method,54.0,Engineering (ENGIN),7,Introduction to Computer Programming for Scientists and Engineers,4.0,Mathematics 1B (maybe taken concurrently),,"Organization and performance of agricultural and resource markets. Conduct of firms within those markets, such as price competition, product differentiation, predatory pricing, vertical integration, dealer networks and advertising. The role of public policy in the markets. Case studies include oil cartel OPEC, agricultural cooperatives, vertical integration of food processors and franchising of fast-food chains. Discussion sections cover empirical applications of theory presented during lectures for current environmental and agricultural policies.",False,False,True
Method,55.0,Engineering (ENGIN),117,Methods of Engineering Analysis,3.0,MATH 53 and 54,,"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.",True,False,False
Method,56.0,Engineering (ENGIN),120,Principles of Engineering Economics,3.0,Completion of 60 units of an approved engineering curriculum,,"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.",True,False,False
Method,57.0,Environmental Design (ENV DES),4B,Global Cities,3.0,None,,"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.",False,True,False
Method,58.0,Environmental Design (ENV DES),100,The City: Theories and Methods in Urban Studies,4.0,None,,"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.""",True,False,False
Core,59.0,Environmental Economics and Policy (ENVECON),C118,Introductory Applied Econometrics,4.0,Statistics 2 or equivalent.,,"Advanced cartographic methods will focus primarily on data acquisition, manipulation, analysis, and representation using GIS software and vector-based illustration software, in the service of geographical analysis. 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.",True,False,False
Method,60.0,Environmental Economics and Policy (ENVECON),131,Globalization and the Natural Environment,3.0,Intermediate microeconomic theory or consent of instructor,"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.",False,True,False
Method,61.0,Environmental Economics and Policy (ENVECON),140AC,"Economics of Race, Agriculture, and the Environment",3.0,"ENVECON 1, or one lower division course in a social science, or consent of instructor","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.",True,True,False
Method,62.0,Environmental Economics and Policy (ENVECON),142,Industrial Organization with Applications to Agriculture and Natural Resources,4.0,Environmental Economics and Policy 100 or Economics 100A or 101A,,"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.",False,True,False
Method,63.0,Environmental Economics and Policy (ENVECON),C180,Ecological Economics in Historical Context,3.0,None,,Formulation to linear programs. Optimal allocation and control problems in industry and environmental studies. Convex sets; properties of optimal solutions. The simplex method; theorems of duality; complementary slackness. Problems of post-optimization. Special structures; network problems. Digital computation.,True,True,False
Core,64.0,"Environmental Science, Policy, and Management (ESPM)",173,Introduction to Ecological Data Analysis,3.0,None,,"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.",True,False,False
Core,65.0,"Environmental Science, Policy, and Management (ESPM)",C177,GIS and Environmental Spatial Data Analysis,4.0,None,,"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.",True,True,False
Method,66.0,"Environmental Science, Policy, and Management (ESPM)",100,Introduction to the Methods of Environmental Science,4.0,Completion of upper division statistics requirement. Open only to declared Environmental Sciences majors.,,"This course uses simulation models for analyzing and optimizing systems where the underlying processes and/or parameters are not fully known, but data may be available, sampled, or artificially generated. Monte Carlo simulations are used in a weekly laboratory to model systems that may be too complex to approximate accurately with deterministic, stationary, or static models; and to measure the robustness of predictions and manage risks in decisions based on data-driven models.",True,True,False
Method,67.0,"Environmental Science, Policy, and Management (ESPM)",88A,Exploring Geospatial Data,2.0,Connector course; current enrollment in Data/CS/Stats/Info C8,,,True,False,False
Method,68.0,"Environmental Science, Policy, and Management (ESPM)",88,Data Sciences in Ecology and the Environment,2.0,Connector course; current enrollment in Data/CS/Stats/Info C8,,,False,False,True
