Estratégias de negociação de cálculo estocástico

Citações quantitativas.

Apenas outro site WordPress.

Como atualizar o Anaconda para o Python 3.3 e usar no Eclipse (Windows)

Abra um prompt de comando e execute:

Você precisará digitar Sim para ir em frente.

No Eclipse, vá para:

Janela \ Preferências \ Pydev \ Interpretador & # 8211; Python.

Adicione um novo Python Interpreter for Python 33 no Anaconda (por exemplo, meu novo ambiente estava em c: \ anaconda \ envs \ p33 \ python. exe)

Para usar com um projeto:

Clique com o botão direito do mouse em um projeto e vá para Propriedades.

Sob PyDev & # 8211; Intérprete / Gramática, selecione seu novo intérprete.

Blog do Autospreader.

Finança quantitativa.

Uma Metodologia de Preços para os Futuros CAC40 e DAX.

Hoje, apresento um método de precificação para os futuros do EUREX DAX e do CAC-40. Este método eu desenvolvi em conjunto com os capítulos 5-7 do livro de Katsano "Intermarket Trading Strategies & # 8221; em que uma análise de correlação entre ações dos EUA e internacionais é realizada e é feita a alegação de que os movimentos do mercado de ações dos EUA lideram os mercados acionários internacionais diariamente. Primeiro, cubra os detalhes do contrato dados pelos hyperlinks abaixo:

A estratégia potencial agora seria a seguinte.

Obter uma lista de membros ativos do estoque do CAC-40 e DAX. Encontre quaisquer ADRs cotados dos EUA que tenham liquidez suficiente para ser um preço preciso (aqui estamos realmente preocupados apenas com o fechamento). Se não houver ADR para esse membro, temos algumas opções. (1) Um preço ponderado da porcentagem do setor no índice que representa substituído por seu equivalente nos EUA ou um parente americano próximo. Construa uma cesta de preços equivalente, levando em consideração os preços de ponderação *. CONTA PARA DIVIDENDOS! Capture os preços de fechamento e calcule o preço da cesta e isso deve ser o equivalente dos futuros preços CAC e DAX se você tiver executado o cálculo como se fosse um índice e se as suposições de Katsano forem válidas. A partir disso, você deve ter um rico / barato para os futuros de índices que estão sendo negociados atualmente.

Exemplo de captura de tela da minha versão:

Existe uma planilha de amostra datada no repositório do SourceForge (sourceforge / projects / autospreader / arquivos / Files são DAX e CAC40-DynamicPricing. xls) que cobre as especificidades deste cálculo. Eu dei uma versão datada para que você tenha que aprender fazendo o trabalho de atualizá-la. Sinta-se à vontade para fazer perguntas ao longo do caminho se encontrar obstáculos.

Referência: & # 8220; Estratégias de Negociação de Intermarkets & # 8221; por Katsanos.

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Como essa estratégia funcionou para você? Você ainda está negociando isso?

Ainda é viável após os custos de transação (custos de transação bancária / custos de transação de varejo?)?

Fez muito bem por cerca de 3 anos. Parei de negociar porque as horas eram terríveis, pois estou localizado nos EUA e tenho que monitorar o negócio até a abertura da sessão europeia. Foi viável com base no lucro líquido; Fiquei espantado com o desempenho de uma estratégia tão simples.

Segui você do NP. Ótimo Blog. Eu sou estudante de graduação em Comp. Engenharia. Vou tentar brincar com isso quando tiver um momento. Deixe vir:)

Obrigado; vai fazer. Eu tenho alguns artigos à beira de sair. Espero que eles saiam esta semana!

Muito obrigado por compartilhar. Eu testei e obtive um retorno de aproximadamente 8% p. a e vol de 5%, desalavancado, se eu fechar minha posição em aberto. Você já experimentou alguma diluição de retornos porque mais pessoas usam a estratégia?

Bom de se ouvir; Eu não experimentei isso, já que não troquei essa estratégia em particular há algum tempo.

Lista de Leitura de Finanças Quantitativas.

Uma lista de curadoria de livros para ajudar você a ter uma quantia melhor.

O financiamento quantitativo é um assunto técnico e abrangente. Abrange mercados financeiros, análise de séries temporais, gerenciamento de risco, engenharia financeira, estatística e aprendizado de máquina.

Os livros a seguir começam com o básico absoluto para cada área de assunto e aumentam gradualmente o nível de dificuldade. Você não precisa ler todos eles, mas certamente deve estudar alguns em profundidade.

Negociação Sistemática.

Aprendizado de Máquina & amp; Aprendizagem Profunda.

Engenharia financeira.

A Quantcademy.

Participe do portal de associação da Quantcademy que atende à crescente comunidade de traders de quantificação de varejo e aprenda como aumentar a lucratividade de sua estratégia.

Negociação Algorítmica Bem Sucedida.

Como encontrar novas ideias de estratégia de negociação e avaliá-las objetivamente para o seu portfólio usando um mecanismo de backtesting personalizado no Python.

Comércio Algorítmico Avançado.

Como implementar estratégias de negociação avançadas usando análise de séries temporais, aprendizado de máquina e estatísticas Bayesianas com R e Python.

Teoria do jogo.

Sobre este curso: Popularizado por filmes como "Uma Mente Brilhante", A teoria dos jogos é a modelagem matemática da interação estratégica entre agentes racionais (e irracionais). Além do que chamamos de jogos & # x27; em linguagem comum, como xadrez, pôquer, futebol, etc., inclui a modelagem de conflitos entre nações, campanhas políticas, competição entre empresas e comportamento comercial em mercados como a NYSE. Como você poderia começar a modelar leilões de palavras-chave e redes de compartilhamento de arquivos peer-to-peer, sem contabilizar os incentivos das pessoas que as usam? O curso fornecerá o básico: representando jogos e estratégias, a forma extensiva (que os cientistas da computação chamam de árvores de jogos), jogos bayesianos (modelando coisas como leilões), jogos repetidos e estocásticos e muito mais. Nós incluiremos uma variedade de exemplos, incluindo jogos clássicos e alguns aplicativos. Você pode encontrar um plano de estudos completo e descrição do curso aqui: web. stanford. edu/

jacksonm / GTOC-Syllabus. html Há também um curso de acompanhamento avançado para este, para pessoas já familiarizadas com a teoria dos jogos: coursera / learn / gametheory2 / Você pode encontrar um vídeo introdutório aqui: web. stanford. edu/

Para quem é essa turma: Este curso é destinado a estudantes, pesquisadores e profissionais que desejam entender mais sobre interações estratégicas. Você deve estar confortável com o pensamento matemático e argumentos rigorosos. Relativamente pouca matemática específica é necessária; mas você deve estar familiarizado com a teoria básica de probabilidade (por exemplo, você deve saber o que é uma probabilidade condicional), e alguns cálculos muito leves seriam úteis.

Vídeo: Vídeo introdutório Leitura: Vídeo do programa: 1-1 Introdução da teoria do jogo - Prompt de discussão do Backoff do TCP: Jogue o jogo do TCP se você gostar Vídeo: 1-2 Agentes Self-Interested e vídeo da teoria de serviço: 1-3 jogos definindo Vídeo: 1 -4 Exemplos de jogos Discussão Prompt: Jogue alguns jogos após a palestra 1.4 se você gosta de Vídeo: 1-5 Nash Equilibrium Intro Vídeo: 1-6 Raciocínio Estratégico Discussão Prompt: Jogar Keynes Jogo Concurso de Beleza se você gosta de vídeo: 1-7 Melhor Resposta Vídeo de Equilíbrio de Nash: 1-8 Equilíbrio de Nash dos Jogos de Exemplo Vídeo: 1-9 Estratégias Dominantes Vídeo: 1-10 Pareto Otimização da Atividade Quiz: Testes em Vídeo Semana 1 Leitura: Uma Breve Introdução aos Fundamentos da Teoria dos Jogos.

Vídeo: 2-1 Estratégias Mistas e Equilíbrio de Nash (I) Vídeo: 2-2 Estratégias Mistas e Equilíbrio de Nash (II) Vídeo: 2-3 Computing Vídeo de Equilíbrio de Nash Misto: 2-4 Dureza Além de Jogos 2x2 - Vídeo Básico: 2- 4 Dureza além dos jogos 2x2 - Vídeo avançado: 2-5 Exemplo: Estratégia mista Vídeo Nash: 2-6 Dados: Esportes profissionais e Estratégias mistas Testes de prática: Testes em vídeo Semana 2.

Vídeo: 3-1 Além do Vídeo de Equilíbrio de Nash: 3-2 Estratégias Estritamente Dominadas & amp; Vídeo de Remoção Iterativa: 3-3 Estratégias Dominadas & amp; Remoção Iterativa: Um Vídeo de Aplicação: 3-4 Vídeo de Estratégias Maxmin: 3-4 Estratégias Maxmin - Vídeo Avançado: 3-5 Equilíbrio Correlacionado: Teste de Prática de Intuição: Ensaios In-Video Semana 3.

Vídeo: 4-1 Informação Perfeita Formulário Extensivo: Gosto Vídeo: 4-2 Formalizando Informações Perfeitas Formulário Extensivo Jogos Discussão Prompt: Jogue o Jogo Centopéia se você gosta de Vídeo: 4-3 Informação Perfeita Formulário Extensivo: Estratégias, BR, NE Vídeo: 4 -4 Vídeo de Perfeição do Subjogo: 4-5 Vídeo de Indução Reversa: 4-6 Sub-Jogo Perfeito Aplicação: Ultimatum Negociação Vídeo: 4-7 Informação Imperfeita Formulário Extensivo: Vídeo de Poker: 4-8 Informação Imperfeita Formulário Extensivo: Definição, Estratégias Vídeo: 4- 9 Estratégias mistas e comportamentais Discussão Prompt: Jogue o jogo Rainbow Warship se gostar de Vídeo: 4-10 Informações incompletas na forma abrangente: além da perfeição no sub-jogo Prática Teste: testes em vídeo Semana 4.

Vídeo: 5-1 Jogos repetidos Discussão Prompt: Jogue alguns jogos repetidos se você gosta de Vídeo: 5-2 Jogos Infinitamente Repetidos: Vídeo Utilitário: 5-3 Jogos Estocásticos Vídeo: 5-4 Aprendizagem em Jogos Repetidos Discussão Avisar: Jogue a batalha repetida do vídeo dos jogos dos sexos: 5-5 Equilibrio de jogos infinitamente repetidos Vídeo: 5-6 jogos repetidos discontados Vídeo: 5-7 Um teorema popular para jogos repetidos descontinuados Quiz da prática: Quizzes In-Video Semana 5.

Vídeo: 6-1 Jogos Bayesianos: Prove Vídeo: 6-2 Jogos Bayesianos: Primeiro Vídeo de Definição: 6-2 Jogos Bayesianos: Primeira Definição (yoav) Vídeo: 6-3 Jogos Bayesianos: Segundo Vídeo de Definição: 6-4 Analisando Jogos Bayesianos Vídeo: 6-5 Analisando Jogos Bayesianos: Outro Exemplo de Teste Prático: Testes em Vídeo Semana 6.

Vídeo: 7-1 Teoria dos jogos de coalizão: Gosto Vídeo: 7-2 Teoria dos jogos de coalizão: Definições Vídeo: 7-3 O vídeo de valor de Shapley: 7-4 O vídeo principal: 7-5 Comparando o valor do núcleo e Shapley em uma prática de exemplo Teste: testes em vídeo Semana 7.

Quando terei acesso às palestras e tarefas?

E se eu precisar de mais tempo para concluir o curso?

Qual é a política de reembolso?

Cada curso é como um livro interativo, com vídeos, questionários e projetos pré-gravados.

Conecte-se com milhares de outros alunos e debata ideias, discuta materiais de cursos e obtenha ajuda para dominar conceitos.

Ganhe reconhecimento oficial pelo seu trabalho e compartilhe seu sucesso com amigos, colegas e empregadores.

Muito bem organizado. Estou ansioso para o curso 2!

Fácil de entender e explorar o novo modelo de pensamento.

Claro demais, realmente interessante, embora seja melhor com mais exercícios.

Ciência e Engenharia de Gestão.

Código de Email: 94305-4026

Os cursos oferecidos pelo Departamento de Ciências e Engenharia de Administração estão listados sob o código de assunto MS & amp; E no site do ExploreCourses do Stanford Bulletin.

O Departamento de Ciência e Engenharia de Gestão lidera na interface de engenharia, negócios e políticas públicas. A missão do departamento é, por meio da educação e da pesquisa, promover o design, o gerenciamento, a operação e a interação de sistemas tecnológicos, econômicos e sociais. A força de pesquisa de engenharia do departamento é integrada ao seu programa educacional nos níveis de graduação, mestrado e doutorado: os graduados do programa são treinados como engenheiros e futuros líderes em tecnologia, política e indústria. Atividades de pesquisa e ensino são complementadas por um programa de extensão que incentiva a transferência de idéias para o meio ambiente do Vale do Silício e além.

Gestão de Ciência e Engenharia (MS & amp; E) fornece programas de educação e pesquisa, integrando três pontos fortes básicos:

profundidade em fundamentos conceituais e analíticos cobertura abrangente de áreas funcionais de interação de aplicativos com outros departamentos de Stanford, indústria do Vale do Silício e organizações em todo o mundo.

Os fundamentos analíticos e conceituais incluem análise de decisão e risco, sistemas dinâmicos, economia, otimização, ciência organizacional e sistemas estocásticos. As áreas funcionais de aplicação incluem empreendedorismo, finanças, informação, marketing, comportamento organizacional, política, produção e estratégia. Associações próximas com outros departamentos de engenharia e com a indústria enriquecem os programas, oferecendo oportunidades para aplicar os métodos de MS & A a problemas importantes e motivando novos desenvolvimentos teóricos a partir da experiência prática. Os programas da MS & amp; E também fornecem uma base para contribuir com outras áreas, como biotecnologia, política de defesa, política ambiental, sistemas de informação e telecomunicações.

Missão do Curso de Graduação em Ciências de Gestão e Engenharia.

A missão do programa de graduação em Ciências da Gestão e Engenharia é fornecer aos alunos os fundamentos da análise de sistemas de engenharia para que eles sejam capazes de planejar, projetar e implementar sistemas complexos de gerenciamento econômico e técnico. O programa se baseia nos cursos básicos de engenharia, incluindo cálculo, fundamentos de engenharia e física ou química, além de ciência de gerenciamento. Os alunos concluem cursos básicos de contabilidade, ciência da computação, economia, ética, teoria organizacional, modelagem matemática, otimização, probabilidade e estatística. Para personalizar sua exploração, os alunos selecionam cursos adicionais de diferentes áreas do departamento, com maior ênfase em um deles. O major prepara os alunos para uma variedade de planos de carreira, incluindo bancos de investimento, consultoria de gestão, gerenciamento de instalações e processos, ou para pós-graduação em engenharia industrial, pesquisa operacional, negócios, economia, direito, medicina ou políticas públicas.

Resultados de Aprendizagem (Graduação)

O departamento espera que os alunos de graduação no programa possam demonstrar os seguintes resultados de aprendizagem. Esses resultados de aprendizado são usados ​​na avaliação de alunos e do programa de graduação do departamento. Espera-se que os alunos sejam capazes de:

aplicar os conhecimentos de matemática, ciências e engenharia; projetar e conduzir experimentos; projetar um sistema ou componentes para atender às necessidades desejadas; identificar, formular e resolver problemas de engenharia; usar técnicas, habilidades e ferramentas modernas de engenharia necessárias para a prática de engenharia; atuar em equipes multidisciplinares; comunicar-se efetivamente; reconhecer a necessidade e demonstrar a capacidade de se envolver na aprendizagem ao longo da vida; obter os antecedentes necessários para admissão em programas superiores de engenharia profissional ou de negócios; entender a responsabilidade profissional e ética; obter a ampla educação necessária para entender o impacto das soluções de engenharia em um contexto global e social; e obter conhecimento de questões contemporâneas pertinentes ao campo da ciência e engenharia de gestão.

