课程信息
4.1
159 个评分
29 个审阅
专项课程

第 4 门课程(共 6 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
完成时间(小时)

完成时间大约为15 小时

建议:There is about 3-4 hours of video lectures per week. Each week's quiz takes about 30 minutes. ...
可选语言

英语(English)

字幕:英语(English), 韩语

您将获得的技能

GraphsDistributed ComputingBig DataMachine Learning
专项课程

第 4 门课程(共 6 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
完成时间(小时)

完成时间大约为15 小时

建议:There is about 3-4 hours of video lectures per week. Each week's quiz takes about 30 minutes. ...
可选语言

英语(English)

字幕:英语(English), 韩语

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 3 小时

Course Orientation

You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course....
Reading
1 个视频 (总计 26 分钟), 4 个阅读材料, 1 个测验
Reading4 个阅读材料
Syllabus10分钟
About the Discussion Forums10分钟
Updating Your Profile10分钟
Social Media10分钟
Quiz1 个练习
Orientation Quiz10分钟
完成时间(小时)
完成时间为 2 小时

Module 1: Spark, Hortonworks, HDFS, CAP

In Module 1, we introduce you to the world of Big Data applications. We start by introducing you to Apache Spark, a common framework used for many different tasks throughout the course. We then introduce some Big Data distro packages, the HDFS file system, and finally the idea of batch-based Big Data processing using the MapReduce programming paradigm. ...
Reading
13 个视频 (总计 108 分钟), 1 个阅读材料, 1 个测验
Video13 个视频
1.1.2 Apache Spark11分钟
1.1.3 Spark Example: Log Mining9分钟
1.1.4 Spark Example: Logistic Regression7分钟
1.1.5 RDD Fault Tolerance4分钟
1.1.6 Interactive Spark4分钟
1.1.7 Spark Implementation4分钟
1.2.1 Introduction to Distros3分钟
1.2.2 Hortonworks23分钟
1.2.3 Cloudera CDH2分钟
1.2.4 MapR Distro2分钟
1.3.1 HDFS Introduction15分钟
1.3.2 YARN and MESOS9分钟
Reading1 个阅读材料
Module 1 Overview10分钟
Quiz1 个练习
Module 1 Quiz30分钟
2
完成时间(小时)
完成时间为 6 小时

Module 2: Large Scale Data Storage

In this module, you will learn about large scale data storage technologies and frameworks. We start by exploring the challenges of storing large data in distributed systems. We then discuss in-memory key/value storage systems, NoSQL distributed databases, and distributed publish/subscribe queues. ...
Reading
24 个视频 (总计 303 分钟), 1 个阅读材料, 1 个测验
Video24 个视频
2.1.1 Introduction to MapReduce with Spark3分钟
2.1.2 MapReduce: Motivation15分钟
2.1.3 MapReduce Programming Model with Spark9分钟
2.1.4 MapReduce Example: Word Count9分钟
2.1.5 MapReduce Example: Pi Estimation & Image Smoothing15分钟
2.1.6 MapReduce Example: Page Rank13分钟
2.1.7 MapReduce Summary4分钟
2.2.1 Eventual Consistency – Part 110分钟
2.2.2 Eventual Consistency – Part 220分钟
2.2.3 Consistency Trade-Offs4分钟
2.2.4 ACID and BASE19分钟
2.2.5 Zookeeper and Paxos: Introduction10分钟
2.2.6 Paxos17分钟
2.2.7 Zookeeper16分钟
2.3.1 Cassandra Introduction27分钟
2.3.2 Redis7分钟
2.3.3 Redis Demonstration14分钟
2.4.1 HBase Usage API15分钟
2.4.2 HBase Internals - Part 117分钟
2.4.3 HBase Internals - Part 29分钟
2.4.4 Spark SQL8分钟
2.5.5 Spark SQL Demo8分钟
2.5.1 Kafka17分钟
Reading1 个阅读材料
Module 2 Overview10分钟
Quiz1 个练习
Module 2 Quiz30分钟
3
完成时间(小时)
完成时间为 4 小时