Method,69.0,Geography (GEOG),187,Geographic Information Analysis,4.0,None,,"This course will cover topics related to the interplay between optimization and statistical learning. The first part of the course will cover statistical modeling procedures that can be defined as the minimizer of a suitable optimization problem. The second part of the course will discuss the formulation and numerical implementation of learning-based model predictive control (LBMPC), which is a method for robust adaptive optimization that can use machine learning to provide the adaptation. The last part of the course will deal with inverse decision-making problems, which are problems where an agent's decisions are observed and used to infer properties about the agent. ",True,False,False
Method,70.0,History (HIST),88,How Does History Count? ,2.0,Connector course; current enrollment in Data/CS/Stats/Info C8,,,True,True,False
Core,71.0,Industrial Engineering and Operations Research (IND ENG),115,Databases,,None (but IEOR courses recommended),,"This course is on computational methods for the solution of large-scale optimization problems. The focus is on converting the theory of optimization into effective computational techniques. Course topics include an introduction to polyhedral theory, cutting plane methods, relaxation, decomposition and heuristic approaches for large-scale optimization problems.",False,False,True
Core,72.0,Industrial Engineering and Operations Research (IND ENG),165,"Engineering Statistics, Quality Control, Forecasting",3.0,IEOR 172 or STAT 134 or course with background in probability theory,TREEAGE,"Introductory course on the theory and applications of decision analysis. Elective course that provides a systematic evaluation of decision-making problems under uncertainty. Emphasis on the formulation, analysis, and use of decision-making techniques in engineering, operations research and systems analysis. Includes formulation of risk problems and probabilistic risk assessments. Graphical methods and computer software using event trees, decision trees, and influence diagrams that focus on model design.",True,False,False
Method,73.0,Industrial Engineering and Operations Research (IND ENG),130,Methods of Manufacturing Improvement,3.0,"172, Mathematics 54, or Statistics 134 (may be taken concurrently).",,"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.",True,False,False
Method,74.0,Industrial Engineering and Operations Research (IND ENG),162,Linear Programming,3.0,Mathematics 53 and 54.,,"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.",False,False,True
Method,75.0,Industrial Engineering and Operations Research (IND ENG),131,Discrete Event Simulation,3.0,"161, 165; 172 or Statistics 134.",,"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.",False,True,False
Method,76.0,Industrial Engineering and Operations Research (IND ENG),172, Probability and Risk Analysis for Engineers,3.0,Mathematics 1A-1B or 16A-16B.,,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.,True,False,True
Core,77.0,Industrial Engineering and Operations Reserach (IEOR),231,"Introduction to Data Modeling, Statistics, and System Simulation",3.0,"IEOR 262A, 263A",,"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).",True,True,False
Method,78.0,Industrial Engineering and Operations Reserach (IEOR),265,Learning and Optimization,3.0,"IEOR 165, IEOR 162",,"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.",True,False,False
Method,79.0,Industrial Engineering and Operations Reserach (IEOR),264,Computational Optimization,3.0,IEOR 262A,,"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. ",True,False,False
Method,80.0,Industrial Engineering and Operations Reserach (IEOR),166,Decision Analysis,3.0,IEOR 172 or Statistics 134,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.",True,False,False
Method,81.0,Information (INFO),155,Introduction to High-Level Programming,3.0,None,,"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.",False,False,True
Method,82.0,Information (INFO),88,Data and Ethics,2.0,Connector course; current enrollment in Data/CS/Stats/Info C8,,,False,True,False
Core,83.0,Interdisciplinary Studies Field Major (ISF),C145,GIS and Environmental Spatial Data Analysis,4.0,None,,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.,True,True,False