Programas de Pós-Graduação em Ciências de Gestão e Engenharia.

MS & amp; E oferece programas que conduzem aos graus de Master of Science e Doctor of Philosophy. O departamento também oferece um coterminal B. S./M. S. grau, mestrado duplo em cooperação com cada um dos outros departamentos da Escola de Engenharia, e mestrados conjuntos com a Faculdade de Direito e o Programa de Políticas Públicas.

Para obter as regras do programa de graduação universitária da Universidade e os formulários de inscrição para a Universidade, consulte o site de graus acadêmicos do Registrador.

Candidatos para admissão como estudantes de pós-graduação em MS & amp; E devem apresentar os resultados das partes verbal, quantitativa e analítica do Graduate Record Examination. O prazo para inscrição no programa de doutorado é 5 de dezembro de 2017, e o prazo para inscrição no programa de mestrado é 16 de janeiro de 2018.

Exceto em circunstâncias incomuns, a admissão é limitada ao trimestre de outono porque os cursos são organizados seqüencialmente com cursos básicos e pré-requisitos oferecidos no início do ano acadêmico.

Assistentes e Bolsas de Estudo.

Um número limitado de bolsas de estudo e de assistência é concedido a cada ano. Candidatos admitidos no programa de doutorado, que indicaram em sua aplicação que eles gostariam de ser considerados para ajuda financeira, são automaticamente considerados para estes assistentes e bolsas de estudo. Alunos de mestrado novos e antigos podem se inscrever para as assistências do curso a cada trimestre, mas a prioridade é dada aos alunos de doutorado da MS & amp;

Informações sobre programas de empréstimo e auxílio com base em necessidades para cidadãos dos EUA e residentes permanentes podem ser obtidas no Escritório de Auxílio Financeiro.

Resultados de Aprendizagem (Graduação)

O M. S. prepara engenheiros para uma carreira ao longo da vida, abordando as necessidades técnicas e gerenciais críticas de organizações privadas e públicas. O programa enfatiza o desenvolvimento de habilidades analíticas, tomando melhores decisões, desenvolvendo e executando estratégias, além de liderar pessoas que inovam. Ao contrário de um MBA, nosso programa de mestrado aborda os desafios técnicos e comportamentais de organizações em execução e sistemas complexos. Nós enfatizamos as habilidades analíticas quantitativas e um espírito empreendedor.

O Ph. D. é conferido a candidatos que demonstraram uma bolsa substancial e a capacidade de conduzir pesquisas independentes. Através do trabalho do curso e pesquisa orientada, o programa prepara os alunos para fazer contribuições originais em Ciências da Gestão e Engenharia e áreas afins.

Carreiras em MS & amp; E.

Os alunos de MS & A são candidatos a carreiras em consultoria, gerenciamento de produtos e projetos, análise financeira e trabalho em arenas de políticas. Um número significativo participa ou encontra start-ups. Muitos se tornaram líderes em negócios baseados em tecnologia, que têm uma necessidade crescente de pessoas analiticamente orientadas que entendem os negócios e a tecnologia. Outros graduados fazem carreiras para enfrentar os problemas enfrentados pelos governos locais, nacionais e internacionais, desenvolvendo novos sistemas de saúde, novos sistemas de energia e um ambiente mais sustentável. Os principais problemas do dia exigem uma capacidade de integrar os modos de pensar técnico, social e econômico. É precisamente isso que o departamento ensina seus alunos a fazer.

Bacharel em Ciência da Gestão e Engenharia.

O programa que leva ao B. S. Licenciatura em Ciências de Gestão e Engenharia (MS & amp; E) é descrito na seção da Escola de Engenharia deste boletim; mais informações estão contidas no Manual da Escola de Engenharia para Programas de Graduação em Engenharia. Os alunos são incentivados a planejar seus programas acadêmicos o mais cedo possível, de preferência no primeiro ano ou no segundo ano. Os alunos não devem esperar até que estejam declarando um major para consultar a equipe de serviços para estudantes do departamento. Isto é particularmente importante para os estudantes que gostariam de estudar no exterior ou buscar outro curso maior ou menor.

O currículo de graduação em Ciência e Engenharia de Gestão fornece aos alunos treinamento nos fundamentos da análise de sistemas de engenharia para prepará-los para planejar, projetar e implementar sistemas complexos de gestão econômica e tecnológica onde um histórico científico ou de engenharia é necessário ou desejável. O major prepara os alunos para uma variedade de planos de carreira, incluindo bancos de investimento, consultoria de gestão, gerenciamento de instalações e processos, ou para pós-graduação em engenharia industrial, pesquisa operacional, negócios, economia, direito, medicina ou políticas públicas.

Os objetivos educacionais do programa de graduação são:

Princípios e habilidades - proporcionam aos alunos uma compreensão básica dos princípios da ciência e engenharia, incluindo soluções analíticas para problemas e habilidades de comunicação. Preparação para a prática - prepare os alunos para a prática em um campo que vê mudanças rápidas em ferramentas, problemas e oportunidades. Preparação para o crescimento contínuo - prepare os alunos para estudos de pós-graduação e desenvolvimento pessoal durante toda a carreira. Preparação para o serviço - desenvolva nos alunos a consciência, o histórico e as habilidades necessárias para se tornarem cidadãos, funcionários e líderes responsáveis.

Veja também os Resultados de Aprendizado de graduação do departamento para obter objetivos adicionais de aprendizado.

O programa baseia-se nos cursos básicos de engenharia, incluindo cálculo, modelagem matemática, probabilidade, estatística, fundamentos de engenharia e física ou química.

Os alunos interessados ​​em menores devem ver a guia Menor nesta seção.

MS & amp; E também participa com os departamentos de Ciência da Computação, Matemática e Estatística em um programa que leva a um B. S. em Ciências Matemáticas e Computacionais. Veja a seção “Matemática e Ciência da Computação” deste boletim.

O núcleo do departamento, tomado para todas as áreas, inclui cursos de contabilidade, ciência da computação, otimização determinística, economia, teoria organizacional e um projeto sênior. Através do núcleo, os alunos do programa são expostos à amplitude dos interesses do corpo docente e estão em uma boa posição para escolher uma área durante o primeiro ano.

O principal é projetado para permitir que um aluno explore as três áreas do departamento com maior profundidade.

Finanças e Decisão: enfoca a concepção e análise de planos financeiros e estratégicos. Operações e análises: enfoca algoritmos, teoria e o projeto e análise de sistemas de produção, produção e serviços. Organizações, Tecnologia e Política: enfoca a compreensão, o design e a análise de organizações e políticas públicas, especialmente questões baseadas em tecnologia.

Ciência e Engenharia de Gestão (MS & amp; E)

Conclusão do programa de graduação em Ciências de Gestão e Engenharia leva à atribuição do Bacharel em Ciências de Gestão e Engenharia.

Requisitos

e Estrutura e Reatividade de Moléculas Orgânicas.

e Estrutura e Reatividade de Moléculas Orgânicas.

e eletricidade, magnetismo e ótica.

e Mecânica, Fluidos e Laboratório de Calor.

e eletricidade e magnetismo.

Áreas Profundas.

Matemática e Ciências devem totalizar um mínimo de 44 unidades. As disciplinas eletivas devem vir da lista aprovada da Escola de Engenharia, ou podem não repetir o material de qualquer outra exigência. Crédito AP / IB para Química e Física pode ser usado.

Fundamentos de engenharia mais profundidade de engenharia devem totalizar um mínimo de 60 unidades. Os fundamentos de engenharia recomendados são E25B, E25E, E40A, E40M e E80.

Os alunos podem solicitar a colocação fora da Metodologia de Programação CS 106A.

Os cursos usados ​​para satisfazer o requisito de Matemática, Ciência, Tecnologia na Sociedade ou Fundamental de Engenharia também não podem ser usados ​​para satisfazer um requisito de profundidade de engenharia.

Ciências de Gestão e Engenharia (MS & amp; E) Menor.

Os seguintes cursos são necessários para atender aos requisitos menores:

Programa Coterminal em Ciências e Engenharia de Gestão.

Este programa permite que os alunos de Stanford tenham a oportunidade de trabalhar simultaneamente para um B. S. em Management Science and Engineering ou outro major quantitativo, e um M. S. em Ciências e Engenharia de Gestão.

Requisitos Universitários Coterminal.

Os candidatos a mestrado da Coterminal devem concluir todos os requisitos de mestrado conforme descrito neste boletim. Os requisitos universitários para o mestrado do coterminal são descritos na seção "Programa de Mestrado Coterminal". Os requisitos universitários para o mestrado são descritos em "Graduate Degrees" seção deste boletim.

Depois de aceitar a admissão para este programa de mestrado, os alunos podem solicitar a transferência de cursos da graduação para a carreira de pós-graduação para satisfazer os requisitos para o mestrado. Transferência de cursos para a carreira de pós-graduação requer revisão e aprovação dos programas de graduação e pós-graduação, caso a caso.

Neste programa de mestrado, os cursos realizados durante ou após o primeiro trimestre do segundo ano são elegíveis para serem considerados para transferência para a carreira de pós-graduação; o momento do primeiro trimestre de graduação não é um fator. Nenhum curso realizado antes do primeiro trimestre do segundo ano pode ser usado para atender aos requisitos de mestrado.

Transferências de cursos não são possíveis após o bacharelado ter sido conferido.

A Universidade exige que o orientador de pós-graduação seja designado no primeiro período de pós-graduação do aluno, embora a carreira de graduação ainda possa estar aberta. A Universidade também exige que a Proposta do Programa de Mestrado seja concluída pelo aluno e aprovada pelo departamento até o final do primeiro trimestre de graduação do aluno.

Mestrado em Ciência e Engenharia de Gestão.

O M. S. programas de graduação exigem um mínimo de 45 unidades além do equivalente a um B. S. grau em Stanford. Todos os programas representam um progresso substancial no campo principal além do grau de bacharel.

Os requisitos universitários para o mestrado são descritos em "Graduate Degrees" seção deste boletim.

O mestrado em Ciências da Gestão e Engenheiro prepara engenheiros para uma carreira ao longo da vida, abordando as necessidades técnicas e gerenciais de organizações privadas e públicas. O programa enfatiza o desenvolvimento de habilidades analíticas, tomada de melhores decisões e desenvolvimento e execução de estratégias, além de liderar pessoas que inovam. Ao contrário de um M. B.A., o programa de mestrado do departamento aborda os desafios técnicos e comportamentais de administrar organizações e sistemas complexos, enfatizando habilidades analíticas quantitativas e um espírito empreendedor.

Os alunos de MS & E conhecem matemática, engenharia e ciência comportamental. Eles podem realizar experimentos para projetar melhores sistemas, organizações e processos de trabalho. Eles entendem como analisar dados para resolver problemas do mundo real. Eles podem desenvolver modelos matemáticos e computacionais para informar a ação. Eles sabem como emergir e examinar suposições não articuladas e causas raízes. Esses alunos podem se comunicar efetivamente nos ambientes de equipe encontrados em tantas organizações contemporâneas.

Os alunos do mestrado MS & amp; E têm amplitude e profundidade. Todos são necessários para desenvolver competência em otimização e análise, organizações e decisões e probabilidade. Além disso, todo estudante busca uma especialidade em uma das seis áreas:

Análise financeira: os alunos que se concentram na Análise financeira estão preparados para carreiras que exigem rigor analítico e a capacidade de inovar em torno dos desafios do mercado. Exemplos de planos de carreira incluem serviços financeiros, gerenciamento de riscos, gerenciamento de investimentos, tecnologia financeira e processamento de dados, regulamentação e políticas financeiras, trocas e câmaras de compensação e auditoria e conformidade. A concentração combina o estudo aprofundado de técnicas quantitativas com a solução prática de problemas de negócios. Os alunos aprendem a usar modelos matemáticos e ferramentas quantitativas para resolver problemas complexos na prática financeira. A concentração explora os laços intelectuais entre finanças, pesquisa operacional, ciência da computação e engenharia. Oferece um alto nível de flexibilidade e uma série de disciplinas eletivas que permitem aos alunos adaptar o programa às suas metas específicas de carreira. Cursos exigidos imergem os estudantes em métodos quantitativos e aprofundam sua compreensão dos fundamentos financeiros. Os cursos de projetos apresentam projetos de equipe e estudos de caso práticos e baseados em dados, promovendo a aprendizagem em grupo e a interação com os pares. Operações e Análises: Os alunos que seguem a trilha de Operações e Análises são preparados nos fundamentos e aplicativos essenciais para carreiras em áreas que vão desde gerenciamento de operações no serviço, assistência médica, produção, manufatura, computador, telecomunicações, bancos, indústrias até a moderna Tecnologia da informação do Vale do Silício e análise de dados. O programa enfatiza o equilíbrio entre o rigor técnico das metodologias com valor duradouro e aplicações modernas e desafios de projeto em uma variedade de indústrias e ambientes de operações estabelecidos e emergentes. Oferece um portfólio de cursos em modelagem probabilística, otimização, simulação, algoritmos, data science, redes, mercados e aplicações correspondentes. Gestão de Tecnologia e Engenharia: Os alunos que se concentram em Tecnologia e Gestão de Engenharia estão preparados para carreiras, incluindo gestão de produtos e projetos, consultoria de gestão e empreendedorismo. Eles adquirem habilidades para gerenciar organizações técnicas, fomentar a inovação e lidar com tecnologias em rápida evolução e mercados dinâmicos. Os cursos especializados são flexíveis, permitindo que os alunos explorem e obtenham profundidade, entendendo as organizações técnicas para desenvolver uma cultura de inovação e empreendedorismo bem-sucedidos, juntamente com métodos de tomada de decisão sob incerteza, análise financeira e planejamento estratégico. Ciências Sociais Computacionais: A trilha da Ciência Social Computacional ensina aos alunos como aplicar métodos estatísticos e computacionais rigorosos para abordar problemas em economia, sociologia, ciência política e além. O programa prepara os alunos para um conjunto diversificado de carreiras em ciência de dados, tecnologia da informação e análise de políticas. O curso básico abrange conceitos estatísticos fundamentais, computação em grande escala e análise de rede. Através de disciplinas eletivas, os alunos podem explorar tópicos como design experimental, economia algorítmica e aprendizado de máquina. Decisão e Análise de Risco: Os alunos especializados em Decisão e Análise de Risco são preparados para carreiras, incluindo consultoria de gestão, análise de políticas e gerenciamento de riscos, aplicando análise de sistemas de engenharia para lidar com problemas complexos de gerenciamento técnico e econômico nos setores público e privado. Eles adquirem as habilidades para identificar e desenvolver oportunidades em situações de incerteza, reconhecendo e protegendo os riscos negativos. O trabalho de curso especializado inclui os fundamentos matemáticos para modelagem em ambientes dinâmicos incertos para avaliar e gerenciar oportunidades e riscos incertos, aplicativos para políticas públicas e uma oportunidade de trabalhar em um projeto de cliente sob orientação do corpo docente. Energia e Meio Ambiente: A trilha Energia e Meio Ambiente é destinada a estudantes interessados ​​em questões energéticas e ambientais, sob a perspectiva de políticas públicas, organizações não-governamentais ou corporações. Esta faixa inclui cursos básicos; cursos de análise econômica, recursos energéticos e análise de política energética / ambiental; e uma concentração projetada individualmente, tipicamente enfatizando política, estratégia ou tecnologia. Os seminários fornecem insights sobre a atual estratégia corporativa, políticas públicas e desenvolvimentos da comunidade de pesquisa. Cursos de projetos energéticos / ambientais dão prática na aplicação de metodologias e conceitos. Modelagem de Sistemas de Saúde: A trilha de Modelagem de Sistemas de Saúde é projetada para estudantes interessados ​​em operações e políticas de saúde. Os cursos nesta pista enfatizam a aplicação de análise matemática e econômica para problemas na política de saúde pública e na concepção e operação de serviços de saúde.