Module 3: Streaming Systems

This module introduces you to real-time streaming systems, also known as Fast Data. We talk about Apache Storm in length, Apache Spark Streaming, and Lambda and Kappa architectures. Finally, we contrast all these technologies as a streaming ecosystem. ...
Reading
18 个视频 (总计 216 分钟), 1 个阅读材料, 1 个测验
Video18 个视频
3.1.1 Streaming Introduction9分钟
3.1.2 "Big Data Pipelines: The Rise of Real-Time"7分钟
3.1.3 Storm Introduction: Protocol Buffers & Thrift15分钟
3.1.4 A Storm Word Count Example3分钟
3.1.5 Writing the Storm Word Count Example10分钟
3.1.6 Storm Usage at Yahoo3分钟
3.2.1 Anchoring and Spout Replay17分钟
3.2.2 Trident: Exactly Once Processing10分钟
3.3.1 Inside Apache Storm9分钟
3.3.2 The Structure of a Storm Cluster4分钟
3.3.3 Using Thrift in Storm10分钟
3.3.4 How Storm Schedulers Work12分钟
3.3.5 Scaling Storm to 4000 Nodes14分钟
3.3.6 Q&A with Bobby Evans (Yahoo) on Storm32分钟
3.4.1 Spark Streaming18分钟
3.4.2 Lambda and Kappa Architecture4分钟
3.4.3 Streaming Ecosystem24分钟
Reading1 个阅读材料
Module 3 Overview10分钟
Quiz1 个练习
Module 3 Quiz30分钟
4
完成时间(小时)
完成时间为 4 小时

Module 4: Graph Processing and Machine Learning

In this module, we discuss the applications of Big Data. In particular, we focus on two topics: graph processing, where massive graphs (such as the web graph) are processed for information, and machine learning, where massive amounts of data are used to train models such as clustering algorithms and frequent pattern mining. We also introduce you to deep learning, where large data sets are used to train neural networks with effective results. ...
Reading
18 个视频 (总计 173 分钟), 1 个阅读材料, 1 个测验
Video18 个视频
4.1.2 Pregel - Part 17分钟
4.1.3 Pregel - Part 211分钟
4.1.4 Pregel - Part 36分钟
4.1.5 Giraph Introduction6分钟
4.1.6 Giraph Example4分钟
4.1.7 Spark GraphX15分钟
4.2.1 Big Data Machine Learning Introduction13分钟
4.2.2 Mahout: Introduction8分钟
4.2.3 Mahout kmeans5分钟
4.2.4 Mahout: Naïve Bayes9分钟
4.2.5 Mahout: fpm6分钟
4.2.6 Spark Naïve Bayes2分钟
4.2.7 Spark fpm2分钟
4.2.8 Spark ML/MLlib11分钟
4.2.9 Introduction to Deep Learning20分钟
4.2.10 Deep Neural Network Systems17分钟
4.3.1 Closing Remarks1分钟
Reading1 个阅读材料
Module 4 Overview10分钟
Quiz1 个练习
Module 4 Quiz30分钟
4.1
29 个审阅Chevron Right

热门审阅

创建者 UNApr 10th 2018

My understanding of Big Data technologies was really enhanced by this course. I have decided to pursue more of these underlying technologies after this course. Good job

创建者 MSNov 27th 2017

Very good introduction of application concepts of cloud data computing. Thank You!

讲师

Avatar

Reza Farivar

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

Roy H. Campbell

Professor of Computer Science
Department of Computer Science
Graduation Cap

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此 课程 隶属于 University of Illinois at Urbana-Champaign 提供的 100% 在线 Master in Computer Science。如果您被录取参加全部课程,您的课程将计入您的学位学习进程。

关于 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. ...

关于 Cloud Computing 专项课程

The Cloud Computing Specialization takes you on a tour through cloud computing systems. We start in in the middle layer with Cloud Computing Concepts covering core distributed systems concepts used inside clouds, move to the upper layer of Cloud Applications and finally to the lower layer of Cloud Networking. We conclude with a project that allows you to apply the skills you've learned throughout the courses. The first four courses in this Specialization form the lecture component of courses in our online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization....
Cloud Computing

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