Method,84.0,International and Area Studies (IAS),102,Scope and Methods of Research in International and Area Studies,4.0,Must intend to take DS H195 following semester,,"Programming for numerical calculations, round-off error, approximation and interpolation, numerical quadrature, and solution of ordinary differential equations. Practice on the computer.",True,False,False
Method,85.0,Linguistics (LINGUIS),140,Introduction to Field Methods,3.0,Linguistics 110 and 115,,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.,True,False,False
Method,86.0,Linguistics (LINGUIS),141,Empiricism and Linguistics,3.0,Linguistics 5 or 100,,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.),True,False,False
Method,87.0,Linguistics (LINGUIS),158,Computational Linguistics,3.0,Linguistics 120; Computer Science 3 or 61A recommended.,,"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. ",True,False,False
Method,88.0,(L & S) ,88-1,"Health, Human Behavior and Data",2.0,Connector course; current enrollment in Data/CS/Stats/Info C8,,,True,False,False
Method,89.0,(L & S) ,88-2,Literature and Data,2.0,Connector course; current enrollment in Data/CS/Stats/Info C8,,,True,False,False
Method,90.0,Mass Communications (MASSCOM),130,Research Methods in Media Studies,4.0,10 or permission of instructor,,"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.",True,True,False
Core,91.0,Mathematics (MATH),170,Mathematical Methods for Optimization,4.0,"Math 53, 54",,"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.",True,False,False
Method,92.0,Mathematics (MATH),127,Mathematical and Computational Methods in Molecular Biology,4.0,"53, 54, and 55; Statistics 20 recommended.",,"This course for upper division and graduate students will explore the molecular and cellular basis of microbial pathogenesis. The course will focus on model microbial systems which illustrate mechanisms of pathogenesis. Most of the emphasis will be on bacterial pathogens of mammals, but there will be some discussion of viral and protozoan pathogens. There will be an emphasis on experimental approaches. The course will also include some aspects of bacterial genetics and physiology, immune response to infection, and the cell biology of host-parasite interactions.",True,False,False
Method,93.0,Mathematics (MATH),191,Experimental Courses in Mathematics,1.0,Consent of instructor,,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.,True,False,False
Method,94.0,Mathematics (MATH),128A,Numerical Analysis,4.0,Math 53 and 54,,"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.",True,False,False
Method,95.0,Mathematics (MATH),98,Introduction to MATLAB programming,1.0,None,,"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.",False,False,False
Method,96.0,Mechanical Engineering (MEC ENG),120,Computational Biomechanics Across Multiple Scales,3.0,Mechanical Engineering C85,,"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.",True,False,False
Method,97.0,Mechanical Engineering (MEC ENG),C180,Engineering Analysis Using the Finite Element Method,3.0,Engineering 7 or Computer Science 61A; Mathematics 53 and 54; senior status in engineering or applied science.,,"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.",True,False,False
Method,98.0,Media Studies (MEDIAST),130,Research Methods in Media Studies,4.0,None,,"Methodological approaches for the quantification of technological risk and risk based decision making. Probabilistic safety assessment, human health risks, environmental and ecological risk analysis.",True,True,False
Method,99.0,Middle Eastern Studies (M E STU),102,Scope and Methods of Research in Middle Eastern Studies,4.0,Upper division standing,,"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.",True,False,False
Method,100.0,Molecular and Cell Biology (MCELLBI),C103,Bacterial Pathogenesis,3.0,MCB C100A/Chemistry C130 or MCB 102 or consent of instructor.,,"Basic principles and techniques used in human and animal nutrition research. Students design, execute, and analyze experiments.",False,False,False
Method,101.0,Molecular and Cell Biology (MCELLBI),137,Computer Simulation in Biology,3.0,None,,"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.",False,True,False
Method,102.0,Molecular and Cell Biology (MCELLBI),C148,Microbial Genomics and Genetics,4.0,None,,"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.",False,False,False
Method,103.0,Molecular and Cell Biology (MCELLBI),166,Biophysical Neurobiology,3.0,"Biology 1A, 1AL, Physics 8A-8B, Chemistry 1A, 3A/3AL-3B, or consent of instructor.",,"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. ",False,False,False