O mestrado é projetado para ser um programa de graduação terminal com foco profissional. O M. S. A graduação pode ser obtida em um ano acadêmico (três trimestres acadêmicos) de trabalho em tempo integral, embora a maioria dos alunos opte por concluir o programa em cinco trimestres acadêmicos, ou dezoito meses, e trabalhe como estagiário no trimestre de verão.

Requisitos de Background.

Espera-se que os alunos tenham concluído tanto a Álgebra Linear e Cálculo Diferencial MATH 51 de Diversas Variáveis, ou um curso de cálculo diferencial multivariado equivalente, e a Metodologia de Programação CS 106A, ou um curso de programação geral equivalente, antes de iniciar o estudo de pós-graduação. Esses cursos não contam para os requisitos de graduação.

Requisitos de Grau.

Os alunos devem ter um mínimo de 45 unidades do curso da seguinte forma:

Três cursos básicos (9-12 unidades) Uma concentração primária ou especializada (12-24 unidades) Um curso de projeto ou dois cursos de projetos integrados (0-8 unidades) Disciplinas eletivas (1-24 unidades; consulte as restrições abaixo)

Cursos Básicos (três cursos obrigatórios)

Otimização e Analytics (selecione um)

Organizações e Decisões (selecione uma)

Probabilidade (selecione um)

Concentrações Primárias.

Concentração de análise financeira (cinco cursos obrigatórios)

Operações e Análise de Concentração (quatro cursos exigidos)

Concentração de Gestão de Tecnologia e Engenharia (quatro cursos além do core requerido)

O curso usado para satisfazer o Núcleo de Organizações e Decisões satisfaz uma das áreas abaixo, mas as unidades do curso não contam duas vezes.

Concentrações Especializadas (deve ter aprovação do orientador acadêmico)

Ciências Sociais Computacionais (quatro cursos obrigatórios)

Análise de Decisão e Análise de Risco (quatro cursos obrigatórios)

Energia e Meio Ambiente Concentração (seis cursos exigidos)

Concentração de Modelagem de Sistemas de Saúde (quatro cursos exigidos)

Selecione um curso de projeto ou dois cursos de projetos integrados; pode contar duas vezes como parte do núcleo ou concentração.

Requisitos adicionais.

Pelo menos 45 unidades devem estar em cursos numerados de 100 e acima. O programa de graduação deve ser preenchido com uma média de notas (GPA) de 3.0 ou superior. Pelo menos 27 unidades devem estar em cursos numerados 200 e acima em MS & amp; E, tiradas por um grau de letra e um mínimo de duas unidades cada. Pelo menos 36 unidades classificadas por letras devem estar em MS & amp; E ou campos estreitamente relacionados. Campos intimamente relacionados incluem qualquer departamento da Escola de Engenharia, matemática, estatística, economia, sociologia, psicologia ou negócios. Todos os cursos usados ​​para satisfazer os requisitos essenciais, de concentração ou de projeto devem ser tomados para um grau de letra. Um máximo de três unidades de cursos de uma unidade, como seminários, colóquios, workshops, em qualquer departamento, incluindo MS e 208A, B e C, Treinamento Prático Curricular. Um máximo de 18 unidades de opção sem grau (NDO) através do Centro de Stanford para o Desenvolvimento Profissional (SCPD). Os cursos realizados em Saúde e Desempenho Humano (Atletismo, Esportes Clubes, Artes Marciais, Educação ao Ar Livre, Educação Física e Educação de Bem-Estar) não podem ser aplicados em relação ao curso.

Educação profissional.

O Centro de Stanford para Desenvolvimento Profissional (SCPD) oferece oportunidades para funcionários de algumas empresas locais e remotas fazerem cursos em Stanford.

O Honors Cooperative Program (HCP) oferece oportunidades para que os funcionários das empresas membros da SCPD ganhem um M. S. grau, durante um período mais longo, tomando um ou dois cursos por trimestre acadêmico. Alguns cursos são oferecidos apenas no campus; Os alunos do HCP podem frequentar esses cursos em Stanford para atender aos requisitos de graduação. É possível concluir este programa como um aluno de HCP remoto, embora as ofertas remotas sejam limitadas. Os estudantes devem solicitar um programa de graduação através do processo de inscrição padrão e devem cumprir os prazos padrão de inscrição.

A opção non-degree (NDO) permite que funcionários de algumas empresas locais façam cursos de crédito nos sites de suas empresas antes de serem admitidos em um programa de graduação. Os estudantes se inscrevem para fazer cursos NDO a cada trimestre através do Stanford Center for Professional Development. Até 18 unidades tomadas como estudante NDO podem ser aplicadas em um programa de graduação. Para obter informações adicionais sobre o processo de aplicação e prazos da NDO, consulte o site da SCPD ou entre em contato com a SCPD em (650) 725-3000.

Certificado.

O departamento oferece um programa de certificação dentro da estrutura do programa NDO. Um certificado pode ser obtido com o preenchimento de três cursos básicos MS & amp; E, além de um curso MS e E eletivo para um total de quatro cursos. Para mais informações, consulte scpd. stanford. edu/scpd/programs/certs/managementSci. htm.

Programa de Mestrado Dual.

O programa de graduação dupla permite que um pequeno grupo de estudantes de pós-graduação obtenha dois graus de mestrado simultaneamente. Os alunos preenchem os requisitos do curso para cada departamento. Um total de 90 unidades é necessário para concluir o mestrado duplo.

Para o grau duplo, a admissão em dois departamentos é necessária, mas é coordenada por membros designados de ambos os comitês de admissão que fazem recomendações aos comitês de seus respectivos departamentos. Os alunos podem se inscrever para apenas um departamento inicialmente. Após o primeiro trimestre em Stanford, os estudantes podem solicitar a admissão no segundo departamento.

Cada aluno do programa de graduação dupla tem um conselheiro em cada departamento.

Joint MS & amp; E Law Degrees.

A Faculdade de Direito e o Departamento de Ciências e Engenharia de Administração oferecem programas de graduação conjuntos que levam a um grau de doutorado e a um mestrado. grau em MS & amp; E, ou para um J. D. e Ph. D. em MS e E. Esses programas destinam-se a estudantes que desejam se preparar para carreiras em áreas relacionadas à lei e à tomada de decisões, formulação de políticas e conhecimento e habilidades para solução de problemas desenvolvidos no programa MS & amp; E. Os estudantes interessados ​​em um programa de graduação conjunto devem solicitar admissão separadamente à Faculdade de Direito e ao Departamento de Ciências e Engenharia de Administração e, como uma etapa adicional, devem garantir o consentimento de ambas as unidades acadêmicas para buscarem graus nessas unidades como parte de um curso. programa conjunto de graduação. O interesse em qualquer programa de graduação conjunto deve ser anotado nos formulários de admissão do estudante e pode ser considerado pelo comitê de admissão de cada programa. Alternativamente, um aluno matriculado na Faculdade de Direito ou na MS & E pode solicitar a admissão no outro programa e para o status de grau conjunto em ambas as unidades acadêmicas após o início dos estudos em qualquer programa.

Estudantes de graduação conjuntos podem optar por iniciar seu curso na Escola de Direito ou no MS & amp; Os alunos são designados para um comitê de programa conjunto composto de pelo menos um membro do corpo docente de Direito e um do MS & amp; E. Esta comissão planeja o programa do aluno em conjunto com o aluno. Os alunos devem estar matriculados em tempo integral na Faculdade de Direito para o primeiro ano de estudos de direito, e é recomendado que os alunos dediquem exclusivamente um trimestre de outono para o MS & amp; E M. S. programa para iniciar seu trabalho MS & amp; E. After that time, enrollment may be in MS&E or Law, and students may choose courses from either program regardless of where enrolled. A candidate in the joint J. D./Ph. D. program should spend a substantial amount of full time residency in MS&E. Students must satisfy the requirements for both the J. D. and the M. S. or Ph. D. degrees as specified in this bulletin or by the School of Law. The Law School may approve courses from MS&E or courses in the student’s MS&E program from outside of the Department of Management Science and Engineering that may count toward the J. D. degree, and MS&E may approve courses from the Law School that may count toward the M. S. or Ph. D. degree in MS&E. In either case, approval may consist of a list applicable to all joint degree students or may be tailored to each individual student’s program. The lists may differ depending on whether the student is pursuing an M. S. or a Ph. D. in MS&E.

In the case of a J. D./M. S. program, no more than 45 units of approved courses may be counted toward both degrees. In the case of a J. D./Ph. D. program, no more than 54 units of approved courses may be counted toward both degrees. In either case, no more than 36 units of courses that originate outside the Law School may count toward the law degree. To the extent that courses under this joint degree program originate outside the Law School but count toward the law degree, the law credits permitted under Section 17(1) of the Law School Regulations are reduced on a unit-per-unit basis, but not below zero. The maximum number of law school credits that may be counted toward the M. S. in MS&E is the greater of: (a) 18 units in the case of the M. S., or (b) the maximum number of hours from courses outside the department that an M. S. candidate in MS&E is permitted to count toward the applicable degree under general departmental guidelines or under departmental rules that apply in the case of a particular student.

Tuition and financial aid arrangements are normally through the school in which the student is then enrolled.

Joint MS&E and Master of Public Policy Degree.

MS MS&E students who wish to apply their analytical and management skills to the field of public policy can simultaneously pursue a master degree in MS&E and a master degree in Public Policy. The MPP is a two-year degree program, but MS MS&E students who pursue the joint program can earn both degrees in a minimum of two years, depending on prior preparation and elective choices, by counting up to 45 quarter units of course work toward both degrees. After admission to the Department of Management Science and Engineering, incoming or current MS students request that their application file be forwarded to the MPP program director for review.

Students in the joint program normally will spend most of their first year taking MS&E core courses. The second year is typically devoted to the MPP core, concentration, and practicum. The joint degree requires 90 quarter units. Tuition for the first year of study is paid at the Graduate Engineering rate, the remaining time at the graduate rate.

Doctor of Philosophy in Management Science and Engineering.

University requirements for the Ph. D. degree are described in the “Graduate Degrees” section of this bulletin.

The Ph. D. degree in MS&E is intended for students primarily interested in a career of research and teaching, or high-level technical work in universities, industry, or government. The program requires three years of full-time graduate study, at least two years of which must be at Stanford. Typically, however, students take four to five years after entering the program to complete all Ph. D. requisitos. The Ph. D. is organized around the expectation that the students acquire a certain breadth across all areas of the department, and depth in one of them. The current areas are:

Computational Social Science Decision and Risk Analysis Energy and Environmental Policy Financial Analytics Health Policy National Security Policy Operations Management Optimization and Stochastics Organizations Strategy, Innovation, and Entrepreneurship.

Doctoral students are required to take a number of courses, both to pass a qualifying exam in one of these areas, and to complete a dissertation based on research which must make an original contribution to knowledge.

Each student admitted to the Ph. D. program must satisfy a breadth requirement and pass a qualification procedure. The purpose of the qualification procedure is to assess the student’s command of the field and to evaluate his or her potential to complete a high-quality dissertation in a timely manner. The student must complete specified course work in one of the areas of the department.

The qualification decision is based on the student’s course work and grade point average (GPA), on the one or two preliminary papers prepared by the student with close guidance from two faculty members, at least one of whom must be an MS&E faculty member, the student’s performance in an area examination or defense of the written paper(s), and an overall assessment by the faculty of the student's ability to conduct high-quality Ph. D. research. Considering this evidence, the department faculty vote on advancing the student to candidacy in the department at large. The Ph. D. requires a minimum of 135 units, up to 45 units of which may be transferred from another graduate program.

All courses used to satisfy breadth and depth requirements must be taken for a letter grade, if the letter graded option is available. Prior to candidacy, at least 3 units of work must be taken with each of four Stanford faculty members. Finally, the student must pass a University oral examination and complete a Ph. D. dissertation. During the course of the Ph. D. program, students who do not have a master’s degree are strongly encouraged to complete one, either in MS&E or in another Stanford department.

Breadth Requirement.

All first year students are required to attend and participate in MS&E 302 Fundamental Concepts in Management Science and Engineering , which meets in the Autumn Quarter.

Each course session is devoted to a specific MS&E Ph. D. research area. At a given session several advanced P..hD students in that area make carefully prepared presentations designed for first-year doctoral students regardless of area. The presentations are devoted to: (a) illuminating how people in the area being explored that day think about and approach problems, and (b) illustrating what can and cannot be done when addressing problems by deploying the knowledge, perspectives, and skills acquired by those who specialize in the area in question.

Faculty in the focal area of the week comment on the student presentations. The rest of the session is devoted to questions posed and comments made by the first year Ph. D. estudantes.

During the last two weeks of the quarter, groups of first year students make presentations on how they would approach a problem drawing on two or more of the perspectives to which they have been exposed earlier in the class.

Attendance is mandatory and performance is assessed on the basis of the quality of the students’ presentations and class participation.

Qualification Procedure Requirements.

The qualification procedure is based on depth in an area of the student’s choice and preparation for dissertation research. The qualification process must be completed by the end of the month of May of the student’s second year of graduate study in the department. The performance of all doctoral students is reviewed every year at a department faculty meeting at the end of May or beginning of June. Ph. D. qualification decisions are made at that time and individual feedback is provided.

The Ph. D. qualification requirements comprise these elements:

Courses and GPA: Students must complete the depth requirements of one of the areas of the MS&E department. (The Ph. D. area course requirements are below).

All courses used to satisfy depth requirements must be taken for a letter grade, if the letter graded option is available. Course substitutions may be approved by the doctoral program adviser or the MS&E dissertation adviser on the candidacy form or on a request for graduate course waiver/substitution form. A student must maintain a GPA of at least 3.4 in the set of all courses taken by the student within the department. The GPA is computed on the basis of the nominal number of units for which each course is offered. Paper(s): A student may choose between two options. The first option involves one paper supervised by a primary faculty adviser and a second faculty reader. This paper should be written in two quarters. The second option involves two shorter sequential tutorials, with two different faculty advisers. Each tutorial should be completed in one quarter. In both options, the student chooses the faculty adviser(s)/reader with the faculty members’ consent.. There must be two faculty members, at least one of whom must be an MS&E faculty member, supervising and evaluating this requirement for advancement to candidacy. The paper/tutorials must be completed before the Spring Quarter of the student’s second year of graduate study in the department if the student's qualifying exam is during the Spring Quarter, and before the end of May of that year otherwise. A student may register for up to 3 units per tutorial and up to 6 units for a paper. Area Qualification: In addition, during the second year, a student must pass an examination in one of the areas of the MS&E department, or defense of the written paper(s). The student chooses the area/program in which to take the examination. This area examination is written, oral, or both, at the discretion of the area faculty administering the exam. Most areas offer the qualifying exam only once per year, which may be early in the second year.

Degree Progress and Student Responsibility.

Each student’s progress is reviewed annually by the MS&E faculty. Typically, this occurs at a faculty meeting at the end of Spring Quarter, and email notifications are sent over the summer.

First-year students should complete 30 units of breadth and depth courses, including MS&E 302 , and develop relationships with faculty members who might serve as dissertation adviser and reading committee. Second-year students should complete most, if not all, of the required depth courses, work with two faculty members, at least one of whom must be an MS&E faculty member, on tutorials/research paper, and pass an area qualifying exam. Most areas offer the qualifying exam only once per year, which may be early in the second year. Students should continue to develop relationships with faculty members who might serve as dissertation advisers and reading committee, and select a dissertation adviser before the beginning of the third year. Third-year students should complete any remaining depth courses, select a dissertation topic, and make progress on the dissertation. Fourth-year students should select a reading committee, and complete, or nearly complete, the oral exam and dissertation.

