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1.782 Environmental Engineering Masters of Engineering Project (MIT) 1.782 Environmental Engineering Masters of Engineering Project (MIT)

Description

This class is one of the core requirements for the Environmental Masters of Engineering program. It is designed to teach about environmental engineering through the use of case studies, computer software tools, and seminars from industrial experts. Case studies provide the basis for group projects as well as individual theses. Past case studies have included the MMR Superfund site on Cape Cod; restoration of the Florida Everglades; dredging of Boston Harbor; local watershed trading programs; appropriate wastewater treatment technology for Brazil; point-of-use water treatment for Nepal, Brownfields Development in Providence, RI, and water resource planning for the island of Cyprus. This class spans the entire academic year: students must register for the Fall term, IAP, and the Spring term. This class is one of the core requirements for the Environmental Masters of Engineering program. It is designed to teach about environmental engineering through the use of case studies, computer software tools, and seminars from industrial experts. Case studies provide the basis for group projects as well as individual theses. Past case studies have included the MMR Superfund site on Cape Cod; restoration of the Florida Everglades; dredging of Boston Harbor; local watershed trading programs; appropriate wastewater treatment technology for Brazil; point-of-use water treatment for Nepal, Brownfields Development in Providence, RI, and water resource planning for the island of Cyprus. This class spans the entire academic year: students must register for the Fall term, IAP, and the Spring term.

Subjects

civil engineering; environmental engineering; professional practice; methodology; thesis; proposal; yonder; geotechnical data; water treatment; aquifer; groundwater; hydrology; Chattahoochee; Tennessee; US Virgin Islands; pollution; contaminants; drinking water | civil engineering; environmental engineering; professional practice; methodology; thesis; proposal; yonder; geotechnical data; water treatment; aquifer; groundwater; hydrology; Chattahoochee; Tennessee; US Virgin Islands; pollution; contaminants; drinking water | civil engineering | civil engineering | environmental engineering | environmental engineering | professional practice | professional practice | methodology | methodology | thesis | thesis | proposal | proposal | yonder | yonder | geotechnical data | geotechnical data | water treatment | water treatment | aquifer | aquifer | groundwater | groundwater | hydrology | hydrology | Chattahoochee | Chattahoochee | Tennessee | Tennessee | US Virgin Islands | US Virgin Islands | pollution | pollution | contaminants | contaminants | drinking water | drinking water

License

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1.782 Environmental Engineering Masters of Engineering Project (MIT) 1.782 Environmental Engineering Masters of Engineering Project (MIT)

Description

This class is one of the core requirements for the Environmental Masters of Engineering program, in conjunction with 1.133 Masters of Engineering Concepts of Engineering Practice. It is designed to teach about environmental engineering through the use of case studies, computer software tools, and seminars from industrial experts. Case studies provide the basis for group projects as well as individual theses. Recent 1.782 projects include the MMR Superfund site on Cape Cod, appropriate wastewater treatment technology for Brazil and Honduras, point-of-use water treatment and safe storage procedures for Nepal and Ghana, Brownfields Development in Providence, RI, and water resource planning for the island of Cyprus and refugee settlements in Thailand. This class spans the entire academic year; This class is one of the core requirements for the Environmental Masters of Engineering program, in conjunction with 1.133 Masters of Engineering Concepts of Engineering Practice. It is designed to teach about environmental engineering through the use of case studies, computer software tools, and seminars from industrial experts. Case studies provide the basis for group projects as well as individual theses. Recent 1.782 projects include the MMR Superfund site on Cape Cod, appropriate wastewater treatment technology for Brazil and Honduras, point-of-use water treatment and safe storage procedures for Nepal and Ghana, Brownfields Development in Providence, RI, and water resource planning for the island of Cyprus and refugee settlements in Thailand. This class spans the entire academic year;

Subjects

civil engineering | civil engineering | environmental engineering | environmental engineering | professional practice | professional practice | methodology | methodology | thesis | thesis | proposal | proposal | request for proposal | request for proposal | water treatment | water treatment | aquifer | aquifer | groundwater | groundwater | hydrology | hydrology | Ghana | Ghana | Thailand | Thailand | Honduras | Honduras | pollution | pollution | contaminants | contaminants | drinking water | drinking water | refugee camp | refugee camp | sanitation | sanitation | water filtration | water filtration | guinea worm | guinea worm | biosand filter | biosand filter | horizontal roughing filter | horizontal roughing filter

