Sobre este Programa de cursos integrados
cursos 100% online

cursos 100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Cronograma flexível

Cronograma flexível

Definição e manutenção de prazos flexíveis.
Nível intermediário

Nível intermediário

Horas para completar

Aprox. 6 meses para completar

7 horas/semana sugeridas
Idiomas disponíveis

Inglês

Legendas: Inglês, Coreano...

Habilidades que você terá

Software-Defined NetworkingDistributed ComputingBig DataCloud Computing
cursos 100% online

cursos 100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Cronograma flexível

Cronograma flexível

Definição e manutenção de prazos flexíveis.
Nível intermediário

Nível intermediário

Horas para completar

Aprox. 6 meses para completar

7 horas/semana sugeridas
Idiomas disponíveis

Inglês

Legendas: Inglês, Coreano...

Como o Programa de cursos integrados funciona

Fazer cursos

Um programa de cursos integrados do Coursera é uma série de cursos para ajudá-lo a dominar uma habilidade. Primeiramente, inscreva-se no programa de cursos integrados diretamente, ou avalie a lista de cursos e escolha por qual você gostaria de começar. Ao se inscrever em um curso que faz parte de um programa de cursos integrados, você é automaticamente inscrito em todo o programa de cursos integrados. É possível concluir apenas um curso — você pode pausar a sua aprendizagem ou cancelar a sua assinatura a qualquer momento. Visite o seu painel de aprendiz para controlar suas inscrições em cursos e progresso.

Projeto prático

Todos os programas de cursos integrados incluem um projeto prático. Você precisará completar com êxito o(s) projeto(s) para concluir o programa de cursos integrados e obter o seu certificado. Se o programa de cursos integrados incluir um curso separado para o projeto prático, você precisará completar todos os outros cursos antes de iniciá-lo.

Obtenha um certificado

Ao concluir todos os cursos e completar o projeto prático, você obterá um certificado que pode ser compartilhado com potenciais empregadores e com sua rede profissional.

how it works

Este Programa de cursos integrados contém 6 cursos

Curso1

Cloud Computing Concepts, Part 1

4.5
607 classificações
156 avaliações
Cloud computing systems today, whether open-source or used inside companies, are built using a common set of core techniques, algorithms, and design philosophies – all centered around distributed systems. Learn about such fundamental distributed computing "concepts" for cloud computing. Some of these concepts include: clouds, MapReduce, key-value/NoSQL stores, classical distributed algorithms, widely-used distributed algorithms, scalability, trending areas, and much, much more! Know how these systems work from the inside out. Get your hands dirty using these concepts with provided homework exercises. In the programming assignments, implement some of these concepts in template code (programs) provided in the C++ programming language. Prior experience with C++ is required. The course also features interviews with leading researchers and managers, from both industry and academia....
Curso2

Cloud Computing Concepts: Part 2

4.6
190 classificações
40 avaliações
Cloud computing systems today, whether open-source or used inside companies, are built using a common set of core techniques, algorithms, and design philosophies – all centered around distributed systems. Learn about such fundamental distributed computing "concepts" for cloud computing. Some of these concepts include: clouds, MapReduce, key-value/NoSQL stores, classical distributed algorithms, widely-used distributed algorithms, scalability, trending areas, and much, much more! Know how these systems work from the inside out. Get your hands dirty using these concepts with provided homework exercises. In the programming assignments, implement some of these concepts in template code (programs) provided in the C++ programming language. Prior experience with C++ is required. The course also features interviews with leading researchers and managers, from both industry and academia. This course builds on the material covered in the Cloud Computing Concepts, Part 1 course....
Curso3

Cloud Computing Applications, Part 1: Cloud Systems and Infrastructure

4.1
316 classificações
84 avaliações
Welcome to the Cloud Computing Applications course, the first part of a two-course series designed to give you a comprehensive view on the world of Cloud Computing and Big Data! In this first course we cover a multitude of technologies that comprise the modern concept of cloud computing. Cloud computing is an information technology revolution that has just started to impact many enterprise computing systems in major ways, and it will change the face of computing in the years to come. We start the first week by introducing some major concepts in cloud computing, the economics foundations of it and we introduce the concept of big data. We also cover the concept of software defined architectures, and how virtualization results in cloud infrastructure and how cloud service providers organize their offerings. In week two, we cover virtualization and containers with deeper focus, including lectures on Docker, JVM and Kubernates. We finish up week two by comparing the infrastructure as a service offering by the big three: Amazon, Google and Microsoft. Week three moves to higher level of cloud offering, including platform as a service, mobile backend as a service and even serverless architectures. We also talk about some of the cloud middleware technologies that are fundamental to cloud based applications such as RPC and REST, JSON and load balancing. Week three also covers metal as a service (MaaS), where physical machines are provisioned in a cloud environment. Week four introduces higher level cloud services with special focus on cloud storage services. We introduce Hive, HDFS and Ceph as pure Big Data Storage and file systems, and move on to cloud object storage systems, virtual hard drives and virtual archival storage options. As discussion on Dropbox cloud solution wraps up week 4 and the course....
Curso4

Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud

4.1
157 classificações
29 avaliações
Welcome to the Cloud Computing Applications course, the second part of a two-course series designed to give you a comprehensive view on the world of Cloud Computing and Big Data! In this second course we continue Cloud Computing Applications by exploring how the Cloud opens up data analytics of huge volumes of data that are static or streamed at high velocity and represent an enormous variety of information. Cloud applications and data analytics represent a disruptive change in the ways that society is informed by, and uses information. We start the first week by introducing some major systems for data analysis including Spark and the major frameworks and distributions of analytics applications including Hortonworks, Cloudera, and MapR. By the middle of week one we introduce the HDFS distributed and robust file system that is used in many applications like Hadoop and finish week one by exploring the powerful MapReduce programming model and how distributed operating systems like YARN and Mesos support a flexible and scalable environment for Big Data analytics. In week two, our course introduces large scale data storage and the difficulties and problems of consensus in enormous stores that use quantities of processors, memories and disks. We discuss eventual consistency, ACID, and BASE and the consensus algorithms used in data centers including Paxos and Zookeeper. Our course presents Distributed Key-Value Stores and in memory databases like Redis used in data centers for performance. Next we present NOSQL Databases. We visit HBase, the scalable, low latency database that supports database operations in applications that use Hadoop. Then again we show how Spark SQL can program SQL queries on huge data. We finish up week two with a presentation on Distributed Publish/Subscribe systems using Kafka, a distributed log messaging system that is finding wide use in connecting Big Data and streaming applications together to form complex systems. Week three moves to fast data real-time streaming and introduces Storm technology that is used widely in industries such as Yahoo. We continue with Spark Streaming, Lambda and Kappa architectures, and a presentation of the Streaming Ecosystem. Week four focuses on Graph Processing, Machine Learning, and Deep Learning. We introduce the ideas of graph processing and present Pregel, Giraph, and Spark GraphX. Then we move to machine learning with examples from Mahout and Spark. Kmeans, Naive Bayes, and fpm are given as examples. Spark ML and Mllib continue the theme of programmability and application construction. The last topic we cover in week four introduces Deep Learning technologies including Theano, Tensor Flow, CNTK, MXnet, and Caffe on Spark....

Instrutores

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Reza Farivar

Data Engineering Manager at Capital One, Adjunct Research Assistant Professor of Computer Science
Department of Computer Science
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Ankit Singla

Assistant Professor
Department of Computer Science, ETH Zürich
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Indranil Gupta

Professor
Department of Computer Science
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P. Brighten Godfrey

Associate Professor
Department of Computer Science
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Roy H. Campbell

Professor of Computer Science
Department of Computer Science
Graduation Cap

Comece a trabalhar rumo ao seu mestrado

Este Programa de cursos integrados é parte de uma graduação 100% on-line Master in Computer Science pela University of Illinois at Urbana-Champaign. Caso seja aceito para o programa completo, seus cursos contarão para sua graduação.

Sobre University of Illinois at Urbana-Champaign

The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs. ...

Perguntas Frequentes – FAQ

  • Sim! Para começar, clique na carta de curso que lhe interessa e se inscreva. Você pode se inscrever e concluir o curso para ganhar um certificado compartilhável ou você pode auditar para ver os materiais do curso de graça. Quando você se inscrever em um curso que faz parte de uma especialização, você está automaticamente inscrito para a especialização completa. Visite o seu painel de aluno para acompanhar o seu progresso.

  • Este curso é totalmente on-line, então não existe necessidade de aparecer em uma sala de aula pessoalmente. Você pode acessar suas palestras, leituras e atribuições a qualquer hora e qualquer lugar, via web ou dispositivo móvel.

  • Esta Especialização não carrega créditos universitários, mas algumas universidades podem optar por aceitar certificados de especialização como crédito. Verifique com sua instituição para saber mais.

  • Time to completion can vary widely based on your schedule. Most learners are able to complete the Specialization in 4-5 months.

  • Each course in the Specialization is offered on a regular schedule with sessions starting about once per month. If you don't complete a course on the first try, you can easily transfer to the next session, and your completed work and grades will carry over.

  • Basic working knowledge of computers and computer systems

    Familiarity with common programming languages (e.g., C, C++, Java)

  • It is recommended that the courses in the Specialization be taken in the order outlined. In the Capstone Project, you will have the opportunity to synthesize your learning in all the courses and apply your combined skills in a final project.

  • There will be hands-on laboratory experiments (Load Balancing and Web Services, MapReduce, Hive, Storm, and Mahout). Case studies will be drawn from Yahoo, Google, Twitter, Facebook, data mining, analytics, and machine learning. We will also explore current practice by talking to leading industry experts, as well as looking into interesting new research that might shape the cloud network’s future.

Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.