It is the responsibility of the student to initiate each step in completing the Ph. D. programa.

It is strongly recommended that each student, in the first year of graduate study at Stanford, make it a special point to become well acquainted with MS&E faculty members and to seek advice and counsel regarding possible Ph. D. candidacy. A faculty member is more likely to accept the responsibility of supervising the research of a student whom he or she knows fairly well than a student whose abilities, initiative, and originality the faculty member knows less.

It is expected that advanced students regularly report to their full reading committee on the progress of their dissertation. It is also expected that the student avail him/herself of the different expertise represented on the committee continually. Each member of this committee must certify approval of both the scope and quality of the dissertation.

The doctoral dissertation reading committee consists of the principal dissertation adviser and two other readers. At least one member must be from the student’s major department.

As administered in this department, the University oral examination is a defense of the dissertation; however, the candidate should be prepared to answer any question raised by any members of the Academic Council who choose to be present. Students should schedule three hours for the oral examination, which usually consists of a 45-minute public presentation, followed by closed-session questioning of the examinee by the committee, and committee deliberation. The University oral examination may be scheduled after the dissertation reading committee has given tentative approval to the dissertation. The student must be enrolled in the quarter of their oral examination.

The examining committee usually consists of the three members of the reading committee as well as a fourth faculty member and an orals chair. It is the responsibility of the student's adviser to find an appropriate orals chair. The chair must be an Academic Council member and may not be affiliated with either the Department of Management Science and Engineering nor any department in which the student's adviser has a regular appointment. Emeriti professors are eligible to serve as an orals chair. The student needs to reserve a room, and meet with the student services manager to complete the oral examination schedule and pick up other paper work. This paperwork, along with an abstract, needs to be delivered to the orals chair at least one week prior to the oral examination.

Requisitos do curso.

Computational Social Science.

The Computational Social Science track teaches students how to apply rigorous statistical and computational methods to address problems in economics, sociology, political science and beyond. The core course work covers fundamental statistical concepts, large-scale computation, and network analysis. Through electives, students can explore topics such as experimental design, algorithmic economics, and machine learning.

Computational Social Science Qualifying Procedure.

The student does two quarter-length tutorials with CSS faculty. At the end of these tutorials, the student must make a 45-minute presentation of one of their tutorials to a committee of three CSS faculty members. The student can do both tutorials with the same faculty member, in which case the presentation can be of the two tutorials together, and another committee member must be kept informed of the student’s progress on a regular basis during the two quarters. The presentation should take place in the Spring Quarter of the student's second year, or earlier. The presentation must include original research or promising directions towards original research. During this presentation, the student must also provide the name of their chosen focus area, and the list of courses that the student has completed and intends to complete in the core as well as in the chosen focus area. The committee then makes a recommendation to the CSS area and the MS&E department regarding qualification of the student for the Ph. D. program in CSS.

Decision Analysis and Risk Analysis.

The finance area focuses on the quantitative and statistical study of financial risks, institutions, markets, and technology. Students take courses in probability, statistics, optimization, finance, economics, and computational mathematics as well as a variety of other courses. Recent dissertation topics include studies of machine learning methods for risk management; systemic financial risk; algorithmic trading; optimal order execution; large-scale portfolio optimization; mortgage markets; and statistical testing of financial models. PhD students in the area typically are affiliated with the Center for Financial and Risk Analytics (CFRA).

Finance Qualifying Procedure.

In addition to beginning an appropriate course program, students must pass two quarters of tutorial and an oral examination to obtain qualification. The tutorials emphasize basic research skills. The oral examination emphasizes command of basic concepts as represented in the required courses as well as the modeling of practical situations.

Energy and Environment Policy (see Policy and Strategy)

Health Policy (see Policy and Strategy)

National Security Policy (see Decision and Risk Analysis)

Gerenciamento de operações.

Optimization and Stochastics.

In addition to the four core courses, students should take at least four 3-4 unit courses in some coherent area of specialization. The area of specialization may be methodological; examples include (but are not limited to) optimization, stochastic systems, stochastic control, algorithms, economic analysis, statistical inference, scientific computing, etc. The area of specialization could also have a significant modeling and application component, such as (but not limited to) information services, telecommunications, financial engineering, supply chains, health care, energy, etc. Independent of the choice of specialization, students are encouraged to take a range of courses covering methodology, modeling, and applications. Any MS&E courses satisfying this requirement must be at the 300-level, while courses outside MS&E must be at a comparable level. Students are expected to earn a letter grade of A - or better in all courses counted for the requirements. A student's plan for completing these requirements must be discussed with and approved by their faculty adviser by the beginning of Autumn Quarter of their second year.

Optimization and Stochastics Qualifying Procedure.

Students take the area qualifying exam at the beginning of their second year of study. The qualifying exam consists of two written exams: one in Optimization and one in Stochastic Systems. The first exam covers the material in MS&E 310 and related prerequisites. The second exam covers the material in MS&E 321 and related prerequisites.

The student does two quarter-length tutorials with Optimization and Stochastics faculty (or affiliated faculty). A written report approved by the supervising faculty member is required on each tutorial. In addition, at the end of the second year, students are expected to make a 30-minute presentation to the broader Optimization and Stochastics faculty. The presentation must include original research or promising directions towards original research. The student can do both tutorials with the same faculty member; in this case a single written report is sufficient, and the presentation can be of the two tutorials together.

Organizations, Strategy, Innovation, and Entrepreneurship.

In their first two years in the Ph. D. program, all students are expected to work with faculty on research. To ensure an early start, all students must work at least 25% of their time in their first year as a research assistant with a faculty member. Students on fellowships can earn course credit for the work. With approval from the students' adviser, one quarter of the requirement may be fulfilled by working as a Course Assistant (CA).

Ph. D. students in organizational behavior must take 3 courses in statistics and research methods. Two of these courses must be statistics courses.

Ph. D. students are required to take a minimum of 2 advanced-content courses chosen with input from their adviser.

Students are expected to complete a yearly plan, of no more than two typed pages in length, detailing the student's plans for the next year in terms of education (e. g., courses and seminars), research (e. g., RAships), and teaching (e. g., TAships). This plan should be provided to the students' academic adviser for review no later than May 15 each calendar year.

Política e Estratégia.

The Policy and Strategy (P&S) Area addresses policy and strategy questions in a variety of organizational and societal settings. In order to approach interdisciplinary research questions in application domains as diverse as energy, environment, health, information technology, innovation, and government regulation, P&S faculty members rely on a broad range of analytical and empirical tools, such as decision analysis, optimization and operations research methods, formal economic modeling, econometrics, case studies, and simulation. After having been exposed to foundational knowledge of economics, strategy, and organizational theory, doctoral students in the P&S Area can select from a variety of courses to deepen their understanding of the specific application domains. The P&S Area's mission is to provide a first-class learning and research environment preparing doctoral students for careers at research universities, government institutions, and in the private sector.

Students are expected to complete a yearly plan, of no more than two typed pages in length, detailing the student's plans for the next year in terms of education (e. g., courses and seminars), research (e. g., RAships), and teaching (e. g., TAships). This plan should be provided to the students' academic adviser for review no later than May 15 each calendar year.

Policy and Strategy Qualifying Procedure.

Advancement to Ph. D. candidacy is determined at the end of the student’s second year of studies, based on the following three components:

the student’s overall grade point average in the program (a GPA of 3.5 or higher is required); a second-year research paper that is written by the student under the supervision of a faculty member, and that is presented to examining faculty members in the second year; a written and an oral qualifying examination taken by the student in the spring quarter of the second year.

Ph. D. Minor in Management Science and Engineering.

Students pursuing a Ph. D. in another department who wish to receive a Ph. D. minor in Management Science and Engineering should consult the MS&E student services office. A minor in MS&E may be obtained by completing 20 units of approved graduate-level MS&E courses, of which at least 6 units must be at the 300-level. Courses approved for the minor must form a coherent program, and include a breadth of courses from across the department. The program must include a minimum of 16 letter-graded units, and a minimum grade point average of 3.3 must be achieved in these courses.

Emeriti: (Professors) James L. Adams, Stephen R. Barley, Richard W. Cottle, B. Curtis Eaves, Warren H. Hausman, Frederick S. Hillier, Donald L. Iglehart, David G. Luenberger, Michael M. May, William J. Perry, David A. Thompson; (Professor, Research) Siegfried S. Hecker, Walter Murray, Michael A. Saunders,

Chair: Nicholas Bambos.

Professors: Nicholas Bambos, Margaret L. Brandeau, Kathleen M. Eisenhardt, Peter W. Glynn, Ashish Goel, Pamela J. Hinds, Ronald A. Howard, Riitta Katila, M. Elisabeth Paté-Cornell, Robert I. Sutton, James L. Sweeney, Benjamin Van Roy, Yinyu Ye.

Associate Professors: Jose Blanchet, Samuel S. Chiu, Charles E. Eesley, Kay Giesecke, Ramesh Johari, Amin Saberi, Ross D. Shachter, Edison T. S. Tse.

Assistant Professors: Itai Ashlagi, Sharad Goel, Markus Pelger, Aaron Sidford, Johan Ugander, Melissa A. Valentine.

Professors (Research): John P. Weyant.

Professors (Teaching): Thomas H. Byers, Robert E. McGinn.

Professor of the Practice: Tina L. Seelig.

Courtesy Professors: Stephen P. Boyd, Douglas K. Owens, Walter Powell, Balaji Prabhakar, Alvin Roth, Tim Roughgarden.

MS&E 20. Discrete Probability Concepts And Models. 4 Units.

Fundamental concepts and tools for the analysis of problems under uncertainty, focusing on structuring, model building, and analysis. Examples from legal, social, medical, and physical problems. Topics include axioms of probability, probability trees, belief networks, random variables, conditioning, and expectation. The course is fast-paced, but it has no prerequisites.

MS&E 52. Introduction to Decision Making. 3 Units.

How to ensure focus, discipline, and passion when making important decisions. Comprehensive examples illustrate Decision Analysis fundamentals. Consulting case studies highlight practical solutions for real decisions. Student teams present insights from their analyses of decisions for current organizations. Topics: declaring when and how to make a decision, framing and structuring the decision basis, defining values and preferences, creating alternative strategies, assessing unbiased probabilistic judgments, developing appropriate risk/reward and portfolio models, evaluating doable strategies across the range of uncertain future scenarios, analyzing relevant sensitivities, determining the value of additional information, and addressing the qualitative aspects of communication and commitment to implementation. Not intended for MS&E majors.

MS&E 92Q. International Environmental Policy. 3 Units.

Preference to sophomores. Science, economics, and politics of international environmental policy. Current negotiations on global climate change, including actors and potential solutions. Sources include briefing materials used in international negotiations and the U. S. Congress.

MS&E 93Q. Nuclear Weapons, Energy, Proliferation, and Terrorism. 3 Units.

Preference to sophomores. At least 20 countries have built or considered building nuclear weapons. However, the paths these countries took in realizing their nuclear ambitions vary immensely. Por que esse é o caso? How do the histories, cultures, national identities, and leadership of these countries affect the trajectory and success of their nuclear programs? This seminar will address these and other questions about nuclear weapons and their proliferation. Students will learn the fundamentals of nuclear technology, including nuclear weapons and nuclear energy, and be expected to use this knowledge in individual research projects on the nuclear weapons programs of individual countries. Case studies will include France, UK, China, India, Israel, Pakistan, North Korea, South Africa, Libya, Iraq, and Iran, among others. Please note any language skills in your application. Recommended: 193 or 293.

Restricted to MS&E majors in their senior year. Students carry out a major project in groups of four, applying techniques and concepts learned in the major. Project work includes problem identification and definition, data collection and synthesis, modeling, development of feasible solutions, and presentation of results. Service Learning Course (certified by Haas Center). Satisfies the WIM requirement for MS&E majors.

MS&E 111. Introduction to Optimization. 3-4 Units.

Formulation and computational analysis of linear, quadratic, and other convex optimization problems. Applications in machine learning, operations, marketing, finance, and economics. Prerequisite: CME 100 or MATH 51.

MS&E 111X. Introduction to Optimization (Accelerated). 3-4 Units.

Optimization theory and modeling. The role of prices, duality, optimality conditions, and algorithms in finding and recognizing solutions. Perspectives: problem formulation, analytical theory, computational methods, and recent applications in engineering, finance, and economics. Theories: finite dimensional derivatives, convexity, optimality, duality, and sensitivity. Methods: simplex and interior-point, gradient, Newton, and barrier. Prerequisite: CME 100 or MATH 51 or equivalent.

MS&E 112. Mathematical Programming and Combinatorial Optimization. 3 Units.

Combinatorial and mathematical programming (integer and non-linear) techniques for optimization. Topics: linear program duality and LP solvers; integer programming; combinatorial optimization problems on networks including minimum spanning trees, shortest paths, and network flows; matching and assignment problems; dynamic programming; linear approximations to convex programs; NP-completeness. Hands-on exercises. Prerequisites: 111 or MATH 103, CS 106A or X.

Same as: MS&E 212.

Concepts and tools for the analysis of problems under uncertainty, focusing on focusing on structuring, model building, and analysis. Examples from legal, social, medical, and physical problems. Topics include axioms of probability, probability trees, random variables, distributions, conditioning, expectation, change of variables, and limit theorems. Prerequisite: CME 100 or MATH 51.

MS&E 121. Introduction to Stochastic Modeling. 4 Units.

Stochastic processes and models in operations research. Discrete and continuous time parameter Markov chains. Queuing theory, inventory theory, simulation. Prerequisite: 120, 125, or equivalents.

MS&E 125. Introduction to Applied Statistics. 4 Units.

An increasing amount of data is now generated in a variety of disciplines, ranging from finance and economics, to the natural and social sciences. Making use of this information, however, requires both statistical tools and an understanding of how the substantive scientific questions should drive the analysis. In this hands-on course, we learn to explore and analyze real-world datasets. We cover techniques for summarizing and describing data, methods for statistical inference, and principles for effectively communicating results. Prerequisite: 120, CS 106A, or equivalents.

MS&E 130. Information Networks and Services. 3 Units.

Architecture of the Internet and performance engineering of computer systems and networks. Switching, routing and shortest path algorithms. Congestion management and queueing networks. Peer-to-peer networking. Wireless and mobile networking. Information service engineering and management. Search engines and recommendation systems. Reputation systems and social networking technologies. Security and trust. Information markets. Select special topics and case studies. Prerequisites: 111, 120, and CS 106A.

This course provides an introduction to how networks underly our social, technological, and natural worlds, with an emphasis on developing intuitions for broadly applicable concepts in network analysis. The course will include: an introduction to graph theory and graph concepts; social networks; information networks; the aggregate behavior of markets and crowds; network dynamics; information diffusion; the implications of popular concepts such as "six degrees of separation", the "friendship paradox", and the "wisdom of crowds".

MS&E 140. Accounting for Managers and Entrepreneurs. 3-4 Units.

Não-majores e menores que tenham tomado ou estão tendo contabilidade elementar não devem se inscrever. Introdução aos conceitos contábeis e às características operacionais dos sistemas contábeis. Os princípios de contabilidade financeira e de custos, concepção de sistemas de contabilidade, técnicas de análise e controle de custos. Interpretação e uso de informações contábeis para tomada de decisão. Projetado para o usuário de informações contábeis e não como uma introdução a uma carreira profissional de contabilidade. Inscrição limitada. Admissão por ordem de inscrição.

Same as: MS&E 240.

MS&E 140X. Financial Accounting Concepts and Analysis. 2 Units.