License

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1.060 Engineering Mechanics II (MIT) 1.060 Engineering Mechanics II (MIT)

Description

This subject provides an introduction to fluid mechanics. Students are introduced to and become familiar with all relevant physical properties and fundamental laws governing the behavior of fluids and learn how to solve a variety of problems of interest to civil and environmental engineers. While there is a chance to put skills from calculus and differential equations to use in this subject, the emphasis is on physical understanding of why a fluid behaves the way it does. The aim is to make the students think as a fluid. In addition to relating a working knowledge of fluid mechanics, the subject prepares students for higher-level subjects in fluid dynamics. This subject provides an introduction to fluid mechanics. Students are introduced to and become familiar with all relevant physical properties and fundamental laws governing the behavior of fluids and learn how to solve a variety of problems of interest to civil and environmental engineers. While there is a chance to put skills from calculus and differential equations to use in this subject, the emphasis is on physical understanding of why a fluid behaves the way it does. The aim is to make the students think as a fluid. In addition to relating a working knowledge of fluid mechanics, the subject prepares students for higher-level subjects in fluid dynamics.

Subjects

fluid mechanics | fluid mechanics | fluids | fluids | civil and environmental engineering | civil and environmental engineering | differential equations | differential equations | calculus | calculus | flow | flow | movement | movement | wave forms | wave forms | Bernoulli's theorem | Bernoulli's theorem | wavelets | wavelets | mechanics | mechanics | solids | solids | hydrostatics | hydrostatics | mass | mass | momentum | momentum | energy | energy | flow nets | flow nets | velocity | velocity | laminar flow | laminar flow | turbulent flow | turbulent flow | groundwater | groundwater | hydraulics | hydraulics | backwater curves | backwater curves

License

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1.963 Environmental Engineering Applications of Geographic Information Systems (MIT) 1.963 Environmental Engineering Applications of Geographic Information Systems (MIT)

Description

This graduate seminar is taught in a lecture and lab exercise format. The subject matter is tailored to introduce Environmental Engineering students to the use and potential of Geographic Information Systems in their discipline. Lectures will cover the general concepts of GIS use and introduce the material in the exercises that demonstrate the practical application of GIS. This graduate seminar is taught in a lecture and lab exercise format. The subject matter is tailored to introduce Environmental Engineering students to the use and potential of Geographic Information Systems in their discipline. Lectures will cover the general concepts of GIS use and introduce the material in the exercises that demonstrate the practical application of GIS.

Subjects

GIS | GIS | Spatial Database Management | Spatial Database Management | Geographic Information Systems | Geographic Information Systems | ArcView | ArcView | census | census | SQL | SQL | databases | databases | cartography | cartography | community planning | community planning | spatial analysis | spatial analysis | wetlands management | wetlands management | data types | data types | map-making | map-making | data mapping | data mapping | hydrology | hydrology | environmental engineering | environmental engineering | deepwater habitats | deepwater habitats | salinization | salinization

License

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1.017 Computing and Data Analysis for Environmental Applications (MIT) 1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm

Subjects

probability | probability | statistics | statistics | events | events | random variables | random variables | univariate distributions | univariate distributions | multivariate distributions | multivariate distributions | uncertainty propagation | uncertainty propagation | Bernoulli trials | Bernoulli trials | Poisson processed | Poisson processed | conditional probability | conditional probability | Bayes rule | Bayes rule | random sampling | random sampling | point estimation | point estimation | interval estimation | interval estimation | hypothesis testing | hypothesis testing | analysis of variance | analysis of variance | linear regression | linear regression | computational analysis | computational analysis | data analysis | data analysis | environmental engineering | environmental engineering | applications | applications | MATLAB | MATLAB | numerical modeling | numerical modeling | probabilistic concepts | probabilistic concepts | statistical methods | statistical methods | field data | field data | laboratory data | laboratory data | numerical techniques | numerical techniques | Monte Carlo simulation | Monte Carlo simulation | variability | variability | sampling | sampling | data sets | data sets | computer | computer | uncertainty | uncertainty | interpretation | interpretation | quantitative data | quantitative data