Method,104.0,Native American Studies (NATAMST),110,Theories and Methods in Native American Studies,4.0,71 or consent of instructor.,,"The instrumentation lab (formerly Basic Semiconductor Circuits) is an introductory course in basic design, analysis and modeling of circuits, and data analysis and control. Topics include but not limited to: linear circuits, semiconductor diodes, JFETS, Op-Amps, Labview programming, ADC and DAC converters, signal processing, and feedback control.",True,False,False
Method,105.0,Nuclear Engineering (NUC ENG),130,Analytical Methods for Non-proliferation,4.0,"101 or equivalent course in nuclear physics, or consent of instructor.",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.",True,False,False
Method,106.0,Nuclear Engineering (NUC ENG),175,Methods of Risk Analysis,3.0,Upper division standing,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.",True,False,False
Method,107.0,Nutritional Sciences and Toxicology (NUSCTX),121,Computational Toxicology,3.0,"110, 120 (may be taken concurrently).",,"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.",True,False,False
Method,108.0,Nutritional Sciences and Toxicology (NUSCTX),170,Experimental Nutrition Laboratory,4.0,Nutritional Sciences and Toxicology 103 and a course in statistics.,,"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.",True,False,False
Method,109.0,Philosophy (PHILOS),100,Philosophical Methods,4.0,"Two courses from Philosophy 2, 4, 25A, 25B.",,"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.",True,False,False
Method,110.0,Philosophy (PHILOS),141,Philosophy and Game Theory,4.0,One course in philosophy.,,"Descriptive statistics, probability, probability distributions, point and interval estimation, hypothesis testing, chi-square, correlation and regression with biomedical applications.",False,True,False
Method,111.0,Physics (PHYSICS),77,Introduction to Computational Techniques in Physics,2.0,Math 1A; Physics 5A or 7A,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.",True,False,False
Method,112.0,Physics (PHYSICS),111A,Instrumentation Laboratory,3.0,consent of instructor,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.",False,False,False
Core,113.0,Psychology (PSYCH),10,Research and Data Analysis in Psychology,4.0,"Completion of the general psychology prerequisite (1, N1, or W1) and one of the following quantitative prerequisites: Math 10A-B, Statistics 2, or consent of instructor.",,"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.",True,False,False
Core,114.0,Psychology (PSYCH),101,Research and Data Analysis in Psychology,4.0,1 and completion of the quantitative prerequisites for the major.,,"This course provides an overview of issues and trends that will shape the need for data mining and analytics across a variety of disciplines, fosters critical thinking about real world actionability from analytics, and develop intuition in various machine learning classification algorithms (e.g. decision trees, neural networks, support vector machines), clustering techniques (e.g. k-means, spectral), as well as big data analytics tools (Torque cluster, EC2, MapReduce).",True,False,False
Method,115.0,Psychology (PSYCH),10,Research and Data Analysis in Psychology,4.0,Psych 1 or equiv and Math 10A-B or Stats 2,,"The seminar explores leading-edge trends in data science and analytics at Silicon Valley and tech firms. The speakers will include executives, entrepreneurs, and researchers from leading firms.  Topics include: Data analytics and äóìBig Data, Machine learning and scalabilitye, Business analytics including online marketing and advertising, financial services and risk analytics, operational and service analytics, Information retrieval (search), Information extraction, Social networks and social media, Healthcare analytics, and Energy analytics.",True,False,False
Core,116.0,Public Health (PB HLTH),140,Introduction to Risk and Demographic Statistics,4.0,Math 1A-1B,,"Specific topics, hours and credit may vary from section to section, year to year. May be repeated for credit with change in content.",True,False,False
Core,117.0,Public Health (PB HLTH),141,Introduction to Biostatistics,5.0,None,,"This course examines the state-of-the-art in applied Natural Language Processing (also known as content analysis and language engineering), with an emphasis on how well existing algorithms perform and how they can be used (or not) in applications. Topics include part-of-speech tagging, shallow parsing, text classification, information extraction, incorporation of lexicons and ontologies into text analysis, and question answering. Students will apply and extend existing software tools to text-processing problems. ",False,False,False