Curso introdutório em contabilidade financeira. A contabilidade é referida como a linguagem dos negócios. Desenvolver a capacidade dos alunos de ler, entender e usar as demonstrações financeiras da empresa. Compreender o mapeamento entre os eventos econômicos subjacentes e as demonstrações financeiras e como esse mapeamento pode afetar as inferências sobre a lucratividade futura da empresa. Introdução à medição e relato do ciclo operacional; o processo de preparação e apresentação de demonstrações financeiras primárias; o julgamento envolvido e a discrição permitida na tomada de decisões contábeis; os efeitos do critério contábil sobre a qualidade das informações financeiras (relatadas); e os fundamentos da análise das demonstrações financeiras. O tempo de aula será alocado para uma combinação de palestras, casos e discussões de casos. Projeto Capstone analisando as finanças da empresa no final do trimestre. Inscrição limitada. Admissão por ordem de inscrição.

MS&E 145. Introduction to Investment Science. 4 Units.

Introduction to the financial concepts and empirical evidence that are useful for investment decisions. Time-value of money: understanding basic interest rates, evaluating investments with present value and internal rates of return, fixed-income securities. Risk-return tradeoff and pricing models: mean variance optimization and portfolio choice, capital asset pricing theory and extensions. No prior knowledge of finance is required. Concepts are applied in a stock market simulation with real data. Prerequisites: basic preparation in probability, statistics, and optimization as covered e. g. in 111 and 120.

MS&E 146. Corporate Financial Management. 4 Units.

Key functions of finance in both large and small companies, and the core concepts and key analytic tools that provide their foundation. Making financing decisions, evaluating investments, and managing cashflow, profitability and risk. Designing performance metrics to effectively measure and align the activities of functional groups and individuals within the firm. Structuring relationships with key customers, partners and suppliers. Prerequisite: 145, 245A, 245G or equivalent.

MS&E 147. Finance and Society for non-MBAs. 4 Units.

The financial system is meant to help people, businesses, and governments fund, invest, and manage risks, but it is rife with conflicts of interests and may allow people with more information and control to harm those with less of both. In this interdisciplinary course we explore the forces that shape the financial system and how individuals and society can benefit most from this system without being unnecessarily harmed and endangered. Topics include the basic principles of investment, the role and ¿dark side¿ of debt, corporations and their governance, banks and other financial institutions, why effective financial regulations are essential yet often fail, and political and ethical issues in finance. The approach will be rigorous and analytical but not overly technical mathematically. Prerequisite: ECON 1.

Explores the ethical reasoning needed to make banking, insurance and financial services safer, fairer and more positively impactful. Weighs tradeoffs in how money is created, privileging some, under-privileging others, using market mechanisms for transforming and trading financial risk, return, maturity and asset types. Technology is changing banks, financial markets, insurance and money. Like technology for medicine, finance is being rebuilt as machine learned code, algorithmic investment rules and regulatory monitoring. Risk models can be built to detect fraud and ethical lapses, or to open doors for them. Investment valuation models can optimize short term or long term returns, by optimizing or ignoring environmental and social impacts. Transparency or opacity can be the norm. Transforming finance through engineering requires finding, applying and evolving codes of professional conduct to make sure that engineers use their skills within legal and ethical norms. Daily, financial engineers focus on two horizons: on the floor, we stand on the bare minimum standards of conduct, and on the ceiling, we aim for higher ethical goals that generate discoveries celebrated though individual fulfillment and TED Talks. Stanford engineers, computer scientists, data scientists, mathematicians and other professionals are building systems for lending, investment and portfolio management decisions that determine future economic and social growth. This course uses the case method to preview intersecting codes of conduct, legal hurdles and ethical impact opportunities, and creates as a safe academic setting for seeing career-limiting ethical stop signs (red lights) and previewing ¿what¿s my life all about¿ events, as unexpected threats or surprising ah-ha moments. Guest speakers will highlight real life situations, lawsuits and other events where ethics of financial engineering was a predominant theme, stumbling block or humanitarian opportunity.

Introduction to hedge fund management. Students actively manage the $1MM Stanford Kudla Fund employing Equity Long/Short, Macro and Quantitative Investment Strategies. Modeled after a hedge fund partnership culture, participation involves significant time commitment, passion for investing, and uncommon teamwork and communication skills. Open to advanced undergraduate and graduate students with continuing participation expectation. Limited to 12 students. Enrollment by application and permission of Instructor.

MS&E 152. Introduction to Decision Analysis. 3-4 Units.

How to make good decisions in a complex, dynamic, and uncertain world. People often make decisions that on close examination they regard as wrong. Decision analysis uses a structured conversation based on actional thought to obtain clarity of action in a wide variety of domains. Topics: distinctions, possibilities and probabilities, relevance, value of information and experimentation, relevance and decision diagrams, risk attitude.

MS&E 175. Innovation, Creativity, and Change. 3-4 Units.

Resolução de problemas nas organizações; habilidades de criatividade e inovação; ferramentas de pensamento; organizações criativas, equipes, indivíduos e comunidades. Inscrição limitada.

Este curso experiencial explora uma ampla gama de ferramentas que são usadas para aprimorar a inovação e como essas ferramentas são aplicadas nas disciplinas de engenharia. Usando workshops, demonstrações e visitas de campo, os alunos aprenderão como a solução criativa de problemas é implantada em campos de engenharia e, em parceria com o Laboratório Virtual de Interação Humana de Stanford, expandem suas próprias habilidades criativas de resolução de problemas com experiências de realidade virtual que aumentam sua imaginação. Inscrição limitada. Admissão por aplicação.

MS&E 178. The Spirit of Entrepreneurship. 2 Units.

Existe mais no empreendedorismo do que inventar a melhor ratoeira? This course uses the speakers from the Entrepreneurial Thought Leader seminar (MS&E472) to drive research and discussion about what makes an entrepreneur successful. Os tópicos incluem financiamento de risco, modelos de negócios e dinâmicas interpessoais no ambiente de inicialização. Students meet before and after MS&E 472 to prepare for and debrief after the sessions. Inscrição limitada a 60 alunos. Application available at first class session.

MS&E 180. Organizations: Theory and Management. 4 Units.

Apenas para alunos de graduação; preference to MS&E majors. Teoria da organização clássica e contemporânea; o comportamento de indivíduos, grupos e organizações. Inscrição limitada. Students must attend and complete an application at the first class session.

MS&E 181. Issues in Technology and Work. 3 Units.

How changes in technology and organization are altering work and lives, and how understanding work and work practices can help design better technologies and organizations. Topics include job and organization design; collaboration and networking tools; distributed and virtual organizations; project work, taskification, and the platform economy; the blurring of boundaries between work and private life; monitoring and surveillance in the workplace; trends in skill requirements and occupational structures; downsizing and its effects on work systems; the growth of contingent employment, telecommuting, and the changing nature of labor relations. Inscrição limitada.

Leadership in action is designed with a significant lab component in which students will be working on leadership projects throughout the quarter. The projects will provide students with hands on experience trying out new leadership behaviors in a variety of situations, along with the opportunity to reflect on these experience and, in turn, expand their leadership skills. Inscrição limitada. Students must attend first class session.

MS&E 184. Future of Work: Issues in Organizational Learning and Design. 4 Units.

The nature of work is changing, with consequences for how we structure jobs, careers, teams, organizations, and labor markets. This class teaches analytical tools from organizational behavior, social psychology, and socially distributed cognition to empower students to analyze and understand the changes and their consequences. Enrollment Limited. Prerequisite: 180.

Issues, challenges, and opportunities facing workers, teams, and organizations working across national boundaries. Topics include geographic distance, time zones, language and cultural differences, technologies to support distant collaboration, team dynamics, and corporate strategy. Inscrição limitada. Recommended: 180.

Grand challenges of our time will demand entirely new ways of thinking about when, how, and under what conditions organizations are "doing good" and what effects that has. This class will focus on the role of organizations in society, the challenges organizations face in attempting to "do good", limitations to current ways of organizing, and alternative ways to organize and lead organizations that are "good".

MS&E 190. Methods and Models for Policy and Strategy Analysis. 3 Units.

Guest lectures by departmental practitioners. Emphasis is on links among theory, application, and observation. Environmental, national security, and health policy; marketing, new technology, and new business strategy analyses. Comparisons between domains and methods.

MS&E 193. Technology and National Security. 3 Units.

Explores the relation between technology, war, and national security policy from early history to modern day, focusing on current U. S. national security challenges and the role that technology plays in shaping our understanding and response to these challenges. Topics include the interplay between technology and modes of warfare; dominant and emerging technologies such as nuclear weapons, cyber, sensors, stealth, and biological; security challenges to the U. S.; and the U. S. response and adaptation to new technologies of military significance.

Provides a solid foundation in understanding and modeling the dynamics of change. Differential equations are used as a mathematical language to facilitate discussions on dynamic phenomena. Develop mathematical tools to analyze the dynamic models, and use such tools to think about and manage the dynamics of change. The analytical part of the course will be on mathematical analysis of linear and nonlinear dynamic systems. The notions of equilibrium, stability, growth and limit cycle will be introduced and discussed in terms of examples in business competition, organizational hierarchy, population dynamics, social interactions, ecology and spread of epidemics. Introduction to Catastrophe Theory, which provides a mathematical model for certain discontinuous phenomena like the crash of the stock market and the extinction of species. Required project in dynamic system modeling. Prerequisite: MATH 104 or equivalent.

MS&E 202. Optimal Control of Dynamic Systems. 3 Units.

Provide a solid foundation in optimal control theory that can be applied to various disciplines. Topics covered are Maximum Principle, Dynamic Programming, Riccati Equation, Infinite time Optimal Control with time discounting, Infinite time differential games. The theory will be applied to optimal economic growth, resource management, production planning, dynamic pricing, financial planning, oligopoly competition, network platform competition. Prerequisite: MS&E 201.

MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Students may take each course once. To receive a permission code to enroll, please submit this form: web. stanford. edu/

lcottle/forms/CPTapp. fb with statement and offer letter.

MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Students may take each course once. To receive a permission code to enroll, please submit this form: web. stanford. edu/

lcottle/forms/CPTapp. fb with statement and offer letter.

MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Students may take each course once. To receive a permission code to enroll, please submit this form: web. stanford. edu/

lcottle/forms/CPTapp. fb with statement and offer letter.

MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Students may take each course once. To receive a permission code to enroll, please submit this form: web. stanford. edu/

lcottle/forms/CPTapp. fb with statement and offer letter.

MS&E 208E. Part-Time Practical Training. 1 Unit.

MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. O curso pode ser repetido para crédito. To receive a permission code to enroll, please submit this form: web. stanford. edu/

lcottle/forms/CPTapp. fb with statement and offer letter.

MS&E 211. Introduction to Optimization. 3-4 Units.

Formulation and computational analysis of linear, quadratic, and other convex optimization problems. Applications in machine learning, operations, marketing, finance, and economics. Prerequisite: CME 100 or MATH 51.

MS&E 211X. Introduction to Optimization (Accelerated). 3-4 Units.

Optimization theory and modeling. The role of prices, duality, optimality conditions, and algorithms in finding and recognizing solutions. Perspectives: problem formulation, analytical theory, computational methods, and recent applications in engineering, finance, and economics. Theories: finite dimensional derivatives, convexity, optimality, duality, and sensitivity. Methods: simplex and interior-point, gradient, Newton, and barrier. Prerequisite: CME 100 or MATH 51 or equivalent.

MS&E 212. Mathematical Programming and Combinatorial Optimization. 3 Units.

Combinatorial and mathematical programming (integer and non-linear) techniques for optimization. Topics: linear program duality and LP solvers; integer programming; combinatorial optimization problems on networks including minimum spanning trees, shortest paths, and network flows; matching and assignment problems; dynamic programming; linear approximations to convex programs; NP-completeness. Hands-on exercises. Prerequisites: 111 or MATH 103, CS 106A or X.

Same as: MS&E 112.

MS&E 213. Introduction to Optimization Theory. 3 Units.

Introduction of core algorithmic techniques and proof strategies that underlie the best known provable guarantees for minimizing high dimensional convex functions. Focus on broad canonical optimization problems and survey results for efficiently solving them, ultimately providing the theoretical foundation for further study in optimization. In particular, focus will be on first-order methods for both smooth and non-smooth convex function minimization as well as methods for structured convex function minimization, discussing algorithms such as gradient descent, accelerated gradient descent, mirror descent, Newton's method, interior point methods, and more. Prerequisite: multivariable calculus and linear algebra.

Concepts and tools for the analysis of problems under uncertainty, focusing on model building and communication: the structuring, processing, and presentation of probabilistic information. Examples from legal, social, medical, and physical problems. Spreadsheets illustrate and solve problems as a complement to analytical closed-form solutions. Topics: axioms of probability, probability trees, random variables, distributions, conditioning, expectation, change of variables, and limit theorems. Prerequisite: multivariable calculus and linear algebra. Recommended: knowledge of spreadsheets.

Focus is on time-dependent random phenomena. Topics: discrete and continuous time Markov chains, renewal processes, queueing theory, and applications. Emphasis is on building a framework to formulate and analyze probabilistic systems. Prerequisite: 220 or equivalent, or consent of instructor.

Discrete-event systems, generation of uniform and non-uniform random numbers, Monte Carlo methods, programming techniques for simulation, statistical analysis of simulation output, efficiency-improvement techniques, decision making using simulation, applications to systems in computer science, engineering, finance, and operations research. Prerequisites: working knowledge of a programming language such as C, C++, Java, Python, or FORTRAN; calculus-base probability; and basic statistical methods.

This course is about understanding "small data": these are datasets that allow interaction, visualization, exploration, and analysis on a local machine. The material provides an introduction to applied data analysis, with an emphasis on providing a conceptual framework for thinking about data from both statistical and machine learning perspectives. Topics will be drawn from the following list, depending on time constraints and class interest: approaches to data analysis: statistics (frequentist, Bayesian) and machine learning; binary classification; regression; bootstrapping; causal inference and experimental design; multiple hypothesis testing. Class lectures will be supplemented by data-driven problem sets and a project. Prerequisites: CME 100 or MATH 51; 120, 220 or STATS 116; experience with R at the level of CME/STATS 195 or equivalent.

MS&E 231. Introduction to Computational Social Science. 3 Units.

With a vast amount of data now collected on our online and offline actions -- from what we buy, to where we travel, to who we interact with -- we have an unprecedented opportunity to study complex social systems. This opportunity, however, comes with scientific, engineering, and ethical challenges. In this hands-on course, we develop ideas from computer science and statistics to address problems in sociology, economics, political science, and beyond. We cover techniques for collecting and parsing data, methods for large-scale machine learning, and principles for effectively communicating results. To see how these techniques are applied in practice, we discuss recent research findings in a variety of areas. Prerequisites: introductory course in applied statistics, and experience coding in R, Python, or another high-level language.

An introduction to economic analysis for modern online services and systems. Topics include: Examples of networked markets. Online advertising. Recommendation and reputation systems. Pricing digital media. Network effects and network externalities. Social learning and herd behavior. Markets and information. Prerequisites: CME 100 or MATH 51, and probability at the level of MS&E 220 or equivalent. No prior economics background will be assumed; requisite concepts will be introduced as needed.

MS&E 234. Data Privacy and Ethics. 3 Units.

This course engages with difficult ethical challenges in the modern practice of data science. The three main focuses are data privacy, personalization and targeting algorithms, and online experimentation. The focus on privacy will raise both practical and theoretical considerations. As part of the module on experimentation, students will be required to complete the Stanford IRB training for social and behavioral research. The course will assume a strong familiarity with the practice of machine learning and and data science. Recommended: MS&E 226, MS&E 231, CS 229, or equivalents.

Explores the underlying network structure of social, economic, and technological world using techniques from graph theory and economics, as well as machine learning and data analysis. Prerequisite: 226, CME 195, or equivalents.

MS&E 237. Networks, Markets, and Crowds. 3 Units.

The course explores the underlying network structure of our social, economic, and technological worlds and uses techniques from graph theory and economics to examine the structure & evolution of information networks, social contagion, the spread of social power and popularity, and information cascades. Prerequisites: basic graph and probability theory.