License

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11.540J Urban Transportation Planning (MIT) 11.540J Urban Transportation Planning (MIT)

Description

The history, policy, and politics of urban transportation are discussed in this class. Also covered are the role of the federal government, the "highway revolt" and public transit in the auto era, using analytic tools for transportation planning and policy analysis. The class then explores the contribution of transportation to air pollution and climate change, land use and transportation interactions, together with issues with bicycles, pedestrians, and traffic calming. Examples used in the class are taken mainly from the Boston metropolitan area. The history, policy, and politics of urban transportation are discussed in this class. Also covered are the role of the federal government, the "highway revolt" and public transit in the auto era, using analytic tools for transportation planning and policy analysis. The class then explores the contribution of transportation to air pollution and climate change, land use and transportation interactions, together with issues with bicycles, pedestrians, and traffic calming. Examples used in the class are taken mainly from the Boston metropolitan area.

Subjects

11.540 | 11.540 | 1.252 | 1.252 | ESD.225 | ESD.225 | urban transportation planning | urban transportation planning | history | history | policy | policy | politics of urban transportation | politics of urban transportation | highway revolt | highway revolt | public transit | public transit | auto era | auto era | policy analysis | policy analysis | air pollution | air pollution | climate change | climate change | land use | land use | transportation interactions | transportation interactions | bicycles | bicycles | pedestrians | pedestrians | traffic calming | traffic calming | boston area examples | boston area examples | infrastructure | infrastructure | Big Dig | Big Dig | civil engineering | civil engineering | environmental engineering | environmental engineering | highway finance | highway finance | environmental and planning regulations | environmental and planning regulations | air quality | air quality | modal characteristics | modal characteristics | information technologies | information technologies

License

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11.380J Urban Transportation Planning (MIT) 11.380J Urban Transportation Planning (MIT)

Description

This class is an introduction to planning transportation in metropolitan areas. The approach, while rooted on the analytical tools which estimate outcomes and alternatives, is holistic. This means starting from a scan of the site, its history and its current trends, in order to frame properly the problem, including the relevant actors, institutions, roles and interests. The design and evaluation of alternatives considers this complexity, in addition to construction, operation and maintenance issues.  The decision-making and implementation process, including the needed feedback mechanisms, focuses as well on the need to build constituencies and alliances. The course topics include the history of urban transportation, highway finance, environmental and planning regulation This class is an introduction to planning transportation in metropolitan areas. The approach, while rooted on the analytical tools which estimate outcomes and alternatives, is holistic. This means starting from a scan of the site, its history and its current trends, in order to frame properly the problem, including the relevant actors, institutions, roles and interests. The design and evaluation of alternatives considers this complexity, in addition to construction, operation and maintenance issues.  The decision-making and implementation process, including the needed feedback mechanisms, focuses as well on the need to build constituencies and alliances. The course topics include the history of urban transportation, highway finance, environmental and planning regulation

Subjects

transportation planning | transportation planning | infrastructure | infrastructure | Big Dig | Big Dig | ivil engineering | | ivil engineering | | civil engineering | civil engineering | environmental engineering | environmental engineering | urban planning | urban planning | urban transportation | urban transportation | highway finance | highway finance | environmental and planning regulations | environmental and planning regulations | air quality | air quality | modal characteristics | modal characteristics | land use | land use | transportation interaction | transportation interaction | information technologies | information technologies | 11.380 | 11.380 | 1.252 | 1.252 | ESD.225 | ESD.225

License

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1.782 Environmental Engineering Masters of Engineering Project (MIT)

Description

This class is one of the core requirements for the Environmental Masters of Engineering program. It is designed to teach about environmental engineering through the use of case studies, computer software tools, and seminars from industrial experts. Case studies provide the basis for group projects as well as individual theses. Past case studies have included the MMR Superfund site on Cape Cod; restoration of the Florida Everglades; dredging of Boston Harbor; local watershed trading programs; appropriate wastewater treatment technology for Brazil; point-of-use water treatment for Nepal, Brownfields Development in Providence, RI, and water resource planning for the island of Cyprus. This class spans the entire academic year: students must register for the Fall term, IAP, and the Spring term.