Core,118.0,Public Health (PB HLTH),142,Introduction to Probability and Statistics in Biology and Public Health,4.0,None,,"Introduction to many different types of quantitative research methods, with an emphasis on linking quantitative statistical techniques to real-world research methods. Introductory and intermediate topics include: defining research problems, theory testing, casual inference, probability, and univariate statistics. Research design and methodology topics include: primary/secondary survey data analysis, experimental designs, and coding qualitative data for quantitative analysis.",True,False,True
Core,119.0,Public Health (PB HLTH),144A,Introduction to SAS Programming,2.0,PB HLTH 142,,"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.",False,False,True
Core,120.0,Public Health (PB HLTH),144B,Introduction to SAS Programming,2.0,PB HLTH 144A,,"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. ",False,False,True
Method,121.0,Public Health (PB HLTH),145,Statistical Analysis of Continuous Outcome data,4.0,PB HLTH 142,,"Problems of research design, measurement, and data collection, processing, and analysis will be considered. Attention will be given to both qualitative and quantitative studies.",True,False,False
Core,122.0,School of Information (INFO),290T,Data Mining and Analytics in Intelligent Business Services,3.0,Some programming experience,,"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.",True,False,False
Core,123.0,School of Information (INFO),296A,Data Science and Analytics: Thought Leaders,2.0,None,,"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.",True,False,True
Core,124.0,School of Information (INFO),290,Research Seminary in Exploratory Data Analysis,1.0,,,"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.",True,False,False
Method,125.0,School of Information (INFO),256,Natural Language Processing,3.0,Familiarity with Python and statistics,,"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.",True,False,True
Method,126.0,School of Information (INFO),271B,Quantitative Research Methods,3.0,Introductory statistics course,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.",True,False,False
Core,127.0,Sociology (SOCIOL),7,The Power of Numbers: Quantitative Data in Social Sciences,4.0,"Soc. 1, 3, 3AC or consent of instructor.",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.",False,True,False
Method,128.0,Sociology (SOCIOL),5,Evaluation of Evidence,4.0,None,"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.",False,False,False
Method,129.0,Sociology (SOCIOL),105,Research Design and Sociological Methods,5.0,SOC 5,,"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.",True,False,False
Method,130.0,Sociology (SOCIOL),106,Quantitative Sociological Methods,4.0,SOC 5,,"Ideas for estimation and hypothesis testing basic to applications, including an introduction to probability. Linear estimation and normal regression theory.",True,False,False
Method,131.0,Sociology (SOCIOL),108,Advanced Methods: In-depth Interviewing,4.0,SOC 5 or equivalent.,,"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.",True,False,False
Method,132.0,Sociology (SOCIOL),7,The Power of Numbers: Quantitative Data in Social Sciences,4.0,"SOC 1,3,3AC",,"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.",False,True,False
Core,133.0,Statistics (STAT),135,Concepts of Statistics,4.0,"STAT 134, Linear Algebra (MATH 54), STAT 133 Recommended",,"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.",True,False,False
Core,134.0,Statistics (STAT),154,Modern Statistical Prediction and Machine Learning,4.0,"MATH 53, MATH  54, STAT 135, some programming language, MATH 55 recommended",,"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. ",False,False,True
Core,135.0,Statistics (STAT),133,Concepts of Computing with Data,3.0,None,R,"Applied statistics with a focus on critical thinking, reasoning skills, and techniques. Hands-on-experience with solving real data problems with high-level programming languages such as R. Emphasis on examining the assumptions behind standard statistical models and methods. Exploratory data analysis (e.g., graphical data summaries, PCAs, clustering analysis). Model formulation, fitting, and validation and testing. Linear regression and generalizations (e.g., GLMs, ridge regression, lasso). ",False,False,True