MS&E 238. Leading Trends in Information Technology. 3 Units.

Focuses on new trends and disruptive technologies in IT. Emphasis on the way technologies create a competitive edge and generate business value. Broad range of views presented by guest speakers, including top level executives of technology companies, and IT executives (e. g. CIOs) of Fortune 1000 companies. Special emphasis in technologies such as Cloud Computing, Artificial Intelligence, Security, Mobility, and Big Data.

MS&E 238A. Leading Trends in Information Technology. 1 Unit.

Focuses on new trends and disruptive technologies in IT. Emphasis on the way technologies create a competitive edge and generate business value. Broad range of views presented by guest speakers, including top level executives of technology companies, and IT executives (e. g. CIOs) of Fortune 1000 companies. Special emphasis in technologies such as Cloud Computing, Artificial Intelligence, Security, Mobility, and Big Data.

MS&E 240. Accounting for Managers and Entrepreneurs. 3-4 Units.

Não-majores e menores que tenham tomado ou estão tendo contabilidade elementar não devem se inscrever. Introdução aos conceitos contábeis e às características operacionais dos sistemas contábeis. Os princípios de contabilidade financeira e de custos, concepção de sistemas de contabilidade, técnicas de análise e controle de custos. Interpretação e uso de informações contábeis para tomada de decisão. Projetado para o usuário de informações contábeis e não como uma introdução a uma carreira profissional de contabilidade. Inscrição limitada. Admissão por ordem de inscrição.

Same as: MS&E 140.

Principal methods of economic analysis of the production activities of firms, including production technologies, cost and profit, and perfect and imperfect competition; individual choice, including preferences and demand; and the market-based system, including price formation, efficiency, and welfare. Practical applications of the methods presented. Recommended: 211, ECON 50.

MS&E 243. Energy and Environmental Policy Analysis. 3 Units.

Concepts, methods, and applications. Energy/environmental policy issues such as automobile fuel economy regulation, global climate change, research and development policy, and environmental benefit assessment. Group project. Prerequisite: MS&E 241 or ECON 50, 51.

MS&E 244. Economic Growth and Development. 3 Units.

Formerly 249. What generates economic growth. Emphasis is on theory accompanied by intuition, illustrated with country cases. Topics: the equation of motion of an economy; optimal growth theory; calculus of variations and optimal control approaches; deriving the Euler and Pontriaguine equations from economic reasoning. Applications: former planned economies in Russia and E. Europe; the present global crisis: causes and consequences; a comparative study of India and China. The links between economic growth and civilization; the causes of the rise and decline of civilizations; lessons for the future. Intended for graduate students. Prerequisite: multivariate calculus and permission of instructor. To receive permission, submit an application at web. stanford. edu/

Basic concepts of modern quantitative finance and investments. Focus is on the financial theory and empirical evidence that are useful for investment decisions. Topics: basic interest rates; evaluating investments: present value and internal rate of return; fixed-income markets: bonds, yield, duration, portfolio immunization; term structure of interest rates; measuring risk: volatility and value at risk; designing optimal portfolios; risk-return tradeoff: capital asset pricing model and extensions. No prior knowledge of finance is required. Concepts are applied in a stock market simulation with real data. Prerequisite: basic preparation in probability, statistics, and optimization.

MS&E 245B. Advanced Investment Science. 3 Units.

Formerly MS&E 342. Topics: forwards and futures contracts, continuous and discrete time models of stock price behavior, geometric Brownian motion, Ito's lemma, basic options theory, Black-Scholes equation, advanced options techniques, models and applications of stochastic interest rate processes, and optimal portfolio growth. Computational issues and general theory. Teams work on independent projects. Prerequisite: 245A.

MS&E 246. Financial Risk Analytics. 3 Units.

Practical introduction to financial risk analytics. The focus is on data-driven modeling, computation, and statistical estimation of credit and market risks. Case studies based on real data will be emphasized throughout the course. Topics include mortgage risk, asset-backed securities, commercial lending, consumer delinquencies, online lending, derivatives risk. Tools from machine learning and statistics will be developed. Data sources will be discussed. The course is intended to enable students to design and implement risk analytics tools in practice. Prerequisites: MS&E 245A or similar, some background in probability and statistics, working knowledge of R, Matlab, or similar computational/statistical package.

MS&E 250A. Engineering Risk Analysis. 3 Units.

The techniques of analysis of engineering systems for risk management decisions involving trade-offs (technical, human, environmental aspects). Elements of decision analysis; probabilistic risk analysis (fault trees, event trees, systems dynamics); economic analysis of failure consequences (human safety and long-term economic discounting); and case studies such as space systems, nuclear power plants, and medical systems. Public and private sectors. Prerequisites: probability, decision analysis, stochastic processes, and convex optimization.

MS&E 250B. Project Course in Engineering Risk Analysis. 3 Units.

Students, individually or in groups, choose, define, formulate, and resolve a real risk management problem, preferably from a local firm or institution. Oral presentation and report required. Scope of the project is adapted to the number of students involved. Three phases: risk assessment, communication, and management. Emphasis is on the use of probability for the treatment of uncertainties and sensitivity to problem boundaries. Inscrição limitada. Prerequisites: MS&E 250A and consent of instructor.

MS&E 251. Introduction to Stochastic Control with Applications. 3 Units.

Focuses on conceptual foundation and algorithmic methodology of Dynamic Programming and Stochastic Control with applications to engineering, operations research, management science and other fields. Elaborates on the concept of probing, learning and control of stochastic systems, and addresses the practical application of the concept and methodology through the use of approximations. Prerequisites: 201, 221, or equivalents.

MS&E 252. Decision Analysis I: Foundations of Decision Analysis. 3-4 Units.

Coherent approach to decision making, using the metaphor of developing a structured conversation having desirable properties, and producing actional thought that leads to clarity of action. Socratic instruction; computational problem sessions. Emphasis is on creation of distinctions, representation of uncertainty by probability, development of alternatives, specification of preference, and the role of these elements in creating a normative approach to decisions. Information gathering opportunities in terms of a value measure. Relevance and decision diagrams to represent inference and decision. Principles are applied to decisions in business, technology, law, and medicine. See 352 for continuation.

The ethical responsibility for consequences of professional analysts who use technical knowledge in support of any individual, organization, or government. The means to form ethical judgments; questioning the desirability of physical coercion and deception as a means to reach any end. Human action and relations in society in the light of previous thought, and research on the desired form of social interactions. Attitudes toward ethical dilemmas through an explicit personal code.

MS&E 256. Technology Assessment and Regulation of Medical Devices. 3 Units.

Regulatory approval and reimbursement for new health technologies are critical success factors for product commercialization. This course explores the regulatory and payer environment in the U. S. and abroad, as well as common methods of health technology assessment. Students will learn frameworks to identify factors relevant to the adoption of new health technologies, and the management of those factors in the design and development phases of bringing a product to market through case studies, guest speakers from government (FDA) and industry, and a course project.

MS&E 256A. Technology Assessment and Regulation of Medical Devices. 1 Unit.

Regulatory approval and reimbursement for new medical technologies as a key component of product commercialization. The regulatory and payer environment in the U. S. and abroad, and common methods of health technology assessment. Framework to identify factors relevant to adoption of new medical devices, and the management of those factors in the design and development phases. Case studies; guest speakers from government (FDA) and industry.

MS&E 260. Introduction to Operations Management. 3 Units.

Operations management focuses on the effective planning, scheduling, and control of manufacturing and service entities. This course introduces students to a broad range of key issues in operations management. Topics include determination of optimal facility location, production planning, optimal timing and sizing of capacity expansion, and inventory control. Prerequisites: basic knowledge of Excel spreadsheets, probability.

MS&E 262. Gestão da cadeia de abastecimento. 3 Units.

Definition of a supply chain; coordination difficulties; pitfalls and opportunities in supply chain management; inventory/service tradeoffs; performance measurement and incentives. Global supply chain management; mass customization; supplier management. Design and redesign of products and processes for supply chain management; tools for analysis; industrial applications; current industry initiatives. Enrollment limited to 50. Admission determined in the first class meeting. Recommended: 260 or 261.

MS&E 263. Healthcare Operations Management. 3 Units.

With healthcare spending in the US exceeding 17% of GDP and growing, improvements in the quality and efficiency of healthcare services are urgently needed. This class focuses on the use of analytical tools to support efficient and effective delivery of health care. Topics include quality control and management, capacity planning, resource allocation, management of patient flows, and scheduling. Prerequisites: basic knowledge of Excel spreadsheets, probability, and optimization.

MS&E 265. Product Management Fundamentals. 3 Units.

Introduction to Product Management (PM). PM's define a product's functional requirements and lead cross functional teams responsible for development, launch, and ongoing improvement. The course uses a learning-by-doing approach covering the following topics: changing role of a PM at different stages of the product life cycle; techniques to understand customer needs and validate demand; user experience design and testing; role of detailed product specifications; waterfall and agile methods of software development. Group projects involve the specification of a software technology product though the skills taught are useful for a variety of product roles. No prior knowledge of design, engineering, or computer science required. Inscrição limitada. Application deadline March 15: goo. gl/HoiXXk. Syllabus: goo. gl/TWPFfU.

MS&E 267. Service Operations and the Design of Marketplaces. 3 Units.

The service sector accounts for approximately 80% of GDP and employment in the US. It is therefore imperative to develop efficient and effective operations of services. The management of service operations can require quite different constraints and objectives than manufacturing operations. The course examines both traditional and new approaches for achieving operational competitiveness in service businesses including (online) marketplaces. Topics include the service concept and operations strategy, the design of effective service delivery systems, capacity management, queuing, quality, revenue management as well as concepts from the design of marketplaces such as matching, congestion and auctions.

MS&E 270. Strategy in Technology-Based Companies. 3-4 Units.

Apenas para estudantes de pós-graduação. Introdução aos conceitos básicos de estratégia, com ênfase em empresas de alta tecnologia. Tópicos: posicionamento competitivo, perspectivas baseadas em recursos, coopetição e definição de padrões e perspectivas de complexidade / evolução. Inscrição limitada. Students must attendnand complete an application at the first class session.

MS&E 271. Global Entrepreneurial Marketing. 3-4 Units.

Habilidades necessárias para comercializar novos produtos baseados em tecnologia para clientes em todo o mundo. Discussões sobre o método do caso. Os casos incluem startups e empresas globais de alta tecnologia. Temas do curso: kit de ferramentas de marketing, segmentação de mercados e clientes, marketing e gerenciamento de produtos, parceiros e distribuição, vendas e negociação e marketing de saída. Exame final para levar para casa baseado em equipe. Inscrição limitada.

MS&E 272. Entrepreneurship without Borders. 3-4 Units.

Como você cria uma start-up fora dos EUA? Quais são as questões exclusivas envolvidas na criação de uma startup de sucesso em economias emergentes como a China ou a Índia? O que é liderança empreendedora em um empreendimento que abrange as fronteiras dos países? O empreendedorismo ao estilo do Vale do Silício é possível em outros lugares? Como um empreendedor age de maneira diferente ao criar uma empresa em um ambiente institucional menos desenvolvido? Aprenda por meio da formação de equipes, um projeto de orientação orientado por mentores focado no desenvolvimento de startups de estudantes em mercados internacionais, estudos de caso, pesquisas sobre os aspectos internacionais do processo empreendedor e contatos com os principais empreendedores e capitalistas de risco que trabalham além das fronteiras. Somente para estudantes de pós-graduação, com preferência por engenheiros e graduados em ciências que buscam entender a formação de start-ups de alto impacto em contextos de economia emergente. Inscrição limitada.

MS&E 273. Technology Venture Formation. 3-4 Units.

Aberto a estudantes de pós-graduação interessados ​​em start-ups voltados para a tecnologia. Fornece a experiência de um empreendedor em estágio inicial em busca de investimento inicial, incluindo: formação de equipe, avaliação de oportunidades, desenvolvimento de clientes, estratégia de entrada no mercado e PI. A equipe de ensino inclui empreendedores em série e capitalistas de risco. Student teams validate the business model using R&D plans and financial projections, and define milestones for raising and using venture capital. O exame final é um pitch de investimento entregue a um painel de parceiros de alto nível da VC. Além de palestras, as equipes interagem com mentores e equipe de ensino semanalmente. Inscrição por aplicação: stanford. edu/class/msande273. Recomendado: 270, 271 ou equivalente.

MS&E 274. Dynamic Entrepreneurial Strategy. 3 Units.

Principalmente para estudantes de pós-graduação. How entrepreneurial strategy focuses on creating structural change or responding to change induced externally. Grabber-holder dynamics as an analytical framework for developing entrepreneurial strategy to increase success in creating and shaping the diffusion of new technology or product innovation dynamics. Topics: First mover versus follower advantage in an emerging market; latecomer advantage and strategy in a mature market; strategy to break through stagnation; and strategy to turn danger into opportunity. Modeling, case studies, and term project.

MS&E 275. Foundations for Large-Scale Entrepreneurship. 3 Units.

Explore the foundational and strategic elements needed for startups to be designed for "venture scale" at inception. Os temas incluem insights controversos e disruptivos, análise competitiva, efeitos de rede, projeto organizacional e implantação de capital. Estudos de caso, convidados especialistas e projetos de aprendizagem experiencial serão usados. Principalmente para estudantes de pós-graduação. Inscrição limitada. Admissão por aplicação. Recomendado: contabilidade básica.

MS&E 276. Entrepreneurial Management and Finance. 3 Units.

Somente para estudantes de pós-graduação, com preferência por engenheiros e estudantes de ciências. Ênfase na gestão de empresas em estágio inicial de alto crescimento, especialmente aquelas com produtos e serviços baseados na inovação. Os alunos trabalham em equipe para desenvolver habilidades e abordagens necessárias para se tornarem líderes e gerentes empresariais eficazes. Os tópicos incluem avaliação de risco, compreensão de modelos de negócios, análise de principais métricas operacionais, modelagem de fluxo de caixa e requisitos de capital, avaliação de fontes de financiamento, estruturação e negociação de investimentos, gestão de cultura organizacional e incentivos, gerenciamento da interação entre propriedade e crescimento e tratamento de adversidades e falhas . Inscrição limitada. Admissão por aplicação. Prerequisite: basic accounting.

MS&E 277. Creativity and Innovation. 3-4 Units.

O curso experiencial explora fatores que promovem e inibem a criatividade e a inovação em indivíduos, equipes e organizações. Ensina ferramentas de criatividade usando workshops, estudos de caso, viagens de campo, convidados de especialistas e desafios de design de equipe. Inscrição limitada a 40. Admissão por inscrição. Veja dschool. stanford. edu/classes.

MS&E 278. Patent Law and Strategy for Innovators and Entrepreneurs. 2-3 Units.

Este curso ensina o essencial para uma startup construir um portfólio valioso de patentes e evitar um processo por violação de patente. Jeffrey Schox, que é o melhor advogado de patentes recomendado para a Y Combinator, construiu o portfólio de patentes para Twilio (IPO), Cruise (aquisição de US $ 1 bilhão) e 250 startups que coletivamente arrecadaram mais de US $ 2 bilhões em capital de risco. Este curso é igualmente aplicável aos alunos de EE, CS e Bioengenharia. Para aqueles estudantes que estão interessados ​​em uma carreira em Direito de Patentes, por favor, note que este curso é um pré-requisito para o Processo de Patente ME238.

MS&E 280. Organizational Behavior: Evidence in Action. 3-4 Units.

Teoria da organização; conceitos e funções de gestão; comportamento do indivíduo, grupo de trabalho e organização. A ênfase está nos casos e discussão relacionada. Inscrição limitada.

MS&E 282. Transformational Leadership. 3 Units.

The personal, team-based and organizational skills needed to become a transformative leader. Case method discussions and lectures. Themes include: personal transformation; the inside-out effect, positive intelligence, group transformation; cross-functional teams; re-engineering; rapid - non-profit and for profit - organizational transformation; and social transformation. Limited enrollment; preference to graduate students. Prerequisite: 180 or 280, or relevant work experience.