Subjects

civil engineering; environmental engineering; professional practice; methodology; thesis; proposal; yonder; geotechnical data; water treatment; aquifer; groundwater; hydrology; Chattahoochee; Tennessee; US Virgin Islands; pollution; contaminants; drinking water | civil engineering | environmental engineering | professional practice | methodology | thesis | proposal | yonder | geotechnical data | water treatment | aquifer | groundwater | hydrology | Chattahoochee | Tennessee | US Virgin Islands | pollution | contaminants | drinking water

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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11.540J Urban Transportation Planning (MIT) 11.540J Urban Transportation Planning (MIT)

Description

The history, policy, and politics of urban transportation are discussed in this class. Also covered are the role of the federal government, the "highway revolt" and public transit in the auto era, using analytic tools for transportation planning and policy analysis. The class then explores the contribution of transportation to air pollution and climate change, land use and transportation interactions, together with issues with bicycles, pedestrians, and traffic calming. Examples used in the class are taken mainly from the Boston metropolitan area. The history, policy, and politics of urban transportation are discussed in this class. Also covered are the role of the federal government, the "highway revolt" and public transit in the auto era, using analytic tools for transportation planning and policy analysis. The class then explores the contribution of transportation to air pollution and climate change, land use and transportation interactions, together with issues with bicycles, pedestrians, and traffic calming. Examples used in the class are taken mainly from the Boston metropolitan area.

Subjects

11.540 | 11.540 | 1.252 | 1.252 | ESD.225 | ESD.225 | urban transportation planning | urban transportation planning | history | history | policy | policy | politics of urban transportation | politics of urban transportation | highway revolt | highway revolt | public transit | public transit | auto era | auto era | policy analysis | policy analysis | air pollution | air pollution | climate change | climate change | land use | land use | transportation interactions | transportation interactions | bicycles | bicycles | pedestrians | pedestrians | traffic calming | traffic calming | boston area examples | boston area examples | infrastructure | infrastructure | Big Dig | Big Dig | civil engineering | civil engineering | environmental engineering | environmental engineering | highway finance | highway finance | environmental and planning regulations | environmental and planning regulations | air quality | air quality | modal characteristics | modal characteristics | information technologies | information technologies

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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11.380J Urban Transportation Planning (MIT)

Description

This class is an introduction to planning transportation in metropolitan areas. The approach, while rooted on the analytical tools which estimate outcomes and alternatives, is holistic. This means starting from a scan of the site, its history and its current trends, in order to frame properly the problem, including the relevant actors, institutions, roles and interests. The design and evaluation of alternatives considers this complexity, in addition to construction, operation and maintenance issues.  The decision-making and implementation process, including the needed feedback mechanisms, focuses as well on the need to build constituencies and alliances. The course topics include the history of urban transportation, highway finance, environmental and planning regulation

Subjects

transportation planning | infrastructure | Big Dig | ivil engineering | | civil engineering | environmental engineering | urban planning | urban transportation | highway finance | environmental and planning regulations | air quality | modal characteristics | land use | transportation interaction | information technologies | 11.380 | 1.252 | ESD.225

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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11.380J Urban Transportation Planning (MIT)

Description

This class is an introduction to planning transportation in metropolitan areas. The approach, while rooted on the analytical tools which estimate outcomes and alternatives, is holistic. This means starting from a scan of the site, its history and its current trends, in order to frame properly the problem, including the relevant actors, institutions, roles and interests. The design and evaluation of alternatives considers this complexity, in addition to construction, operation and maintenance issues.  The decision-making and implementation process, including the needed feedback mechanisms, focuses as well on the need to build constituencies and alliances. The course topics include the history of urban transportation, highway finance, environmental and planning regulation

Subjects

transportation planning | infrastructure | Big Dig | ivil engineering | | civil engineering | environmental engineering | urban planning | urban transportation | highway finance | environmental and planning regulations | air quality | modal characteristics | land use | transportation interaction | information technologies | 11.380 | 1.252 | ESD.225