Core,136.0,Statistics (STAT),159,Reproducible and Collaborative Data Science,4.0,"STAT 133, STAT 134, STAT 135 or equivalent",,"Course builds on 215A in developing critical thinking skills and the techniques of advanced applied statistics. Particular topics vary with instructor. Examples of possible topics include planning and design of experiments, ANOVA and random effects models, splines, classification, spatial statistics, categorical data analysis, survival analysis, and multivariate analysis. ",False,False,True
Method,137.0,Statistics (STAT),153,Time Series,4.0,"STAT 101, 134 or consent of instructor. 133 or 135 recommended.",,"Classification regression, clustering, dimensionality, reduction, and density estimation. Mixture models, hierarchical models, factorial models, hidden Markov, and state space models, Markov properties, and recursive algorithms for general probabilistic inference nonparametric methods including decision trees, kernal methods, neural networks, and wavelets. Ensemble methods. ",True,False,False
Method,138.0,Statistics (STAT),131A,Introduction to Probability and Statistics for Life Scientists,4.0,One semester of calculus or consent of instructor,,"Recent topics include: Graphical models and approximate inference algorithms. Markov chain Monte Carlo, mean field and probability propagation methods. Model selection and stochastic realization. Bayesian information theoretic and structural risk minimization approaches. Markov decision processes and partially observable Markov decision processes. Reinforcement learning. ",True,False,True
Method,139.0,Statistics (STAT),151A,Linear Modeling: Theory and Applications,4.0,Stat 102 or 135. 133 recommended.,,"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.",False,False,False
Method,140.0,Statistics (STAT),152,Sampling Surveys,4.0,Stat 101 or 134. 133 and 135 recommended.,,"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 ",True,False,False
Method,141.0,Statistics (STAT),158,The Design and Analysis of Experiments,4.0,Stat 134 and 135 or consent of instructor. Stat 135 may be taken concurrently. Stat 133 is recommended.,,"Marketing research objectives; qualitative research, surveys, experiments, sampling, data analysis.",True,False,False
Method,142.0,Statistics (STAT),134,Concepts of Probability,3.0,"MATH 1A, MATH 1B",,"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.",False,False,True
Method,143.0,Statistics (STAT),215A,Statistical Models: Theory and Application,4.0," Linear algebra, calculus, upper division probability and statistics, and familiarity with high-level programming languages. Statistics 133, 134, and 135 recommended",,"Economic analysis applicable to the problems of business enterprises with emphasis on the determination of the level of prices, outputs, and inputs; effects of the state of the competitive environment on business and government policies.",True,False,False
Method,144.0,Statistics (STAT),215B,Statistical Models: Theory and Application,4.0,STAT 215A,,Analysis of the operation of the market system with emphasis on the factors responsible for economic instability; analysis of public and business policies which are necessary as a result of business fluctuations.,True,False,False
Method,145.0,Statistics (STAT),241A,Statistical Learning Theory,3.0,"Linear algebra, calculus, basic probability, and statistics, algorithms. Computer Science 289 recommended.",,,False,False,False
Method,146.0,Statistics (STAT),241B,Advanced Topics in Learning and Decision Making,3.0,Computer Science C281A/Statistics C241A.,,,False,True,False
Method,147.0,Statistics (STAT),2,Introduction to Statistics,4.0,None,,,True,False,True
Method,148.0,Statistics (STAT),20,Introduction to Probability and Statistics,4.0,One semester of calculus,,,True,False,True
Method,149.0,Statistics (STAT),21,Introductory Probability and Statistics for Business,4.0,One semester of calculus,,,True,False,True
Method,150.0,Statistics (STAT),W21,Introductory Probability and Statistics for Business (Online),4.0,One semester of calculus,,,True,False,True
Method,151.0,Statistics (STAT),88,Probability and Mathematical Statistics in Data Science ,2.0,"One semester of calculus, concurrent enrollment in Stats/CS/Info C8",,,True,False,True
Method,152.0,Statistics (STAT),89A,Introduction to Matrices and Graphs in Data Science,2.0,"One year of calculus, concurrent enrollment in Stats/CS/Info C8",,,True,False,True