MS&E 284. Designing Modern Work Organizations. 3 Units.

This practice-based experiential lab course is geared toward MS&E masters students. Students will master the concepts of organizational design, with an emphasis on applying them to modern challenges (technology, growth, globalization, and the modern workforce). Students will also gain mastery of skills necessary for success in today's workplace (working in teams, communicating verbally, presenting project work). Guest speakers from industry will present real-world challenges related to class concepts. Students will complete a quarter-long project designing and managing an actual online organization.

Primarily for master's students; also open to undergraduates and doctoral students. The application of mathematical, statistical, economic, and systems models to problems in health policy. Areas include: disease screening, prevention, and treatment; assessment of new technologies; bioterrorism response; and drug control policies.

MS&E 293. Technology and National Security. 3 Units.

Explores the relation between technology, war, and national security policy from early history to modern day, focusing on current U. S. national security challenges and the role that technology plays in shaping our understanding and response to these challenges. Topics include the interplay between technology and modes of warfare; dominant and emerging technologies such as nuclear weapons, cyber, sensors, stealth, and biological; security challenges to the U. S.; and the U. S. response and adaptation to new technologies of military significance.

MS&E 294. Systems Modeling for Climate Policy Analysis. 3 Units.

Design and application of formal analytical methods in climate policy development. Emphasis on integrated use of modeling tools from diverse methodologies and application of these modeling tools towards policy-making. Students will work with one of several widely-used climate policy models for the course project. Issues addressed include model selection, instrument design, technology development, resource management, multiparty negotiation, and dealing with complexity and uncertainty. Links among art, theory, and practice. Prerequisites: 211, 241, 252, or equivalents, or permission of instructor.

Design and application of formal analytical methods for policy and technology assessments of energy efficiency and renewable energy options. Emphasis is on integrated use of modeling tools from diverse methodologies and requirements for policy and corporate strategy development. Prerequisites: ECON 50, MS&E 211, MS&E 252, or equivalents, or permission of instructor.

MS&E 297. "Hacking for Defense": Solving National Security issues with the Lean Launchpad. 3-4 Units.

In a crisis, national security initiatives move at the speed of a startup yet in peacetime they default to decades-long acquisition and procurement cycles. Startups operate with continual speed and urgency 24/7. Over the last few years they¿ve learned how to be not only fast, but extremely efficient with resources and time using lean startup methodologies. In this class student teams will take actual national security problems and learn how to apply ¿lean startup¿ principles, ("business model canvas," "customer development," and "agile engineering¿) to discover and validate customer needs and to continually build iterative prototypes to test whether they understood the problem and solution. Teams take a hands-on approach requiring close engagement with actual military, Department of Defense and other government agency end-users. Team applications required in February. Inscrição limitada. Course builds on concepts introduced in MS&E 477.

MS&E 298. Hacking for Diplomacy: Tackling Foreign Policy Challenges with the Lean Launchpad. 3-4 Units.

Em um momento de significativa incerteza global, os diplomatas estão enfrentando desafios transnacionais e transversais que desafiam a solução fácil, incluindo: a busca contínua de armas de destruição em massa por grupos estatais e não-estatais, a eclosão de conflitos internos em todo o Oriente Médio e em partes da África, o fluxo mais significativo de refugiados desde a Segunda Guerra Mundial, e um clima em mudança que está começando a ter impactos nos países desenvolvidos e em desenvolvimento. Embora as ferramentas tradicionais de governança continuem relevantes, os formuladores de políticas buscam aproveitar o poder das novas tecnologias para repensar como o governo dos EUA se aproxima e responde a esses e outros desafios de longa data. In this class, student teams will take actual foreign policy challenges and learn how to apply lean startup principles, ("mission model canvas," "customer development," and "agile engineering¿) to discover and validate agency and user needs and to continually build iterative prototypes to test whether they understood the problem and solution. Teams take a hands-on approach requiring close engagement with officials in the U. S. State Department and other civilian agencies. Team applications required at the end of shopping period. Inscrição limitada.

MS&E 299. Voluntary Social Systems. 1-3 Unit.

Ethical theory, feasibility, and desirability of a social order in which coercion by individuals and government is minimized and people pursue ends on a voluntary basis. Topics: efficacy and ethics; use rights for property; contracts and torts; spontaneous order and free markets; crime and punishment based on restitution; guardian-ward theory for dealing with incompetents; the effects of state action-hypothesis of reverse results; applications to help the needy, armed intervention, victimless crimes, and environmental protection; transition strategies to a voluntary society.

MS&E 300. Ph. D. Qualifying Tutorial or Paper. 1-3 Unit.

Restricted to Ph. D. students assigned tutorials as part of the MS&E Ph. D. qualifying process. Enrollment optional.

Prerequisite: doctoral candidacy.

MS&E 302. Fundamental Concepts in Management Science and Engineering. 1 Unit.

Each course session will be devoted to a specific MS&E PhD research area. Advanced students will make presentations designed for first-year doctoral students regardless of area. The presentations will be devoted to: illuminating how people in the area being explored that day think about and approach problems, and illustrating what can and cannot be done when addressing problems by deploying the knowledge, perspectives, and skills acquired by those who specialize in the area in question. Area faculty will attend and participate. During the last two weeks of the quarter groups of first year students will make presentations on how they would approach a problem drawing on two or more of the perspectives to which they have been exposed earlier in the class. Attendance is mandatory and performance will be assessed on the basis of the quality of the students¿ presentations and class participation. Restricted to first year MS&E PhD students.

Formulation of standard linear programming models. Theory of polyhedral convex sets, linear inequalities, alternative theorems, and duality. Variants of the simplex method and the state of art interior-point algorithms. Sensitivity analyses, economic interpretations, and primal-dual methods. Relaxations of harder optimization problems and recent convex conic linear programs. Applications include game equilibrium facility location. Prerequisite: MATH 113 or consent of instructor.

Applications, theories, and algorithms for finite-dimensional linear and nonlinear optimization problems with continuous variables. Elements of convex analysis, first - and second-order optimality conditions, sensitivity and duality. Algorithms for unconstrained optimization, and linearly and nonlinearly constrained problems. Modern applications in communication, game theory, auction, and economics. Prerequisites: MATH 113, 115, or equivalent.

MS&E 312. Advanced Methods in Numerical Optimization. 3 Units.

Topics include interior-point methods, relaxation methods for nonlinear discrete optimization, sequential quadratic programming methods, optimal control and decomposition methods. Topic chosen in first class; different topics for individuals or groups possible. Individual or team projects. Pode ser repetido por crédito.

MS&E 313. Almost Linear Time Graph Algorithms. 3 Units.

Over the past decade there has been an explosion in activity in designing new provably efficient fast graph algorithms. Leveraging techniques from disparate areas of computer science and optimization researchers have made great strides on improving upon the best known running times for fundamental optimization problems on graphs, in many cases breaking long-standing barriers to efficient algorithm design. In this course we will survey these results and cover the key algorithmic tools they leverage to achieve these breakthroughs. Possible topics include but are not limited to, spectral graph theory, sparsification, oblivious routing, local partitioning, Laplacian system solving, and maximum flow. Prerequisites: calculus and linear algebra.

MS&E 314. Linear and Conic Optimization with Applications. 3 Units.

Linear, semidefinite, conic, and convex nonlinear optimization problems as generalizations of classical linear programming. Algorithms include the interior-point, barrier function, and cutting plane methods. Related convex analysis, including the separating hyperplane theorem, Farkas lemma, dual cones, optimality conditions, and conic inequalities. Complexity and/or computation efficiency analysis. Applications to combinatorial optimization, sensor network localization, support vector machine, and graph realization. Prerequisite: MS&E 211 or equivalent.

MS&E 316. Discrete Mathematics and Algorithms. 3 Units.

Topics: Basic Algebraic Graph Theory, Matroids and Minimum Spanning Trees, Submodularity and Maximum Flow, NP-Hardness, Approximation Algorithms, Randomized Algorithms, The Probabilistic Method, and Spectral Sparsification using Effective Resistances. Topics will be illustrated with applications from Distributed Computing, Machine Learning, and large-scale Optimization. Prerequisites: CS 261 is highly recommended, although not required.

MS&E 317. Algorithms for Modern Data Models. 3 Units.

We traditionally think of algorithms as running on data available in a single location, typically main memory. In many modern applications including web analytics, search and data mining, computational biology, finance, and scientific computing, the data is often too large to reside in a single location, is arriving incrementally over time, is noisy/uncertain, or all of the above. Paradigms such as map-reduce, streaming, sketching, Distributed Hash Tables, Bulk Synchronous Processing, and random walks have proved useful for these applications. This course will provide an introduction to the design and analysis of algorithms for these modern data models. Prerequisite: Algorithms at the level of CS 261.

MS&E 318. Large-Scale Numerical Optimization. 3 Units.

The main algorithms and software for constrained optimization emphasizing the sparse-matrix methods needed for their implementation. Iterative methods for linear equations and least squares. The simplex method. Basis factorization and updates. Interior methods. O método de gradiente reduzido, métodos Lagrangeanos aumentados e métodos de SQP. Prerequisites: Basic numerical linear algebra, including LU, QR, and SVD factorizations, and an interest in MATLAB, sparse-matrix methods, and gradient-based algorithms for constrained optimization. Recommended: MS&E 310, 311, 312, 314, or 315; CME 108, 200, 302, 304, 334, or 335.

MS&E 319. Approximation Algorithms. 3 Units.

Combinatorial and mathematical programming techniques to derive approximation algorithms for NP-hard optimization problems. Prossible topics include: greedy algorithms for vertex/set cover; rounding LP relaxations of integer programs; primal-dual algorithms; semidefinite relaxations. Pode ser repetido por crédito. Prerequisites: 112 or CS 161.

Topics in stochastic processes, emphasizing applications. Markov chains in discrete and continuous time; Markov processes in general state space; Lyapunov functions; regenerative process theory; renewal theory; martingales, Brownian motion, and diffusion processes. Application to queueing theory, storage theory, reliability, and finance. Prerequisites: 221 or STATS 217; MATH 113, 115. (Glynn).

MS&E 322. Stochastic Calculus and Control. 3 Units.

Ito integral, existence and uniqueness of solutions of stochastic differential equations (SDEs), diffusion approximations, numerical solutions of SDEs, controlled diffusions and the Hamilton-Jacobi-Bellman equation, and statistical inference of SDEs. Applications to finance and queueing theory. Prerequisites: 221 or STATS 217: MATH 113, 115.

MS&E 324. Stochastic Methods in Engineering. 3 Units.

The basic limit theorems of probability theory and their application to maximum likelihood estimation. Basic Monte Carlo methods and importance sampling. Markov chains and processes, random walks, basic ergodic theory and its application to parameter estimation. Discrete time stochastic control and Bayesian filtering. Diffusion approximations, Brownian motion and an introduction to stochastic differential equations. Examples and problems from various applied areas. Prerequisites: exposure to probability and background in analysis.

MS&E 325. Advanced Topics in Applied Probability. 3 Units.

Current stochastic models, motivated by a wide range of applications in engineering, business, and science, as well as the design and analysis of associated computational methods for performance analysis and control of such stochastic systems.

MS&E 326. Advanced Topics in Game Theory with Engineering Applications. 3 Units.

Advanced Topics in Game Theory with Engineering Applications.

Data and algorithms are transforming law enforcement and criminal justice, a shift that is ripe for rigorous empirical and narrative exploration. This class is centered around several data-driven projects in criminal justice, with the goal of fostering greater understanding, transparency, and public accountability. Students work in interdisciplinary teams, using a combination of statistical and journalistic methods. Some of the work may be published by news organizations or may be used to advance data journalism investigations. Students with a background in statistics, computer science, law, public policy or journalism are encouraged to participate. Enrollment is limited, and project teams will be selected during the first week of class.

MS&E 332. Topics in Social Algorithms. 3 Units.

In depth discussion of selected research topics in social algorithms, including networked markets, collective decision making, recommendation and reputation systems, prediction markets, social computing, and social choice theory. The class will include a theoretical project and a paper presentation. Prerequisites: CS 261 or equivalent; understanding of basic game theory.

This seminar will introduce students to research in the field of social algorithms, including networked markets, collective decision making, recommendation and reputation systems, prediction markets, social choice theory, and models of influence and contagion.

This course provides a in-depth survey of methods research for the analysis of large-scale social and behavioral data. There will be a particular focus on recent developments in discrete choice theory and preference learning. Connections will be made to graph-theoretic investigations common in the study of social networks. Topics will include random utility models, item-response theory, ranking and learning to rank, centrality and ranking on graphs, and random graphs. The course is intended for Ph. D. students, but masters students with an interested in research topics are welcome. Recommended: 221, 226, CS161, or equivalents.

MS&E 335. Queueing and Scheduling in Processing Networks. 3 Units.

Advanced stochastic modeling and control of systems involving queueing and scheduling operations. Stability analysis of queueing systems. Key results on single queues and queueing networks. Controlled queueing systems. Dynamic routing and scheduling in processing networks. Applications to modeling, analysis and performance engineering of computing systems, communication networks, flexible manufacturing, and service systems. Prerequisite: 221 or equivalent.

MS&E 336. Platform and Marketplace Design. 3 Units.

The last decade has witnessed a meteoric rise in the number of online markets and platforms competing with traditional mechanisms of trade. Examples of such markets include online marketplaces for goods, such as eBay; online dating markets; markets for shared resources, such as Lyft, Uber, and Airbnb; and online labor markets. We will review recent research that aims to both understand and design such markets. Emphasis on mathematical modeling and methodology, with a view towards preparing Ph. D. students for research in this area. Prerequisites: Mathematical maturity; 300-level background in optimization and probability; prior exposure to game theory.

Advanced material in this area is sometimes taught for the first time as a topics course. Prerequisite: consent of instructor.

MS&E 347. Credit Risk: Modeling and Management. 3 Units.

Credit risk modeling, valuation, and hedging emphasizing underlying economic, probabilistic, and statistical concepts. Point processes and their compensators. Structural, incomplete information and reduced form approaches. Single name products: corporate bonds, equity, equity options, credit and equity default swaps, forwards and swaptions. Multiname modeling: index and tranche swaps and options, collateralized debt obligations. Implementation, calibration and testing of models. Industry and market practice. Data and implementation driven group projects that focus on problems in the financial industry.

MS&E 348. Optimization of Uncertainty and Applications in Finance. 3 Units.

How to make optimal decisions in the presence of uncertainty, solution techniques for large-scale systems resulting from decision problems under uncertainty, and applications in finance. Decision trees, utility, two-stage and multi-stage decision problems, approaches to stochastic programming, model formulation; large-scale systems, Benders and Dantzig-Wolfe decomposition, Monte Carlo sampling and variance reduction techniques, risk management, portfolio optimization, asset-liability management, mortgage finance. Projects involving the practical application of optimization under uncertainty to financial planning.

Topics in financial statistics with focus on current research: Time-series modeling, volatility modeling, high-frequency statistics, large-dimensional factor modeling and estimation of continuous-time processes. Prerequisites: 220, 226 or STATS 200, 221 or STATS 217, 245A, or equivalents.

MS&E 351. Dynamic Programming and Stochastic Control. 3 Units.

Markov population decision chains in discrete and continuous time. Risk posture. Present value and Cesaro overtaking optimality. Optimal stopping. Successive approximation, policy improvement, and linear programming methods. Team decisions and stochastic programs; quadratic costs and certainty equivalents. Maximum principle. Controlled diffusions. Examples from inventory, overbooking, options, investment, queues, reliability, quality, capacity, transportation. MATLAB. Prerequisites: MATH 113, 115; Markov chains; linear programming.

MS&E 352. Decision Analysis II: Professional Decision Analysis. 3-4 Units.