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm

Subjects

probability | statistics | events | random variables | univariate distributions | multivariate distributions | uncertainty propagation | Bernoulli trials | Poisson processed | conditional probability | Bayes rule | random sampling | point estimation | interval estimation | hypothesis testing | analysis of variance | linear regression | computational analysis | data analysis | environmental engineering | applications | MATLAB | numerical modeling | probabilistic concepts | statistical methods | field data | laboratory data | numerical techniques | Monte Carlo simulation | variability | sampling | data sets | computer | uncertainty | interpretation | quantitative data

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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11.380J Urban Transportation Planning (MIT)

Description

This class is an introduction to planning transportation in metropolitan areas. The approach, while rooted on the analytical tools which estimate outcomes and alternatives, is holistic. This means starting from a scan of the site, its history and its current trends, in order to frame properly the problem, including the relevant actors, institutions, roles and interests. The design and evaluation of alternatives considers this complexity, in addition to construction, operation and maintenance issues.  The decision-making and implementation process, including the needed feedback mechanisms, focuses as well on the need to build constituencies and alliances. The course topics include the history of urban transportation, highway finance, environmental and planning regulation

Subjects

transportation planning | infrastructure | Big Dig | ivil engineering | | civil engineering | environmental engineering | urban planning | urban transportation | highway finance | environmental and planning regulations | air quality | modal characteristics | land use | transportation interaction | information technologies | 11.380 | 1.252 | ESD.225

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm

Subjects

probability | statistics | events | random variables | univariate distributions | multivariate distributions | uncertainty propagation | Bernoulli trials | Poisson processed | conditional probability | Bayes rule | random sampling | point estimation | interval estimation | hypothesis testing | analysis of variance | linear regression | computational analysis | data analysis | environmental engineering | applications | MATLAB | numerical modeling | probabilistic concepts | statistical methods | field data | laboratory data | numerical techniques | Monte Carlo simulation | variability | sampling | data sets | computer | uncertainty | interpretation | quantitative data

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm

Subjects

probability | statistics | events | random variables | univariate distributions | multivariate distributions | uncertainty propagation | Bernoulli trials | Poisson processed | conditional probability | Bayes rule | random sampling | point estimation | interval estimation | hypothesis testing | analysis of variance | linear regression | computational analysis | data analysis | environmental engineering | applications | MATLAB | numerical modeling | probabilistic concepts | statistical methods | field data | laboratory data | numerical techniques | Monte Carlo simulation | variability | sampling | data sets | computer | uncertainty | interpretation | quantitative data

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm

Subjects

probability | statistics | events | random variables | univariate distributions | multivariate distributions | uncertainty propagation | Bernoulli trials | Poisson processed | conditional probability | Bayes rule | random sampling | point estimation | interval estimation | hypothesis testing | analysis of variance | linear regression | computational analysis | data analysis | environmental engineering | applications | MATLAB | numerical modeling | probabilistic concepts | statistical methods | field data | laboratory data | numerical techniques | Monte Carlo simulation | variability | sampling | data sets | computer | uncertainty | interpretation | quantitative data

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm

Subjects

probability | statistics | events | random variables | univariate distributions | multivariate distributions | uncertainty propagation | Bernoulli trials | Poisson processed | conditional probability | Bayes rule | random sampling | point estimation | interval estimation | hypothesis testing | analysis of variance | linear regression | computational analysis | data analysis | environmental engineering | applications | MATLAB | numerical modeling | probabilistic concepts | statistical methods | field data | laboratory data | numerical techniques | Monte Carlo simulation | variability | sampling | data sets | computer | uncertainty | interpretation | quantitative data

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm

Subjects

probability | statistics | events | random variables | univariate distributions | multivariate distributions | uncertainty propagation | Bernoulli trials | Poisson processed | conditional probability | Bayes rule | random sampling | point estimation | interval estimation | hypothesis testing | analysis of variance | linear regression | computational analysis | data analysis | environmental engineering | applications | MATLAB | numerical modeling | probabilistic concepts | statistical methods | field data | laboratory data | numerical techniques | Monte Carlo simulation | variability | sampling | data sets | computer | uncertainty | interpretation | quantitative data