How to organize the decision conversation, the role of the decision analysis cycle and the model sequence, assessing the quality of decisions, framing decisions, the decision hierarchy, strategy tables for alternative development, creating spare and effective decision diagrams, biases in assessment, knowledge maps, uncertainty about probability. Sensitivity analysis, approximations, value of revelation, joint information, options, flexibility, bidding, assessing and using corporate risk attitude, risk sharing and scaling, and decisions involving health and safety. See 353 for continuation. Prerequisite: 252.

MS&E 353. Decision Analysis III: Frontiers of Decision Analysis. 3 Units.

The concept of decision composite; probabilistic insurance and other challenges to the normative approach; the relationship of decision analysis to classical inference and data analysis procedures; the likelihood and exchangeability principles; inference, decision, and experimentation using conjugate distributions; developing a risk attitude based on general properties; alternative decision aiding practices such as analytic hierarchy and fuzzy approaches. Student presentations on current research. Goal is to prepare doctoral students for research. Prerequisite: 352.

MS&E 355. Influence Diagrams and Probabilistics Networks. 3 Units.

Network representations for reasoning under uncertainty: influence diagrams, belief networks, and Markov networks. Structuring and assessment of decision problems under uncertainty. Learning from evidence. Conditional independence and requisite information. Node reductions. Belief propagation and revision. Simulação. Linear-quadratic-Gaussian decision models and Kalman filters. Dynamic processes. Bayesian meta-analysis. Prerequisites: 220, 252, or equivalents, or consent of instructor.

MS&E 365. Advanced Topics in Market Design. 3 Units.

Primarily for doctoral students. Focus on quantitative models dealing with sustainability and related to operations management. Prerequisite: consent of instructor. Pode ser repetido por crédito.

MS&E 371. Innovation and Strategic Change. 2-3 Units.

Seminário de pesquisa de doutorado, limitado ao Ph. D. estudantes. Pesquisa atual sobre estratégia de inovação. Tópicos: descoberta científica, busca de inovação, aprendizado organizacional, abordagens evolutivas e mudanças incrementais e radicais. Os tópicos mudam anualmente. Recomendado: curso em estatística ou métodos de pesquisa.

MS&E 372. Entrepreneurship Doctoral Research Seminar. 1-3 Unit.

Pesquisa clássica e atual sobre empreendedorismo. Inscrição limitada, restrita a estudantes de doutorado. Pré-requisitos: SOC 363 ou equivalente e permissão do instrutor.

MS&E 374. Cross Border Regional Innovation. 3 Units.

This is an advanced research seminar class that is restricted to students that had taken MS&E 274. Disruptive innovation is the realization of new value proposition through establishment of a new ecosystem. Value proposition depends on the culture and social value in a particular region; while the ability to establish the ecosystem to realize the value proposition is highly dependent on the firm¿s knowledge and skills to operate effectively under the political, social, and economic structure of that particular region. Therefore cross border and regional innovations in different regions will take different path. This course will examine cases that cover innovations in developing economy, cross border e-commerce, and international business groups.

MS&E 376. Strategy Doctoral Research Seminar. 3 Units.

Pesquisa clássica e atual sobre negócios e estratégia corporativa. Inscrição limitada, restrita a estudantes de doutorado. Pré-requisitos: SOC 363 ou equivalente e permissão do instrutor. O curso pode ser repetido para crédito.

Introdução aplicada à boa pesquisa empírica e inferência causal para cientistas sociais e outros que analisam dados sociais. Projetado para fornecer uma introdução a algumas das técnicas quantitativas mais comumente usadas para inferência causal em dados sociais, incluindo: delineamento de pesquisa e inferência, regressão e propensão de correspondência de escores, variáveis ​​instrumentais, diferenças-em-diferenças, desenhos de descontinuidade de regressão, erros-padrão e a análise de big data. Aplicações: organizações, empreendedorismo, políticas públicas, inovação, economia, educação on-line, representações visuais, comunicação, crítica e design de figuras, gráficos. Não abrange explicitamente a estrutura de redes sociais ou o aprendizado de máquinas, pois esses tópicos são bem abordados em outros lugares. Os alunos trabalham em grupo e individualmente para projetar e realizar um pequeno projeto de pesquisa com base no uso de análises, grandes conjuntos de dados ou outras inovações digitais relacionadas a empresas ou outras organizações. Os alunos se familiarizam com uma variedade de abordagens ao design de pesquisa e são ajudados a desenvolver seus próprios projetos de pesquisa. O curso prioriza uma compreensão profunda e fundamentada das suposições sobre provas e derivações matemáticas. Destinado a estudantes de doutorado, mas aberto com permissão aos alunos do mestrado e aos alunos de outros programas de Stanford com cursos relevantes ou experiência em análise e estatística.

MS&E 380. Doctoral Research Seminar in Organizations. 3 Units.

Limited to Ph. D. estudantes. Topics from current published literature and working papers. Content varies. Prerequisite: consent of instructor.

MS&E 381. Doctoral Research Seminar in Work, Technology, and Organization. 2-3 Units.

Enrollment limited to Ph. D. estudantes. Topics from current published literature and working papers. Content varies. Prerequisite: consent of instructor.

MS&E 383. Doctoral Seminar on Ethnographic Research. 3 Units.

For graduate students; upper-level undergraduates with consent of instructor. Interviewing and participant observation. Techniques for taking, managing, and analyzing field notes and other qualitative data. Methods texts and ethnographies offer examples of how to analyze and communicate ethnographic data. Prerequisite: consent of instructor.

Research on groups and teams in organizations from the perspective of organizational behavior and social psychology. Topics include group effectiveness, norms, group composition, diversity, conflict, group dynamics, temporal issues in groups, geographically distributed teams, and intergroup relations.

MS&E 387. Design of Field Research Methods. 3 Units.

Field research involves collecting original data (qualitative and/or quantitative) in field sites. This course combines informal lecture and discussion with practical exercises to build specific skills for conducting field research in organizations. Readings include books and papers about research methodology and articles that provide exemplars of field research. Specific topics covered include: the role of theory in field research, variance versus process models, collecting and analyzing different kinds of data (observation, interview, survey), levels of analysis, construct development and validity, blending qualitative and quantitative data (in a paper, a study, or a career), and writing up field research for publication. Students will develop intuition about the contingent relationship between the nature of the research question and the field research methods used to answer it as a foundation for conducting original field research.

MS&E 388. Contemporary Themes in Work and Organization Studies. 3 Units.

Seminário de pesquisa de doutorado, limitado ao Ph. D. estudantes. Current meso-level field research on organizational behavior, especially work and coordination. Topics: work design, job design, roles, teams, organizational change and learning, knowledge management, performance. Focus on understanding theory development and research design in contemporary field research. Os tópicos mudam anualmente. Recomendado: curso em estatística ou métodos de pesquisa.

MS&E 389. Seminar on Organizational Theory. 5 Units.

The social science literature on organizations assessed through consideration of the major theoretical traditions and lines of research predominant in the field.

MS&E 390. Doctoral Research Seminar in Health Systems Modeling. 1-3 Unit.

Restricted to PhD students, or by consent of instructor. Doctoral research seminar covering current topics in health policy, health systems modeling, and health innovation. Pode ser repetido por crédito.

MS&E 391. Doctoral Research Seminar in Energy-Environmental Systems Modeling and Analysis. 1-3 Unit.

Restricted to PhD students, or by consent of instructor. Doctoral research seminar covering current topics in energy and environmental modeling and analysis. Current emphasis on approaches to incorporation of uncertainty and technology dynamics into complex systems models. Pode ser repetido por crédito.

Modeling approaches for examining real life problems: how to get started. Critical thinking in framing and problem formulation leading to actionable solutions and communication of results to decision makers. Models to identify and evaluate multiple objectives/metrics. Models examined include both deterministic and probabilistic components. Overview of optimization and probability, decomposition principles to model large scale problems, appropriate integration of uncertainties into model formulations. Primarily team-project based assignments, with three to four group projects. Project topics drawn from applications with real data. Sample project topics include: optimizing group phone plans for large corporations, life insurance business models, making sense of the health care debate, logistic decision problems. Project teams will critically grade other teams¿ project reports using provided guidelines. Project presentations throughout the quarter. Prerequisites: 211, 220.

MS&E 408. Directed Reading and Research. 1-15 Unit.

Directed reading and research on a subject of mutual interest to student and faculty member. Prerequisite: consent of instructor.

MS&E 441. Policy and Economics Research Roundtable. 1 Unit.

Research in progress or contemplated in policy and economics areas. Emphasis depends on research interests of participants, but is likely to include energy, environment, transportation, or technology policy and analysis. Pode ser repetido por crédito.

MS&E 442. Energy Efficiency: Technology, Policy, and Investment. 1-2 Unit.

Provides students with a basic understanding of the technologies, policies, and investments behind energy efficiency. Explores each of these dimensions, and their interplay, through structured lectures and expert perspectives from leading professionals and practitioners. The seminar covers the following energy efficiency topics: fundamental concepts; history and achievements; role of policy and new policy frameworks; investment, strategy and finance; evolving digital/analytical and platform tools; low income programs; desenvolvimento Internacional; relationship between efficiency and climate change; energy efficiency and the changing grid; and new entrants and business models. Limited to 30 students. Prerequisite: at least one ¿ or equivalent ¿ of CEE 100, CEE 226A, CEE 107A/207A, ENVRES 280, LAW 2503, GSBGEN 532.

MS&E 446A. Mathematical and Computational Finance Seminar. 1 Unit.

May be repeat for credit.

MS&E 447. Systemic and Market Risk : Notes on Recent History, Practice, and Policy. 3 Units.

The global financial crisis of 2007-8 threw into sharp relief the ongoing challenges of understanding risk, the financial system, links with the global economy, and interactions with policy. We will explore elements of the crisis, a few other key events, and ongoing debates about systemic risk. Group projects will explore in more detail past events and current topics in systemic risk. Supplements a rigorous technical curriculum in modern finance with select aspects relevant to understanding the practice and broader context of modern financial activities such as derivatives, financial engineering, and risk management.

MS&E 448. Big Financial Data and Algorithmic Trading. 3 Units.

Project course emphasizing the connection between data, models, and reality. Vast amounts of high volume, high frequency observations of financial quotes, orders and transactions are now available, and poses a unique set of challenges. This type of data will be used as the empirical basis for modeling and testing various ideas within the umbrella of algorithmic trading and quantitative modeling related to the dynamics and micro-structure of financial markets. Due to the fact that it is near impossible to perform experiments in finance, there is a need for empirical inference and intuition, any model should also be justified in terms of plausibility that goes beyond pure econometric and data mining approaches. Introductory lectures, followed by real-world type projects to get a hands-on experience with realistic challenges and hone skills needed in the work place. Work in groups on selected projects that will entail obtaining and cleaning the raw data and becoming familiar with techniques and challenges in handling big data sets. Develop a framework for modeling and testing (in computer languages such as Python, C++ , Matlab and R) and prepare presentations to present to the class. Example projects include optimal order execution, developing a market making algorithm, design of an intra-day trading strategy, and modeling the dynamics of the bid and ask. Prerequisites: MS&E 211, 242, 342, or equivalents, some exposure to statistics and programming. Inscrição limitada. Admission by application; details at first class.

In-class lectures and guest speakers who work in the Buy-Side to explore the synergies amongst the various players¿ roles, risk appetites, and investment time and return horizons. We aim to see the forest and the different species of trees growing in the forest known as the Buy-Side, so as to develop a perspective as financial engineers for how the ecosystem functions, what risks it digests, how it generates capital at what rate and amount for the Sell-Side, and how impacts in the real economy are reflected - or should be reflected - in the culture and risk models adopted by the Buy-Side participants.

MS&E 450. Lessons in Decision Making. 1 Unit.

Entrepreneurs, senior management consultants, and executives from Fortune 500 companies share real-world stories and insights from their experience in decision making.

MS&E 454. Decision Analysis Seminar. 1 Unit.

Current research and related topics presented by doctoral students and invited speakers. Pode ser repetido por crédito. Prerequisite: 252.

MS&E 463. Healthcare Systems Design. 3-4 Units.

Students work on projects to analyze and design various aspects of healthcare including hospital patient flow, physician networks, clinical outcomes, reimbursement incentives, and community health. Students work in small teams under the supervision of the course instructor and partners at the Lucille Packard Children's Hospital, the Stanford Hospital, and other regional healthcare providers. Prerequisite: 263.

MS&E 472. Entrepreneurial Thought Leaders' Seminar. 1 Unit.

Líderes empresariais compartilham lições de experiências do mundo real em ambientes empresariais. Os palestrantes da ETL incluem empreendedores, líderes de empresas globais de tecnologia, capitalistas de risco e autores de best-sellers. Conversas de meia hora seguidas de meia hora de interação de classe. Discussão na web obrigatória. Pode ser repetido por crédito.

MS&E 475A. Entrepreneurial Leadership. 1 Unit.

Este seminário explora uma ampla gama de tópicos relacionados à liderança empreendedora por meio de discussões em sala de aula, estudos de caso, viagens de campo e palestrantes convidados. Faz parte da Fellowship de Líderes Empresariais da DFJ, que requer uma aplicação durante o trimestre de outono. Details can be found at: stvp. stanford. edu/dfj/.

MS&E 475B. Entrepreneurial Leadership. 1 Unit.

Este seminário explora uma ampla gama de tópicos relacionados à liderança empreendedora por meio de discussões em sala de aula, estudos de caso, viagens de campo e palestrantes convidados. Faz parte da Fellowship de Líderes Empresariais da DFJ, que requer uma aplicação durante o trimestre de outono. Details can be found at: stvp. stanford. edu/dfj/.

MS&E 487. D: PROTOTYPING ORGANIZATIONAL CHANGE. 2-4 Units.

d will send outstanding, proven design thinkers into select organizations to architect "organizational R&D" experiments. Students will work directly with teams in those organizations to implement tools to drive change and measure results. Performance will be measured by the student's effectiveness at driving changes in the organization. Prerequisite: MS&E 489 and permission of instructor.

MS&E 489. d. Leadership: Design Leadership in Context. 4 Units.

d. Liderança é um curso que ensina as habilidades de coaching e liderança necessárias para conduzir um bom processo de design em grupos. Os líderes trabalharão em projetos reais que conduzem projetos de design dentro das organizações e ganham habilidades do mundo real à medida que experimentam seu estilo de liderança. Faça este curso se você se inspirar em aulas de design anteriores e quiser habilidades para liderar projetos de design além de Stanford. Preferência dada aos alunos que tenham participado de outras aulas do Grupo de Design ou da escola. Admissão por aplicação. See dschool. stanford. edu/classes for more information.

MS&E 493. Current issues in Technology and National Security. 1-3 Unit.

Probe deeply into both key regional security concerns and advanced technologies that shape today¿s headlines. Shaped by current events, regional focus areas include the Korean peninsula, the South and East China Seas, the Ukraine, and Iran. Technologies explored include missile defense systems, national security space protection and defense, hypersonic vehicles, nuclear stockpile and deterrence concepts, drone and autonomous platforms, and the realities of cyber warfare. Expert speakers followed by an in-depth discussion of both the technologies and their policy implications. Open to undergraduate and graduate students.

Interdisciplinary exploration of current energy challenges and opportunities, with talks by faculty, visitors, and students. Pode ser repetido por crédito.

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How to upgrade Anaconda to Python 3.3, and use in Eclipse (Windows)

Open up a command prompt, and run:

You will need to enter Yes to go ahead.

In Eclipse go to:

Window \ Preferences \ Pydev \ Interpreter – Python.

Add a new Python Interpreter for Python 33 in Anaconda (e. g. my new environment was at c:\anaconda\envs\p33\python. exe)

To use with a project:

Right click on a project and go to Properties.

Under PyDev – Interpreter/Grammar, select your new interpreter.