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm

Subjects

probability | statistics | events | random variables | univariate distributions | multivariate distributions | uncertainty propagation | Bernoulli trials | Poisson processed | conditional probability | Bayes rule | random sampling | point estimation | interval estimation | hypothesis testing | analysis of variance | linear regression | computational analysis | data analysis | environmental engineering | applications | MATLAB | numerical modeling | probabilistic concepts | statistical methods | field data | laboratory data | numerical techniques | Monte Carlo simulation | variability | sampling | data sets | computer | uncertainty | interpretation | quantitative data

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm

Subjects

probability | statistics | events | random variables | univariate distributions | multivariate distributions | uncertainty propagation | Bernoulli trials | Poisson processed | conditional probability | Bayes rule | random sampling | point estimation | interval estimation | hypothesis testing | analysis of variance | linear regression | computational analysis | data analysis | environmental engineering | applications | MATLAB | numerical modeling | probabilistic concepts | statistical methods | field data | laboratory data | numerical techniques | Monte Carlo simulation | variability | sampling | data sets | computer | uncertainty | interpretation | quantitative data

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm

Subjects

probability | statistics | events | random variables | univariate distributions | multivariate distributions | uncertainty propagation | Bernoulli trials | Poisson processed | conditional probability | Bayes rule | random sampling | point estimation | interval estimation | hypothesis testing | analysis of variance | linear regression | computational analysis | data analysis | environmental engineering | applications | MATLAB | numerical modeling | probabilistic concepts | statistical methods | field data | laboratory data | numerical techniques | Monte Carlo simulation | variability | sampling | data sets | computer | uncertainty | interpretation | quantitative data

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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http://ocw.mit.edu/rss/all/mit-allthaicourses.xml

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1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm

Subjects

probability | statistics | events | random variables | univariate distributions | multivariate distributions | uncertainty propagation | Bernoulli trials | Poisson processed | conditional probability | Bayes rule | random sampling | point estimation | interval estimation | hypothesis testing | analysis of variance | linear regression | computational analysis | data analysis | environmental engineering | applications | MATLAB | numerical modeling | probabilistic concepts | statistical methods | field data | laboratory data | numerical techniques | Monte Carlo simulation | variability | sampling | data sets | computer | uncertainty | interpretation | quantitative data

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm

Subjects

probability | statistics | events | random variables | univariate distributions | multivariate distributions | uncertainty propagation | Bernoulli trials | Poisson processed | conditional probability | Bayes rule | random sampling | point estimation | interval estimation | hypothesis testing | analysis of variance | linear regression | computational analysis | data analysis | environmental engineering | applications | MATLAB | numerical modeling | probabilistic concepts | statistical methods | field data | laboratory data | numerical techniques | Monte Carlo simulation | variability | sampling | data sets | computer | uncertainty | interpretation | quantitative data

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allthaicourses.xml

Attribution

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1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm

Subjects

probability | statistics | events | random variables | univariate distributions | multivariate distributions | uncertainty propagation | Bernoulli trials | Poisson processed | conditional probability | Bayes rule | random sampling | point estimation | interval estimation | hypothesis testing | analysis of variance | linear regression | computational analysis | data analysis | environmental engineering | applications | MATLAB | numerical modeling | probabilistic concepts | statistical methods | field data | laboratory data | numerical techniques | Monte Carlo simulation | variability | sampling | data sets | computer | uncertainty | interpretation | quantitative data

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allthaicourses.xml

Attribution

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1.017 Computing and Data Analysis for Environmental Applications (MIT)

Description

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environm

Subjects

probability | statistics | events | random variables | univariate distributions | multivariate distributions | uncertainty propagation | Bernoulli trials | Poisson processed | conditional probability | Bayes rule | random sampling | point estimation | interval estimation | hypothesis testing | analysis of variance | linear regression | computational analysis | data analysis | environmental engineering | applications | MATLAB | numerical modeling | probabilistic concepts | statistical methods | field data | laboratory data | numerical techniques | Monte Carlo simulation | variability | sampling | data sets | computer | uncertainty | interpretation | quantitative data

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allthaicourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

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